The relationship between body roundness index (BRI) and suicidal ideation: Evidence from NHANES 2013-2018

The relationship between body roundness index (BRI) and suicidal ideation: Evidence from NHANES 2013-2018 | 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 The relationship between body roundness index (BRI) and suicidal ideation: Evidence from NHANES 2013-2018 Miao-yu Ye, Di Zhang, Wu Liu, Jia-zhi Gao, Qian-ming Yi, Jun-jie Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5583719/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Apr, 2025 Read the published version in BMC Psychiatry → Version 1 posted 8 You are reading this latest preprint version Abstract Importance: Emerging research suggests a complex interplay between physical health metrics and mental health outcomes, including suicidal ideation. The body roundness index (BRI), a novel measure of body size, may offer insights into this relationship. This study seeks to investigate the intricate connection between BRI and suicidal ideation in a sample that reflects the entire U.S. population-working to validate the potential of BRI as a predictor of suicidal ideation. Methods: Data from the NHANES spanning 2013 to 2018 was used in this cross-sectional study to analyze a sample of 14,058 participants. BRI was calculated based on height and waist circumference measurements. Suicidal ideation was assessed using the Mental Health-Depression Screener Questionnaire ninth question. To investigate potential non-linear relationships between BRI and suicidal ideation, we employed generalized additive models and smooth curve fitting, and used ROC curves to compare the predictive ability of BMI and BRI for suicidal ideation. Subgroup analyses and interaction tests were utilized to study the moderating effects of categorical covariates. Results: Multiple logistic regression analysis revealed a significant positive correlation between increased BRI and heightened suicidal ideation, with a nonlinear pattern persisting even after adjustment for covariates. This nonlinear relationship was reinforced by a sharp increase in the prevalence of suicidal ideation when the BRI exceeded 6.7. Based on ROC curve analysis, BRI demonstrates a slightly stronger predictive ability for suicide ideation compared to BMI. Subgroup analyses confirmed the relative robustness of this association across populations, including age, education and PRI, etc. However, the interaction between the two was influenced by gender, coronary artery disease and angina pectoris. Conclusion: In conclusion, our cross-sectional study reveals a significant positive correlation between increased BRI and heightened suicidal ideation, with a sharp increase in prevalence when BRI exceeds 6.7. It is recommended that appropriate BRI be maintained to minimize suicidal ideation. Body roundness index Suicidal ideation Cross-sectional study Mental health projection Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Suicidal ideation (SI) stands as a critical global public health issue that cannot be overlooked. It encompasses both passive suicidal thoughts and active plans and behaviors leading to self-harm[ 1 ]. In the United States, suicide is one of the top ten causes of death and is the second leading cause for those between the ages of 10 and 34[ 2 ]. According to a recent WHO study, the global annual suicide rate is 11.4 per 100,000 individuals, with approximately 700,000 individuals losing their lives to suicide each year, which highlights the pervasiveness and gravity of the issue[ 3 ]. Despite efforts in the medical, socio-economic, and political spheres to mitigate and prevent suicide, the complexity of the causes of suicidal ideation puts it out of reach[ 4 ]. Therefore, it is imperative to focus on suicidal ideation. The medical field views obesity as a multifaceted, chronic condition that impacts various aspects of health, including metabolism, biomechanics, and psychosocial well-being[ 5 ]. In the U.S., 22 states have adult obesity rates at or above 35%, an increase from 2021[ 6 ]. Body mass index (BMI) is the traditional measure of obesity. Through BMI, multiple studies have found that obesity is associated with suicide risk, depressive symptoms, and mood problems[ 7 – 10 ]. However, BMI has some drawbacks, including the inability to differentiate between areas of fat, muscle or bone mass, and the omission of other health-related factors such as age, gender and race[ 11 ]. This raises a critical question: could individuals with high BMI but minimal fat be inappropriately categorized, thereby compromising the objectivity of clinical outcomes[ 12 ]? In response, the Body Roundness Index (BRI) has been introduced to provide a more holistic view of visceral fat distribution[ 13 ]. BRI utilizes an elliptical model to assess body roundness and estimates visceral and total body fat using eccentricity measures[ 14 ]. This index has outperformed other anthropometric indicators in predicting risks for a range of clinical outcomes, such as cardiometabolic diseases[ 13 , 15 , 16 ], renal diseases[ 17 ], and various types of cancer[ 18 ]. In addition, some studies showed a positive correlation between BRI and depression, and Weight-adjusted-waist index (WWI) with suicide ideation[ 19 , 20 ]. Unlike BMI, the BRI formula integrates the distribution of abdominal fat through the relationship between waist circumference and height to assess the accumulation of visceral fat in terms of the overall body proportion. Utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2018, this study aims to enhance our understanding of how abdominal adiposity affects suicidal ideation, ultimately offering new perspectives on suicide prevention and mental health strategies. Methods Study population All data were obtained from the publicly available database of NHANES, a series of nationally representative cohort surveys designed to monitor the public health of the U.S. population. Data is gathered through in-home interviews, physical exams at mobile centers, and lab tests. The study was approved by the NCHS Ethics Review Board, and all participants provided informed consent. Further details about the NHANES program are available on its official website ( https://www.cdc.gov/nchs/nhanes/ ). For this research, data from three NHANES cycles (2013–2014, 2015–2016 and 2017–2018) were utilized to investigate the relationship between BRI and suicidal ideation among adults. In order to better capture the effect of metabolism on suicidal ideation in early adulthood and aligns with precedent NHANES studies analyzing adult health outcomes [ 21 , 22 ], we chose 20 years of age as the threshold of adulthood. These cycles were selected based on the availability of comprehensive data on both BRI and suicidal ideation. The study sample initially consisted of 29400 participants. To construct the final analysis cohort, we used the following exclusion criteria: 1. Less than 20 years, 2. Missing information on suicidal ideation, height and waist, 3. Missing information on covariates: gender, ethnicity, education level, marital status, BMI, hypertension, diabetes, stroke, asthma, coronary heart disease, angina pectoris, cancer and smoking, including rejection and lack of clarity. Given the large sample size and the varying proportions of missing values for each covariate, we used different strategies to deal with missing data[ 23 , 24 ]. For covariates with missing values less than 5% of the total data, we excluded these records. For covariates with missing values between 5% and 10%, such as PIR, we used plurality estimation. For covariates with missing values > 10%, such as alcohol consumption, we used multiple regression estimation to address missing values. After applying these criteria, the final study sample included 14058 participants. Figure 1 presents a comprehensive flowchart that details the process of selecting samples. BRI definition BRI is an innovative metric for assessing body shape based on an individual’s height (cm) and waist circumference (cm). These measurements are sourced from participants' medical examination records. To ensure precision, trained health technicians perform the measurements at Mobile Examination Centers, with assistance from a recorder. Participants are instructed to remove their shoes and outer clothing before the assessment. Height is recorded as the vertical measurement taken while standing, and the measurement of waist circumference is done at the midpoint between the lower ribs and the top of the iliac crest while the individual stands. The calculation of BRI follows the formula developed by Thomas et al[ 14 ]: BRI = 364.2 − 365.5 × √ (1 − [WC (cm)/2π] ² / [0.5 × H(cm)] ²) This formula integrates the relationship between waist circumference and height into a mathematical model to provide a value strongly associated with body shape. Suicidal ideation definition Suicidal ideation refers to the desire to end a person's life, assessed in the context of the NHANES Mental Health—Depression Screener. Participants are asked: "Over the last two weeks, how often have you been bothered by thoughts that you would be better off dead or of hurting yourself in some way?" Response options include: Not at all = 0, Several days = 1, More than half the days = 2, Nearly every day = 3. A response of 1, 2, or 3 indicates the presence of suicidal ideation, while a response of 0 is categorized as non-suicidal ideation. This approach, validated by prior research, ensures reliable identification of suicidal ideation within the population[ 25 – 27 ]. Covariates Building on previous studies[ 19 , 20 , 28 ], this research investigates the potential effects of various covariates, including income-to-poverty ratio (PIR), BMI, and a range of health conditions such as hypertension, diabetes, stroke, asthma, angina, coronary heart disease and cancer. PIR is assessed across different levels, while BMI is analyzed as a continuous variable. Lifestyle factors are also taken into account, such as smoking status (defined as having smoked more than 100 cigarettes in one's lifetime[ 29 ]) and drinking status (defined as having drunk 12 or more drinks in any one year[ 30 ]). Health conditions, such as hypertension, diabetes, asthma, angina, coronary heart disease and cancer, are defined based on standard diagnostic criteria, including physician diagnosis, clinical measurements, and medication use. Statistical analysis This study used NHANES 2013–2018 data, which applies a complex, stratified, multistage sampling design to ensure the representativeness of the U.S. non-institutionalized population. Sample weights from NHANES were used to adjust for unequal selection probabilities, oversampling, and non-response, in accordance with NHANES analytic guidelines. We developed three statistical models to analyze the association between BRI and suicidal ideation: Model 1: An unadjusted model. Model 2: Taking into account demographic variables such as age and gender, ethnicity, education level, and marital status. Model 3: Extended Model 2 by further adjusting for PIR, hypertension, diabetes, stroke, asthma, coronary heart disease, angina, cancer, smoking status, and drinking status. In addition to these models, BRI was incorporated both as a continuous variable and as a categorical variable divided into four groups based on quartiles. Categorical variables are depicted in percentage form, and continuous variables are detailed with means and standard errors. To investigate potential non-linear relationships between BRI and suicidal ideation, we employed smooth curve fitting and generalized additive models. Subgroup analyses and interaction tests were conducted to explore the moderating effects of categorical covariates, including age, sex, education and PRI, etc. These covariates were included in the fully adjusted Model 3 to account for potential confounding effects. R Statistical Software (version 4.2) and Empower Statistical Software (version 2.2) were used for all analyses, adhering to NHANES-specific recommendations for analyzing complex survey data. Statistical significance was determined by a two-tailed P < 0.05. Results Baseline Characteristics of Participants This analysis included 14,058 participants, of whom 13,566 did not report suicidal ideation, while 492 individuals did. The overall prevalence of suicidal ideation was approximately 3.50%. Table 1 presents the baseline characteristics, which indicate that participants with suicidal ideation differed significantly from those without suicidal ideation across a number of dimensions (p ＜ 0.05) , including socioeconomic status, lifestyle behaviors, and clinical health indicators. These findings highlight the complex interplay of factors that influence suicidal ideation and warrant further exploration to better understand the underlying mechanisms. Table 1. The Distribution of the Variables per Covariate. Covariates Without SI a （ 13566 ） With SI a （ 492 ） Standardized Difference P-value Age ， year 49.572 ± 17.399 50.124 ± 17.227 0.032 (-0.058, 0.122) 0.489 PRI 2.553 ± 1.544 1.955 ± 1.396 0.407 (0.316, 0.497) <0.001 BRI 5.657 ± 2.425 6.190 ± 2.661 0.209(0. 120 , 0. 299 ) <0.001 BMI 29.433 ± 7.017 30.367 ± 7.457 0.129 (0.039, 0.219) 0.004 Gender, n (%) 0.027 (-0.063, 0.117) 0.561 Male 6633 (48.894%) 234 (47.561%) Female 6933 (51.106%) 258 (52.439%) Ethnicity, n (%) 0.187 (0.097, 0.277) 0.002 Mexican American 2008 (14.802%) 74 (15.041%) Other Hispanic 1397 (10.298%) 72 (14.634%) Non-Hispanic black 5119 (37.734%) 196 (39.837%) Non-Hispanic white 2941 (21.679%) 78 (15.854%) Other 2101 (15.487%) 72 (14.634%) Educational level, n (%) 0.284 (0. 194 , 0. 374 ) ＜ 0.01 Less than high school 2713 (19.999%) 157 (31.911%) High school or GED 3105 (22.888%) 109 (22.154%) More than high School 7748 (57.113%) 226 (45.935%) Marital status, n (%) 0.406 (0.3 16 , 0. 496 ) <0.001 Married/Living with partner 8243 (60.762%) 201(40.854%) Living alone 5323 (39.238%) 291 (59.146%) Smoke, n (%) 0.343 (0.253, 0.433) <0.001 Yes 5757 (42.437%) 292 (59.350%) No 7809 (57.563%) 200 (40.650%) Drinking, n (%) 0.103 (0.013, 0.193) 0.006 Yes 13400 (98.776%) 479 (97.358%) No 166 (1.224%) 13 (2.642%) Hypertension, n (%) 0.092 (0.002, 0.182) 0.043 Yes 4963 (36.584%) 202 (41.057%) No 8603 (63.416%) 290 (58.943%) Diabetes, n (%) 0.178 (0.088, 0.268) <0.001 Yes 1860 (13.711%) 97 (19.715%) No 11314 (83.400%) 387 (78.659%) Borderline 392 (2.890%) 8 (1.626%) Asthma, n (%) 0.176 (0.086, 0.266) <0.001 Yes 2030 (14.964%) 107 (21.748%) No 11536 (85.036%) 385 (78.252%) Coronary heart disease, n (%) 0.168 (0.078, 0.258) <0.001 Yes 537 (3.958%) 39 (7.927%) No 13029 (96.042%) 453 (92.073%) Angina pectoris, n (%) 0.164 (0.074, 0.253) <0.001 Yes 316 (2.329%) 27 (5.488%) No 13250 (97.671%) 465 (94.512%) Stroke, n (%) 0.135 (0.045, 0.225) <0.001 Yes 462 (3.406%) 31 (6.301%) No 13104 (96.594%) 461 (93.699%) Cancer, n (%) 0.073 (-0.017, 0.163) 0.098 Yes 1320 (9.730%) 59 (11.992%) No 12246 (90.270%) 433 (88.008%) For continuous variables: p-values were calculated by weighted linear regression modeling. For categorical variables, n (%): p-values were calculated by weighted chi-square tests. Association between BRI and suicidal ideation In Table 2 , the findings from multivariate logistic regression analyses are presented, assessing the association between BRI and suicidal ideation across three models. The results consistently show a significant positive association between BRI and the likelihood of suicidal ideation. A one-unit increase in BRI was associated with a 7.1% higher likelihood of having suicidal ideation when BRI was used as a continuous variable in Model 2 (OR: 1.071, 95% CI: 1.035-1.110, P ＜ 0.01 ). In Model 3, the association remained significant with a 4.8% increase in the likelihood of suicidal ideation (OR: 1.048, 95% CI: 1.008-1.090, P=0.027 ). Regarding the quartile analysis, compared to individuals in the lowest quartile (Q1), those in the fourth quartile (Q4) had a 69.4% higher likelihood of suicidal ideation in Model 1 (OR: 1.694, 95% CI: 1.240 - 2.315, P ＜ 0.01 ). In Model 2, this increased to 149.2% (OR: 2.492, 95% CI: 2.007–3.096, P ＜ 0.01 ), indicating a clear dose-response relationship with increasing BRI quartiles. We also employed a generalized model with smoothed curve fitting to examine the nonlinear relationship between BRI and suicidal ideation ( Fig.2 ). Through threshold effect analysis, we identified a critical threshold for BRI at 6.7, with a distinct turning point ( Table 3 ). Below this value, changes in the prevalence of suicidal ideation were relatively mild, while above this threshold, the prevalence increased sharply. Table 2. Association between BRI and Suicidal ideation Characteristics Model 1 [OR (95% CI)] Model 2 [OR (95% CI)] Model 3 [OR (95% CI)] Continuous BRI 1.069 (1.035, 1.104) 1.071(1.035, 1.110) 1.048(1.008, 1.090) p-value <0.001 <0.001 0.027 Quartile Q1 Reference Reference Reference Q2 1.177 (0.792, 1.749) 0.565 (0.396, 0.805) 1. 294 (0.854, 1.963) Q3 1.377 (0.949, 1.998) 0.481 (0.338, 0.686) 1.476 (0.960, 2.270) Q4 1.694 (1.240, 2.315) 2.492 (2.007, 3.096 ) 1.579 (1.110, 2.247) p for trend <0.001 <0.001 <0.001 Model 1: not adjusted for covariates. Model 2: age, gender, ethnicity, education level, and marital status. Model 3: age, gender, ethnicity, education level, marital status, PIR, hypertension, diabetes, stroke, asthma, coronary heart disease, angina pectoris, cancer, smoking status and drinking. Table 3. Threshold effect analysis results of BRI on Suicidal ideation Threshold effect analysis OR (95% CI), P-value Inflection points of LAP (K) 6.7 K slope 1.0(1.0, 1.1), 0.567 Log-likelihood ratio test 0.096 Predictive value of BRI on the prevalence of suicidal ideation Based on ROC curve analysis, BRI demonstrates a slightly stronger predictive ability for suicide ideation compared to BMI ( Fig.3 ). As shown in Table 4 , the area under the curve (AUC) for BRI is 0.564 (95% CI: 0.590–0.538), whereas BMI’s AUC is 0.539 (95% CI: 0.565–0.513). Additionally, the best threshold for BRI is 5.86, with a sensitivity of 0.520 and specificity of 0.604, while the best threshold for BMI is 28.45, with a sensitivity of 0.569 and specificity of 0.512. Although BMI has a higher sensitivity, BRI offers better specificity, indicating that BRI is more effective in distinguishing individuals without suicidal ideation. Overall, BRI outperforms BMI in predicting suicidal ideation, especially in terms of its ability to correctly classify those without the condition. Table 4. BRI versus BMI for predicting suicidal ideation. Test AUC （95% CI） Best threshold Sensitivity Specificity BMI 0.539(0.513–0.565) 28.45 0.569 0.512 BRI 0.564 (0.538–0.590) 5.86 0.520 0.604 Packet analyses We performed stratified analyses based on age, gender, race, education, PIR, hypertension, diabetes, coronary heart disease, angina pectoris, asthma, stroke, cancer, smoking and drinking. As shown in Fig.4 , the subgroup analysis revealed an inconsistent relationship between BRI and suicidal ideation. Significant associations between BRI and suicidal ideation were found in all subgroups stratified by age, gender, PIR, hypertension, coronary heart disease, angina pectoris, stroke, and smoke (P ＜ 0.05) . However, when stratified by race, education, diabetes, asthma, cancer, and drinking, significant associations were only observed in the following groups: Mexican American, Other Hispanic, Non-Hispanic White, individuals with more than a high school education, non-diabetic individuals with borderline diabetes, those without asthma, those without cancer, and non-drinkers (P ＜ 0.05). Interaction analyses revealed that only gender, coronary heart disease, and angina pectoris were likely to affect the positive association between BRI and suicidal ideation (interaction P ＜0.05) . Specifically, the interaction between BRI and gender showed that the association was stronger in men (interaction P = 0.009) . Similarly, the presence of coronary heart disease and angina pectoris significantly intensified the positive correlation between BRI and suicidal ideation, with individuals suffering from these conditions showing a stronger association between abdominal obesity and suicidal thoughts. The interaction P-value for coronary heart disease was 0.022, and for angina pectoris, it was 0.006. No significant differences in the BRI-SI relationship were observed in the other stratified subgroups (e.g., age, education level, PIR), suggesting that these factors did not significantly influence the positive correlation (interaction P ＞0.05) . Discussion A cross-sectional examination was conducted with a representative sample of 14,058 individuals from the U.S. and explored the association between BRI and suicidal ideation. Research revealed that suicidal ideation affected about 3.5% of the adult population in the U.S. In Model 3, each one-unit increase in BRI was associated with a 4.8% likelihood of suicidal ideation. Further analysis using BRI quartiles showed a clear dose-response relationship. This nonlinear relationship was reinforced by a sharp increase in the prevalence of suicidal ideation when the BRI exceeded 6.7. Notably, this relationship persisted even after adjusting for multiple covariates, including age, race, education level, marital status, PIR, hypertension, diabetes, stroke, asthma, cancer, smoking status and drinking. However, subgroup analyses also showed that the relationship between BRI and suicidal ideation varied significantly by sex, coronary heart disease, and angstroms, as evidenced by an interaction p-value of less than 0.05. Besides, based on ROC curve analysis, BRI demonstrates a slightly stronger predictive ability for suicide ideation compared to BMI. The observed trend remained consistent across the Delong test, reinforcing the potential of BRI as a predictor of suicidal ideation. These findings highlight a robust positive association between BRI and suicidal ideation. However, given the cross-sectional nature of the study, we caution against inferring causality. While the observed association suggests a potential link between higher BRI and increased likelihood of suicidal ideation, the temporal direction of this relationship cannot be definitively established. Currently, numerous diseases have obesity as a prevalent risk factor[ 31 – 33 ]. Throughout the decades, BMI has been the predominant metric for determining whether an individual is overweight or obese, and it has been widely adopted in clinical and public health contexts. A growing body of research has highlighted its association with depression and suicidal ideation[ 7 , 8 , 34 , 35 ]. Compared to BMI, a study confirmed that BRI is a better indicator of fat distribution and that visceral fat content may be more closely associated with mortality risk[ 17 ]. Several studies have surfaced that the BRI, as a comprehensive assessment of physical obesity, is effective in predicting the probability of cardiovascular disease[ 20 ], diabetes mellitus[ 36 , 37 ], metabolic syndrome[ 15 , 38 ], certain cancers[ 39 ], respiratory disease[ 13 ], liver disease[ 40 ], and mental health problems[ 20 ]. Among these, BRI facilitates the measurement of visceral fat, and its resulting depressed mood is a major contributor to suicidal ideation[ 41 , 42 ]. Moreover, abdominal obesity is often associated with diminished self-esteem and poor body image, factors that significantly impact mental health and elevate the likelihood of suicide[ 43 ]. These findings highlight the potential role of BRI as a valuable tool in identifying individuals at increased likelihood of suicidal ideation, similar to other obesity-related measures[ 44 ]. Several interrelated biological mechanisms may explain the association between BRI and suicidal ideation. As a more accurate indicator of abdominal fat distribution than traditional measures like BMI, BRI primarily reflects central adiposity, which has been strongly linked to chronic low-grade inflammation[ 45 ]. This inflammatory state is driven by adipokine activity, particularly interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α), which impair critical neurotransmitter systems involved in mood regulation. Notably, serotonin and dopamine pathways—essential for emotional homeostasis—are disrupted by these inflammatory mediators. Such neurochemical dysregulation may subsequently elevate suicide risk, particularly in individuals with preexisting vulnerabilities to mood disorders[ 46 , 47 ]. Elevated BRI levels are strongly associated with hypothalamic-pituitary-adrenal (HPA) axis dysregulation, particularly through excessive cortisol production. Abdominal obesity serves as both a contributor to and consequence of HPA axis dysfunction, creating a self-perpetuating cycle. Research demonstrates that individuals with abdominal adiposity exhibit significantly higher cortisol levels compared to those with peripheral fat distribution patterns[ 48 – 50 ]. This abdominal fat-HPA axis interaction may be particularly relevant in understanding suicide risk, as chronic cortisol elevation induces neurostructural changes implicated in emotional regulation deficits. The sustained hypercortisolism observed in abdominal obesity leads to distinct brain alterations, including hippocampal volume reduction and prefrontal cortex dysfunction[ 51 , 52 ]. These neuroanatomical changes are clinically significant given their established associations with major depressive disorder and suicidal behavior. For mentally vulnerable populations, the combined effects of HPA axis hyperactivity and cortisol-mediated neural remodeling may create a neurobiological substrate that facilitates suicidal ideation development. Additionally, the fat distribution pattern captured by BRI is linked to metabolic disturbances, including insulin resistance and dyslipidemia, which further impair brain function and emotional regulation[ 53 , 54 ]. BRI also influences neuroendocrine pathways through alterations in hormones such as leptin and adiponectin. These modifications may be involved in the development of mood disorders like depression and anxiety, both of which are well-established precursors to suicidal ideation[ 55 , 56 ]. Furthermore, abdominal obesity, reflected by a high BRI, is often associated with sleep disturbances, such as obstructive sleep apnea, which exacerbate mood dysregulation and cognitive impairments, further increasing the risk of suicidal ideation[ 57 ]. Suicide risk factors in the general population include, but are not limited to, unemployment[ 58 ], mental disorders[ 59 ], homelessness[ 60 ], low socioeconomic status[ 61 ], and acute psychosocial stress. This study showed that gender, coronary heart disease, and angina pectoris affect the relationship between BRI and suicidal ideation. Gender differences may play a significant role in the impact of abdominal obesity on mental health. Men might be more vulnerable to the negative psychological effects of elevated BRI, potentially due to physiological differences, as well as sociocultural factors that contribute to men underestimating mental health issues and seeking help less frequently[ 62 ]. For individuals with coronary heart disease and angina pectoris, the physical and emotional burden of chronic illness can intensify the negative effects of abdominal obesity on mental health. Persistent chest pain significantly reduces quality of life, contributing to increased psychological distress[ 63 ] and may lead individuals to feel like a burden to others, contributing to self-loathing and increasing the likelihood of suicidal thoughts[ 64 ]. Lastly, the effects of abdominal obesity on psychosocial well-being, including body dissatisfaction, low self-esteem, and social stigma, raise psychological distress levels and are tied to a higher odd of developing suicidal ideation[ 65 , 66 ]. Collectively, these mechanisms underscore how BRI, by capturing both physical and psychological vulnerabilities, serves as a critical marker for assessing suicidal ideation. Strength and limitation This study presents several key strengths. First, we are the first to investigate the relationship between BRI and suicidal ideation, employing multiple analytical models—such as weighted multivariable logistic regression and ROC sensitivity analysis to compare BRI with BMI and explore the complexity of BRI’s influence on suicide ideation. Second, the data we use comes from the NHANES database, which offers a sample that is nationally representative of the U.S. population. By applying appropriate sampling weights, our findings offer a reliable representation of the general population. Moreover, we enhanced the robustness of our analysis by incorporating a wide range of confounding variables, thereby reducing potential biases that could distort the results. Despite these strengths, our study has several restrictions. First, due to the cross-sectional design, we can only identify associations, not causal relationships, between BRI and suicidal ideation. Second, this study was unable to fully and clearly distinguish between suicidal ideation (SI) and self-injurious ideation. The NHANES Depression Screening Program, which assesses thoughts of “better off dead” and thoughts of “hurting myself” captured both conceptualizations but was unable to clearly distinguish between them. While this measure is widely used, this simultaneous focus on suicidal and self-injurious ideation limits the precision of the data regarding participants' exact intentions. Furthermore, the low AUC values suggest that the predictive power of BRI for suicidal ideation is relatively weak, and their application in real-world scenarios may not be highly effective. Lastly, while the large sample size strengthens the generalizability of the results, it may also magnify statistically significant but clinically trivial differences, making it important to interpret the findings with caution regarding their practical significance. Conclusions In conclusion, our cross-sectional study reveals a significant positive correlation between increased BRI and heightened suicidal ideation, with a sharp increase in prevalence when BRI exceeds 6.7. It is recommended that appropriate BRI be maintained to minimize suicidal ideation. Abbreviations BRI: Body roundness index BMI: Body mass index SI: Suicidal ideation WWI: Weight-adjusted-waist index NHANES: National Health and Nutrition Examination Survey NCHS: National Center for Health Statistics PIR: Income-to-poverty ratio AUC: Area under the curve ROC: Receiver operating characteristic curve. IL-6: Interleukin-6 TNF-α: Tumor necrosis factor-alpha HPA: Hypothalamic-pituitary-adrenal Declarations Ethics approval and consent to participate The National Center for Health Statistics Ethics Review Board of the U.S. CDC approved the NHANES protocols, and all participants gave their written informed consent for the survey. Consent for publication Not applicable. Funding Support for this research came from the National Natural Science Foundation of China (No. 82105043). Credit authorship contribution statement Data – Data Collection & Analysis: Miao-yu Ye, Di Zhang, Qian-ming Yi Figures & Tables – draft: Miao-yu Ye, Di Zhang Writing – original draft: Miao-yu Ye, Jun-jie Chen Writing – review & editing: Liu Wu, Jian Luo Writing – revise & editing: Miao-yu Ye, Di Zhang, Jia-zhi Gao Declaration of Competing Interest There are no known competing financial interests or personal relationships which may have influenced the authors' work. References Silverman MM, Berman AL.2014. Suicide risk assessment and risk formulation part I: a focus on suicide ideation in assessing suicide risk. Suicide Life Threat Behav 44:420-31. Hedegaard H, Curtin SC, Warner M.2021. Suicide Mortality in the United States, 1999-2019. NCHS Data Brief:1-8. Ropret S, Kouter K, Zupanc T, Videtic Paska A.2021. BDNF methylation and mRNA expression in brain and blood of completed suicides in Slovenia. World J Psychiatry 11:1301-1313. Karaye IM.2022. 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Relationship between four visceral obesity indices and prediabetes and diabetes: a cross-sectional study in Dalian, China. BMC Endocr Disord 24:191. Witarto BS, Witarto AP, Visuddho V, Wungu CDK, Maimunah U, Rejeki PS, Oceandy D.2024. Gender-specific accuracy of lipid accumulation product index for the screening of metabolic syndrome in general adults: a meta-analysis and comparative analysis with other adiposity indicators. Lipids Health Dis 23:198. Chen Y, Wang Y, Zheng X, Liu T, Liu C, Lin S, Xie H, Shi J, Liu X, Ma X, Deng L, Wu S, Shi H.2024. Body Roundness Index Trajectories and the Risk of Cancer: A Cohort Study. Cancer Med 13:e70447. Jiang N, Zhang S, Chu J, Yang N, Lu M.2023. Association between body roundness index and non-alcoholic fatty liver disease detected by Fibroscan in America. J Clin Lab Anal 37:e24973. Lotfi K, Hassanzadeh Keshteli A, Saneei P, Afshar H, Esmaillzadeh A, Adibi P.2022. 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The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat Rev Immunol 16:22-34. Dantzer R, O'Connor JC, Freund GG, Johnson RW, Kelley KW.2008. From inflammation to sickness and depression: when the immune system subjugates the brain. Nat Rev Neurosci 9:46-56. Incollingo Rodriguez AC, Epel ES, White ML, Standen EC, Seckl JR, Tomiyama AJ.2015. Hypothalamic-pituitary-adrenal axis dysregulation and cortisol activity in obesity: A systematic review. Psychoneuroendocrinology 62:301-18. Dong C, Sanchez LE, Price RA.2004. Relationship of obesity to depression: a family-based study. Int J Obes Relat Metab Disord 28:790-5. Pasquali R, Vicennati V, Cacciari M, Pagotto U.2006. The hypothalamic-pituitary-adrenal axis activity in obesity and the metabolic syndrome. Ann N Y Acad Sci 1083:111-28. Pariante CM, Lightman SL.2008. The HPA axis in major depression: classical theories and new developments. Trends Neurosci 31:464-8. Stetler C, Miller GE.2011. Depression and hypothalamic-pituitary-adrenal activation: a quantitative summary of four decades of research. Psychosom Med 73:114-26. Li S, Yang D, Zhou X, Chen L, Liu L, Lin R, Li X, Liu Y, Qiu H, Cao H, Liu J, Cheng Q.2024. Neurological and metabolic related pathophysiologies and treatment of comorbid diabetes with depression. CNS Neurosci Ther 30:e14497. Schwartz MW, Seeley RJ, Zeltser LM, Drewnowski A, Ravussin E, Redman LM, Leibel RL.2017. Obesity Pathogenesis: An Endocrine Society Scientific Statement. Endocr Rev 38:267-296. Friedman JM.2019. Leptin and the endocrine control of energy balance. Nat Metab 1:754-764. Espiau M, Yeste D, Noguera-Julian A, Soler-Palacín P, Fortuny C, Ferrer R, Comas I, Martín-Nalda A, Deyà-Martínez Á, Figueras C, Carrascosa A.2017. Adiponectin, Leptin and Inflammatory Markers in HIV-associated Metabolic Syndrome in Children and Adolescents. Pediatr Infect Dis J 36:e31-e37. Gupta MA, Simpson FC.2015. Obstructive sleep apnea and psychiatric disorders: a systematic review. J Clin Sleep Med 11:165-75. Blakely TA, Collings SC, Atkinson J.2003. Unemployment and suicide. Evidence for a causal association? J Epidemiol Community Health 57:594-600. Mortensen PB, Agerbo E, Erikson T, Qin P, Westergaard-Nielsen N.2000. Psychiatric illness and risk factors for suicide in Denmark. Lancet 355:9-12. Votta E, Manion I.2004. Suicide, high-risk behaviors, and coping style in homeless adolescent males' adjustment. J Adolesc Health 34:237-43. Lorant V, Kunst AE, Huisman M, Costa G, Mackenbach J.2005. Socio-economic inequalities in suicide: a European comparative study. Br J Psychiatry 187:49-54. Shi P, Yang A, Zhao Q, Chen Z, Ren X, Dai Q.2021. A Hypothesis of Gender Differences in Self-Reporting Symptom of Depression: Implications to Solve Under-Diagnosis and Under-Treatment of Depression in Males. Front Psychiatry 12:589687. Balit J, Erlangsen A, Docherty A, Turecki G, Orri M.2024. Association of chronic pain with suicide attempt and death by suicide: a two-sample Mendelian randomization. Mol Psychiatry 29:2043-2049. Teismann T, Brailovskaia J, Robison M, Joiner TE.2023. Self-burdensomeness, other-burdensomeness, and suicidal ideation. Behav Res Ther 169:104388. Puhl RM, Heuer CA.2009. The stigma of obesity: a review and update. Obesity (Silver Spring) 17:941-64. Mazzeo SE, Weinstock M, Vashro TN, Henning T, Derrigo K.2024. Mitigating Harms of Social Media for Adolescent Body Image and Eating Disorders: A Review. Psychol Res Behav Manag 17:2587-2601. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Apr, 2025 Read the published version in BMC Psychiatry → Version 1 posted Editorial decision: Accepted 08 Apr, 2025 Reviews received at journal 07 Apr, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviews received at journal 25 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers invited by journal 24 Mar, 2025 Submission checks completed at journal 24 Mar, 2025 First submitted to journal 20 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5583719","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433767040,"identity":"f595980f-771f-406f-a8ba-a756566945b8","order_by":0,"name":"Miao-yu Ye","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese medicine","correspondingAuthor":false,"prefix":"","firstName":"Miao-yu","middleName":"","lastName":"Ye","suffix":""},{"id":433767041,"identity":"ac17813e-9152-4166-9eae-c9afb5f4f870","order_by":1,"name":"Di Zhang","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese medicine","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Zhang","suffix":""},{"id":433767042,"identity":"894632f3-5a16-45d9-9699-6f0046d398c5","order_by":2,"name":"Wu Liu","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese medicine","correspondingAuthor":false,"prefix":"","firstName":"Wu","middleName":"","lastName":"Liu","suffix":""},{"id":433767043,"identity":"41c65a5e-35a4-4c0a-baeb-3a8afe656e7f","order_by":3,"name":"Jia-zhi Gao","email":"","orcid":"","institution":"Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Jia-zhi","middleName":"","lastName":"Gao","suffix":""},{"id":433767044,"identity":"31091c77-b0fd-4546-b834-2cd8317d78f7","order_by":4,"name":"Qian-ming Yi","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese medicine","correspondingAuthor":false,"prefix":"","firstName":"Qian-ming","middleName":"","lastName":"Yi","suffix":""},{"id":433767045,"identity":"cb881be5-e6a8-49e9-a97b-85644da9df1e","order_by":5,"name":"Jun-jie Chen","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese medicine","correspondingAuthor":false,"prefix":"","firstName":"Jun-jie","middleName":"","lastName":"Chen","suffix":""},{"id":433767046,"identity":"e3ce0a5d-43b9-4bc2-bf88-41d130a7f12e","order_by":6,"name":"Jian Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIie3PMQrCQBBA0VkCsZmQNoF0XiD2wfUoCYKVB0i5EknlARTvIZazLMRmjW0gl0iwtdDGftIJ7qummM8wAI7zg1IwZF5lhuFMsZOmILSbJD4QO8E5BXuTpd2KmwgLFKsHQgdiGLesK3eixbVHcVZefLqwkjanwvboJeR7AS+xKem6RT/KJyR6VxPihKTJDdg1Rqgr3i/yaMwTyqWUt0oPIyeBiL6TUJz9j5C76DiO87/eqAY8N4Jf5FgAAAAASUVORK5CYII=","orcid":"","institution":"Hospital of Chengdu of Traditional Chinese medicine","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-12-05 04:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5583719/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5583719/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12888-025-06834-z","type":"published","date":"2025-04-17T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79255983,"identity":"31f3c8b2-3885-48db-8305-1aba71b1da73","added_by":"auto","created_at":"2025-03-26 08:55:39","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77287,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant Selection Flowchart.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5583719/v1/3d99e2a1ae56b5aeb6bd6fc7.jpg"},{"id":79255982,"identity":"f47a543f-1e4b-4fd1-9d36-abc1164d8b72","added_by":"auto","created_at":"2025-03-26 08:55:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26838,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between BRI and suicidal ideation.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5583719/v1/ed67dd827544cfd748035704.jpg"},{"id":79257119,"identity":"3f345f88-625d-49c2-af9c-3293ee9ddd49","added_by":"auto","created_at":"2025-03-26 09:03:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34868,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve the predictive value of BRI and BMI for suicidal ideation.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5583719/v1/f90b28eeb975b5a6df34da19.jpg"},{"id":79258676,"identity":"48381111-4bdc-4c55-8cee-180b4c92b0f3","added_by":"auto","created_at":"2025-03-26 09:11:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96373,"visible":true,"origin":"","legend":"\u003cp\u003ePacket analysis of the association between BRI and suicidal ideation.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5583719/v1/8a88aaa8b6763ef014386290.jpg"},{"id":81050879,"identity":"3ad0b6c7-1db0-42a2-a2f8-b25a2928ceee","added_by":"auto","created_at":"2025-04-21 16:06:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1350209,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5583719/v1/5ebdb1cc-fa80-4d8a-ac83-cf764c7e2630.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The relationship between body roundness index (BRI) and suicidal ideation: Evidence from NHANES 2013-2018","fulltext":[{"header":"Background","content":"\u003cp\u003eSuicidal ideation (SI) stands as a critical global public health issue that cannot be overlooked. It encompasses both passive suicidal thoughts and active plans and behaviors leading to self-harm[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the United States, suicide is one of the top ten causes of death and is the second leading cause for those between the ages of 10 and 34[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to a recent WHO study, the global annual suicide rate is 11.4 per 100,000 individuals, with approximately 700,000 individuals losing their lives to suicide each year, which highlights the pervasiveness and gravity of the issue[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite efforts in the medical, socio-economic, and political spheres to mitigate and prevent suicide, the complexity of the causes of suicidal ideation puts it out of reach[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, it is imperative to focus on suicidal ideation.\u003c/p\u003e \u003cp\u003eThe medical field views obesity as a multifaceted, chronic condition that impacts various aspects of health, including metabolism, biomechanics, and psychosocial well-being[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In the U.S., 22 states have adult obesity rates at or above 35%, an increase from 2021[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Body mass index (BMI) is the traditional measure of obesity. Through BMI, multiple studies have found that obesity is associated with suicide risk, depressive symptoms, and mood problems[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, BMI has some drawbacks, including the inability to differentiate between areas of fat, muscle or bone mass, and the omission of other health-related factors such as age, gender and race[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This raises a critical question: could individuals with high BMI but minimal fat be inappropriately categorized, thereby compromising the objectivity of clinical outcomes[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]?\u003c/p\u003e \u003cp\u003eIn response, the Body Roundness Index (BRI) has been introduced to provide a more holistic view of visceral fat distribution[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. BRI utilizes an elliptical model to assess body roundness and estimates visceral and total body fat using eccentricity measures[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This index has outperformed other anthropometric indicators in predicting risks for a range of clinical outcomes, such as cardiometabolic diseases[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], renal diseases[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and various types of cancer[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, some studies showed a positive correlation between BRI and depression, and Weight-adjusted-waist index (WWI) with suicide ideation[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Unlike BMI, the BRI formula integrates the distribution of abdominal fat through the relationship between waist circumference and height to assess the accumulation of visceral fat in terms of the overall body proportion. Utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2018, this study aims to enhance our understanding of how abdominal adiposity affects suicidal ideation, ultimately offering new perspectives on suicide prevention and mental health strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eAll data were obtained from the publicly available database of NHANES, a series of nationally representative cohort surveys designed to monitor the public health of the U.S. population. Data is gathered through in-home interviews, physical exams at mobile centers, and lab tests. The study was approved by the NCHS Ethics Review Board, and all participants provided informed consent. Further details about the NHANES program are available on its official website \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eFor this research, data from three NHANES cycles (2013\u0026ndash;2014, 2015\u0026ndash;2016 and 2017\u0026ndash;2018) were utilized to investigate the relationship between BRI and suicidal ideation among adults. In order to better capture the effect of metabolism on suicidal ideation in early adulthood and aligns with precedent NHANES studies analyzing adult health outcomes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], we chose 20 years of age as the threshold of adulthood. These cycles were selected based on the availability of comprehensive data on both BRI and suicidal ideation. The study sample initially consisted of 29400 participants. To construct the final analysis cohort, we used the following exclusion criteria: 1. Less than 20 years, 2. Missing information on suicidal ideation, height and waist, 3. Missing information on covariates: gender, ethnicity, education level, marital status, BMI, hypertension, diabetes, stroke, asthma, coronary heart disease, angina pectoris, cancer and smoking, including rejection and lack of clarity. Given the large sample size and the varying proportions of missing values for each covariate, we used different strategies to deal with missing data[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For covariates with missing values less than 5% of the total data, we excluded these records. For covariates with missing values between 5% and 10%, such as PIR, we used plurality estimation. For covariates with missing values\u0026thinsp;\u0026gt;\u0026thinsp;10%, such as alcohol consumption, we used multiple regression estimation to address missing values.\u003c/p\u003e \u003cp\u003eAfter applying these criteria, the final study sample included 14058 participants. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a comprehensive flowchart that details the process of selecting samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBRI definition\u003c/h3\u003e\n\u003cp\u003eBRI is an innovative metric for assessing body shape based on an individual\u0026rsquo;s height (cm) and waist circumference (cm). These measurements are sourced from participants' medical examination records. To ensure precision, trained health technicians perform the measurements at Mobile Examination Centers, with assistance from a recorder. Participants are instructed to remove their shoes and outer clothing before the assessment. Height is recorded as the vertical measurement taken while standing, and the measurement of waist circumference is done at the midpoint between the lower ribs and the top of the iliac crest while the individual stands.\u003c/p\u003e \u003cp\u003eThe calculation of BRI follows the formula developed by Thomas et al[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cb\u003eBRI\u0026thinsp;=\u0026thinsp;364.2\u0026thinsp;\u0026minus;\u0026thinsp;365.5 \u0026times; \u0026radic; (1 \u0026minus; [WC (cm)/2π] \u0026sup2; / [0.5 \u0026times; H(cm)] \u0026sup2;)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis formula integrates the relationship between waist circumference and height into a mathematical model to provide a value strongly associated with body shape.\u003c/p\u003e\n\u003ch3\u003eSuicidal ideation definition\u003c/h3\u003e\n\u003cp\u003eSuicidal ideation refers to the desire to end a person's life, assessed in the context of the NHANES Mental Health\u0026mdash;Depression Screener. Participants are asked: \u003cem\u003e\"Over the last two weeks, how often have you been bothered by thoughts that you would be better off dead or of hurting yourself in some way?\"\u003c/em\u003e Response options include: Not at all =\u0026thinsp;0, Several days\u0026thinsp;=\u0026thinsp;1, More than half the days\u0026thinsp;=\u0026thinsp;2, Nearly every day\u0026thinsp;=\u0026thinsp;3. A response of 1, 2, or 3 indicates the presence of suicidal ideation, while a response of 0 is categorized as non-suicidal ideation. This approach, validated by prior research, ensures reliable identification of suicidal ideation within the population[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eBuilding on previous studies[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], this research investigates the potential effects of various covariates, including income-to-poverty ratio (PIR), BMI, and a range of health conditions such as hypertension, diabetes, stroke, asthma, angina, coronary heart disease and cancer. PIR is assessed across different levels, while BMI is analyzed as a continuous variable. Lifestyle factors are also taken into account, such as smoking status (defined as having smoked more than 100 cigarettes in one's lifetime[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]) and drinking status (defined as having drunk 12 or more drinks in any one year[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]). Health conditions, such as hypertension, diabetes, asthma, angina, coronary heart disease and cancer, are defined based on standard diagnostic criteria, including physician diagnosis, clinical measurements, and medication use.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThis study used NHANES 2013\u0026ndash;2018 data, which applies a complex, stratified, multistage sampling design to ensure the representativeness of the U.S. non-institutionalized population. Sample weights from NHANES were used to adjust for unequal selection probabilities, oversampling, and non-response, in accordance with NHANES analytic guidelines. We developed three statistical models to analyze the association between BRI and suicidal ideation:\u003c/p\u003e \u003cp\u003eModel 1: An unadjusted model.\u003c/p\u003e \u003cp\u003eModel 2: Taking into account demographic variables such as age and gender, ethnicity, education level, and marital status.\u003c/p\u003e \u003cp\u003eModel 3: Extended Model 2 by further adjusting for PIR, hypertension, diabetes, stroke, asthma, coronary heart disease, angina, cancer, smoking status, and drinking status.\u003c/p\u003e \u003cp\u003eIn addition to these models, BRI was incorporated both as a continuous variable and as a categorical variable divided into four groups based on quartiles. Categorical variables are depicted in percentage form, and continuous variables are detailed with means and standard errors. To investigate potential non-linear relationships between BRI and suicidal ideation, we employed smooth curve fitting and generalized additive models. Subgroup analyses and interaction tests were conducted to explore the moderating effects of categorical covariates, including age, sex, education and PRI, etc. These covariates were included in the fully adjusted Model 3 to account for potential confounding effects.\u003c/p\u003e \u003cp\u003eR Statistical Software (version 4.2) and Empower Statistical Software (version 2.2) were used for all analyses, adhering to NHANES-specific recommendations for analyzing complex survey data. Statistical significance was determined by a two-tailed \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Characteristics of Participants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis analysis included 14,058 participants, of whom 13,566 did not report suicidal ideation, while 492 individuals did. The overall prevalence of suicidal ideation was approximately 3.50%. \u003cu\u003eTable 1\u003c/u\u003e presents the baseline characteristics, which indicate that participants with suicidal ideation differed significantly from those without suicidal ideation across a number of dimensions \u003cem\u003e(p\u003c/em\u003e\u003cem\u003e＜\u003c/em\u003e\u003cem\u003e0.05)\u003c/em\u003e, including socioeconomic status, lifestyle behaviors, and clinical health indicators. These findings highlight the complex interplay of factors that influence suicidal ideation and warrant further exploration to better understand the underlying mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. The Distribution of the Variables \u003cem\u003eper\u003c/em\u003e Covariate.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\" style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\n \u003ctable style=\"border: none;border-collapse: collapse;width: 763px;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:166.0pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:#8EAADB;padding:0in 5.4pt 0in 5.4pt;height:30.6pt;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;text-indent:55.2pt;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003eCovariates\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:100.0pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:#8EAADB;padding:0in 5.4pt 0in 5.4pt;height:30.6pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:11px;font-family:\"Cambria\",serif;color:#1B1B1B;background:white;'\u003eWithout SI\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u003cspan style='font-size:16px;font-family:\"Cambria\",serif;color:#1B1B1B;background:white;'\u003ea\u003c/span\u003e\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:12px;font-family:SimSun;color:#1B1B1B;background:white;\"\u003e（\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan style='font-size:12px;font-family:\"Cambria\",serif;color:#1B1B1B;background:white;'\u003e13566\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan style=\"font-size:12px;font-family:SimSun;color:#1B1B1B;background:white;\"\u003e）\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:96.5pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:#8EAADB;padding:0in 5.4pt 0in 5.4pt;height:30.6pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:11px;font-family:\"Cambria\",serif;color:#1B1B1B;background:white;'\u003eWith SI\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u003cspan style='font-size:16px;font-family:\"Cambria\",serif;color:#1B1B1B;background:white;'\u003ea\u003c/span\u003e\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:12px;font-family:SimSun;color:#1B1B1B;background:white;\"\u003e（\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan style='font-size:12px;font-family:\"Cambria\",serif;color:#1B1B1B;background:white;'\u003e492\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan style=\"font-size:12px;font-family:SimSun;color:#1B1B1B;background:white;\"\u003e）\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:#8EAADB;padding:0in 5.4pt 0in 5.4pt;height:30.6pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New 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style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026lt;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:166.0pt;background:#F7CAAC;padding:0in 5.4pt 0in 5.4pt;height:14.6pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003eBRI\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:100.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.6pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e5.657\u003c/span\u003e\u003cspan style=\"font-size:15px;font-family:SimSun;color:black;\"\u003e\u0026plusmn;\u003c/span\u003e\u003cspan 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Roman\",serif;color:black;'\u003eOther\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:100.0pt;padding:0in 5.4pt 0in 5.4pt;height:17.0pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e2101 (15.487%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:96.5pt;padding:0in 5.4pt 0in 5.4pt;height:17.0pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e72 (14.634%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:17.0pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:17.0pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:166.0pt;background:#F7CAAC;padding:0in 5.4pt 0in 5.4pt;height:20.0pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003eEducational level,\u0026nbsp;n\u0026nbsp;(%)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:100.0pt;padding:0in 5.4pt 0in 5.4pt;height:20.0pt;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:96.5pt;padding:0in 5.4pt 0in 5.4pt;height:20.0pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:20.0pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e0.284\u003c/span\u003e\u003cspan style='font-size:15px;font-family: \"Times New Roman\",serif;'\u003e\u0026nbsp;(0.\u003c/span\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e194\u003c/span\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e, 0.\u003c/span\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e374\u003c/span\u003e\u003cspan style='font-size:15px;font-family: \"Times New Roman\",serif;'\u003e)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:20.0pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"font-size:15px;font-family:SimSun;\"\u003e＜\u003c/span\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e0.01\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:166.0pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003eLess than high school\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:100.0pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e2713 (19.999%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:96.5pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e157 (31.911%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:166.0pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;High school or GED\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:100.0pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e3105 (22.888%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:96.5pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e109 (22.154%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:166.0pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003eMore than high School\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:100.0pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e7748 (57.113%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:96.5pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e226 (45.935%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:166.0pt;background:#F7CAAC;padding:0in 5.4pt 0in 5.4pt;height:4.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003eMarital status,\u0026nbsp;n\u0026nbsp;(%)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:100.0pt;padding:0in 5.4pt 0in 5.4pt;height:4.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:96.5pt;padding:0in 5.4pt 0in 5.4pt;height:4.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:4.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e0.406\u003c/span\u003e\u003cspan style='font-size:15px;font-family: \"Times New Roman\",serif;'\u003e\u0026nbsp;(0.3\u003c/span\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e16\u003c/span\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e, 0.\u003c/span\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e496\u003c/span\u003e\u003cspan style='font-size:15px;font-family: \"Times New Roman\",serif;'\u003e)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:4.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026lt;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:166.0pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003eMarried/Living with partner\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:100.0pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e8243 (60.762%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:96.5pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e201(40.854%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:166.0pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;Living alone\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:100.0pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e5323 (39.238%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:96.5pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp 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style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e11536 (85.036%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:96.5pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e385 (78.252%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp 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style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:15.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e0.073 (-0.017, 0.163)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:15.1pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e0.098\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:166.0pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:100.0pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e1320 (9.730%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:96.5pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e59 (11.992%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:166.0pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003eNo\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:100.0pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;12246 (90.270%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:96.5pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e433 (88.008%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:105.3pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:13.85pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\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\u003eFor continuous variables: p-values were calculated by weighted linear regression modeling.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor categorical variables, n (%): p-values were calculated by weighted chi-square tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between BRI and suicidal ideation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn \u003cu\u003eTable 2\u003c/u\u003e, the findings from multivariate logistic regression analyses are presented, assessing the association between BRI and suicidal ideation across three models. The results consistently show a significant positive association between BRI and the likelihood of suicidal ideation.\u003c/p\u003e\n\u003cp\u003eA one-unit increase in BRI was associated with a 7.1% higher likelihood of having suicidal ideation when BRI was used as a continuous variable in Model 2 (OR: 1.071, 95% CI: 1.035-1.110,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u003cem\u003e＜\u003c/em\u003e\u003cem\u003e0.01\u003c/em\u003e). In Model 3, the association remained significant with a 4.8% increase in the likelihood of suicidal ideation (OR: 1.048, 95% CI: 1.008-1.090,\u0026nbsp;\u003cem\u003eP=0.027\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eRegarding the quartile analysis, compared to individuals in the lowest quartile (Q1), those in the fourth quartile (Q4) had a 69.4% higher likelihood of suicidal ideation in Model 1 (OR: 1.694, 95% CI: 1.240 - 2.315,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u003cem\u003e＜\u003c/em\u003e\u003cem\u003e0.01\u003c/em\u003e). In Model 2, this increased to 149.2% (OR: 2.492, 95% CI: 2.007\u0026ndash;3.096,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u003cem\u003e＜\u003c/em\u003e\u003cem\u003e0.01\u003c/em\u003e), indicating a clear dose-response relationship with increasing BRI quartiles.\u003c/p\u003e\n\u003cp\u003eWe also employed a generalized model with smoothed curve fitting to examine the nonlinear relationship between BRI and suicidal ideation (\u003cstrong\u003eFig.2\u003c/strong\u003e). Through threshold effect analysis, we identified a critical threshold for BRI at 6.7, with a distinct turning point (\u003cu\u003eTable 3\u003c/u\u003e). Below this value, changes in the prevalence of suicidal ideation were relatively mild, while above this threshold, the prevalence increased sharply.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"726\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Association between BRI and Suicidal ideation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.1405%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1 [OR (95% CI)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2 [OR (95% CI)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3 [OR (95% CI)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.1405%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContinuous BRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e1.069 (1.035, 1.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e1.071(1.035, 1.110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e1.048(1.008, 1.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.1405%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.1405%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.1405%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.1405%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e1.177 (0.792, 1.749)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e0.565 (0.396, 0.805)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e1. 294 (0.854, 1.963)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.1405%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e1.377 (0.949, 1.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e0.481 (0.338, 0.686)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e1.476 (0.960, 2.270)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.1405%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e1.694 (1.240, 2.315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e\u0026nbsp;2.492 (2.007, 3.096 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e1.579 (1.110, 2.247)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.1405%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1:\u0026nbsp;\u003c/strong\u003enot adjusted for covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2:\u0026nbsp;\u003c/strong\u003eage, gender, ethnicity, education level, and marital status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 3:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eage, gender, ethnicity, education level, marital status, PIR, hypertension, diabetes, stroke, asthma, coronary heart disease, angina pectoris, cancer, smoking status and drinking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThreshold effect\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;analysis results of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBRI on Suicidal ideation\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"441\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.1818%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThreshold effect analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.8182%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI), P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.1818%;\"\u003e\n \u003cp\u003eInflection points of LAP (K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51.8182%;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.1818%;\"\u003e\n \u003cp\u003e\u0026lt; K slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.8182%;\"\u003e\n \u003cp\u003e1.1(1.0, 1.2), 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.1818%;\"\u003e\n \u003cp\u003e\u0026gt; K slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.8182%;\"\u003e\n \u003cp\u003e1.0(1.0, 1.1), 0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48.1818%;\"\u003e\n \u003cp\u003eLog-likelihood ratio test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.8182%;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive value of BRI on the prevalence of suicidal ideation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on ROC curve analysis, BRI demonstrates a slightly stronger predictive ability for suicide ideation compared to BMI (\u003cstrong\u003eFig.3\u003c/strong\u003e). As shown in \u003cu\u003eTable 4\u003c/u\u003e, the area under the curve (AUC) for BRI is 0.564 (95% CI: 0.590\u0026ndash;0.538), whereas BMI\u0026rsquo;s AUC is 0.539 (95% CI: 0.565\u0026ndash;0.513). Additionally, the best threshold for BRI is 5.86, with a sensitivity of 0.520 and specificity of 0.604, while the best threshold for BMI is 28.45, with a sensitivity of 0.569 and specificity of 0.512. Although BMI has a higher sensitivity, BRI offers better specificity, indicating that BRI is more effective in distinguishing individuals without suicidal ideation. Overall, BRI outperforms BMI in predicting suicidal ideation, especially in terms of its ability to correctly classify those without the condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. BRI versus BMI for predicting suicidal ideation.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.4601%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.885%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e （95% CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.885%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBest threshold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.885%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.885%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.4601%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.885%;\"\u003e\n \u003cp\u003e0.539(0.513\u0026ndash;0.565)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.885%;\"\u003e\n \u003cp\u003e28.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.885%;\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.885%;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.4601%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.885%;\"\u003e\n \u003cp\u003e0.564 (0.538\u0026ndash;0.590)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.885%;\"\u003e\n \u003cp\u003e5.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.885%;\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.885%;\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePacket analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed stratified analyses based on age, gender, race, education, PIR, hypertension, diabetes, coronary heart disease, angina pectoris, asthma, stroke, cancer, smoking and drinking.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eFig.4\u003c/strong\u003e, the subgroup analysis revealed an inconsistent relationship between BRI and suicidal ideation. Significant associations between BRI and suicidal ideation were found in all subgroups stratified by age, gender, PIR, hypertension, coronary heart disease, angina pectoris, stroke, and smoke\u003cem\u003e\u0026nbsp;(P\u003c/em\u003e\u003cem\u003e＜\u003c/em\u003e\u003cem\u003e0.05)\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, when stratified by race, education, diabetes, asthma, cancer, and drinking, significant associations were only observed in the following groups: Mexican American, Other Hispanic, Non-Hispanic White, individuals with more than a high school education, non-diabetic individuals with borderline diabetes, those without asthma, those without cancer, and non-drinkers\u003cem\u003e\u0026nbsp;(P\u003c/em\u003e\u003cem\u003e＜\u003c/em\u003e\u003cem\u003e0.05).\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInteraction analyses revealed that only gender, coronary heart disease, and angina pectoris were likely to affect the positive association between BRI and suicidal ideation \u003cem\u003e(interaction P\u003c/em\u003e\u003cem\u003e＜0.05)\u003c/em\u003e. Specifically, the interaction between BRI and gender showed that the association was stronger in men\u003cem\u003e\u0026nbsp;(interaction P = 0.009)\u003c/em\u003e. Similarly, the presence of coronary heart disease and angina pectoris significantly intensified the positive correlation between BRI and suicidal ideation, with individuals suffering from these conditions showing a stronger association between abdominal obesity and suicidal thoughts. The interaction P-value for coronary heart disease was 0.022, and for angina pectoris, it was 0.006. No significant differences in the BRI-SI relationship were observed in the other stratified subgroups (e.g., age, education level, PIR), suggesting that these factors did not significantly influence the positive correlation \u003cem\u003e(interaction P\u003c/em\u003e\u003cem\u003e＞0.05)\u003c/em\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eA cross-sectional examination was conducted with a representative sample of 14,058 individuals from the U.S. and explored the association between BRI and suicidal ideation. Research revealed that suicidal ideation affected about 3.5% of the adult population in the U.S. In Model 3, each one-unit increase in BRI was associated with a 4.8% likelihood of suicidal ideation. Further analysis using BRI quartiles showed a clear dose-response relationship. This nonlinear relationship was reinforced by a sharp increase in the prevalence of suicidal ideation when the BRI exceeded 6.7. Notably, this relationship persisted even after adjusting for multiple covariates, including age, race, education level, marital status, PIR, hypertension, diabetes, stroke, asthma, cancer, smoking status and drinking. However, subgroup analyses also showed that the relationship between BRI and suicidal ideation varied significantly by sex, coronary heart disease, and angstroms, as evidenced by an interaction p-value of less than 0.05. Besides, based on ROC curve analysis, BRI demonstrates a slightly stronger predictive ability for suicide ideation compared to BMI. The observed trend remained consistent across the Delong test, reinforcing the potential of BRI as a predictor of suicidal ideation.\u003c/p\u003e \u003cp\u003eThese findings highlight a robust positive association between BRI and suicidal ideation. However, given the cross-sectional nature of the study, we caution against inferring causality. While the observed association suggests a potential link between higher BRI and increased likelihood of suicidal ideation, the temporal direction of this relationship cannot be definitively established.\u003c/p\u003e \u003cp\u003eCurrently, numerous diseases have obesity as a prevalent risk factor[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Throughout the decades, BMI has been the predominant metric for determining whether an individual is overweight or obese, and it has been widely adopted in clinical and public health contexts. A growing body of research has highlighted its association with depression and suicidal ideation[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Compared to BMI, a study confirmed that BRI is a better indicator of fat distribution and that visceral fat content may be more closely associated with mortality risk[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have surfaced that the BRI, as a comprehensive assessment of physical obesity, is effective in predicting the probability of cardiovascular disease[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], diabetes mellitus[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], metabolic syndrome[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], certain cancers[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], respiratory disease[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], liver disease[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and mental health problems[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Among these, BRI facilitates the measurement of visceral fat, and its resulting depressed mood is a major contributor to suicidal ideation[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Moreover, abdominal obesity is often associated with diminished self-esteem and poor body image, factors that significantly impact mental health and elevate the likelihood of suicide[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These findings highlight the potential role of BRI as a valuable tool in identifying individuals at increased likelihood of suicidal ideation, similar to other obesity-related measures[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral interrelated biological mechanisms may explain the association between BRI and suicidal ideation. As a more accurate indicator of abdominal fat distribution than traditional measures like BMI, BRI primarily reflects central adiposity, which has been strongly linked to chronic low-grade inflammation[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This inflammatory state is driven by adipokine activity, particularly interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α), which impair critical neurotransmitter systems involved in mood regulation. Notably, serotonin and dopamine pathways\u0026mdash;essential for emotional homeostasis\u0026mdash;are disrupted by these inflammatory mediators. Such neurochemical dysregulation may subsequently elevate suicide risk, particularly in individuals with preexisting vulnerabilities to mood disorders[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eElevated BRI levels are strongly associated with hypothalamic-pituitary-adrenal (HPA) axis dysregulation, particularly through excessive cortisol production. Abdominal obesity serves as both a contributor to and consequence of HPA axis dysfunction, creating a self-perpetuating cycle. Research demonstrates that individuals with abdominal adiposity exhibit significantly higher cortisol levels compared to those with peripheral fat distribution patterns[\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This abdominal fat-HPA axis interaction may be particularly relevant in understanding suicide risk, as chronic cortisol elevation induces neurostructural changes implicated in emotional regulation deficits. The sustained hypercortisolism observed in abdominal obesity leads to distinct brain alterations, including hippocampal volume reduction and prefrontal cortex dysfunction[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. These neuroanatomical changes are clinically significant given their established associations with major depressive disorder and suicidal behavior. For mentally vulnerable populations, the combined effects of HPA axis hyperactivity and cortisol-mediated neural remodeling may create a neurobiological substrate that facilitates suicidal ideation development.\u003c/p\u003e \u003cp\u003eAdditionally, the fat distribution pattern captured by BRI is linked to metabolic disturbances, including insulin resistance and dyslipidemia, which further impair brain function and emotional regulation[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. BRI also influences neuroendocrine pathways through alterations in hormones such as leptin and adiponectin. These modifications may be involved in the development of mood disorders like depression and anxiety, both of which are well-established precursors to suicidal ideation[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Furthermore, abdominal obesity, reflected by a high BRI, is often associated with sleep disturbances, such as obstructive sleep apnea, which exacerbate mood dysregulation and cognitive impairments, further increasing the risk of suicidal ideation[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSuicide risk factors in the general population include, but are not limited to, unemployment[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], mental disorders[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], homelessness[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], low socioeconomic status[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], and acute psychosocial stress. This study showed that gender, coronary heart disease, and angina pectoris affect the relationship between BRI and suicidal ideation. Gender differences may play a significant role in the impact of abdominal obesity on mental health. Men might be more vulnerable to the negative psychological effects of elevated BRI, potentially due to physiological differences, as well as sociocultural factors that contribute to men underestimating mental health issues and seeking help less frequently[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. For individuals with coronary heart disease and angina pectoris, the physical and emotional burden of chronic illness can intensify the negative effects of abdominal obesity on mental health. Persistent chest pain significantly reduces quality of life, contributing to increased psychological distress[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] and may lead individuals to feel like a burden to others, contributing to self-loathing and increasing the likelihood of suicidal thoughts[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLastly, the effects of abdominal obesity on psychosocial well-being, including body dissatisfaction, low self-esteem, and social stigma, raise psychological distress levels and are tied to a higher odd of developing suicidal ideation[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Collectively, these mechanisms underscore how BRI, by capturing both physical and psychological vulnerabilities, serves as a critical marker for assessing suicidal ideation.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrength and limitation\u003c/h2\u003e \u003cp\u003eThis study presents several key strengths. First, we are the first to investigate the relationship between BRI and suicidal ideation, employing multiple analytical models\u0026mdash;such as weighted multivariable logistic regression and ROC sensitivity analysis to compare BRI with BMI and explore the complexity of BRI\u0026rsquo;s influence on suicide ideation. Second, the data we use comes from the NHANES database, which offers a sample that is nationally representative of the U.S. population. By applying appropriate sampling weights, our findings offer a reliable representation of the general population. Moreover, we enhanced the robustness of our analysis by incorporating a wide range of confounding variables, thereby reducing potential biases that could distort the results.\u003c/p\u003e \u003cp\u003eDespite these strengths, our study has several restrictions. First, due to the cross-sectional design, we can only identify associations, not causal relationships, between BRI and suicidal ideation. Second, this study was unable to fully and clearly distinguish between suicidal ideation (SI) and self-injurious ideation. The NHANES Depression Screening Program, which assesses thoughts of \u0026ldquo;better off dead\u0026rdquo; and thoughts of \u0026ldquo;hurting myself\u0026rdquo; captured both conceptualizations but was unable to clearly distinguish between them. While this measure is widely used, this simultaneous focus on suicidal and self-injurious ideation limits the precision of the data regarding participants' exact intentions. Furthermore, the low AUC values suggest that the predictive power of BRI for suicidal ideation is relatively weak, and their application in real-world scenarios may not be highly effective. Lastly, while the large sample size strengthens the generalizability of the results, it may also magnify statistically significant but clinically trivial differences, making it important to interpret the findings with caution regarding their practical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our cross-sectional study reveals a significant positive correlation between increased BRI and heightened suicidal ideation, with a sharp increase in prevalence when BRI exceeds 6.7. It is recommended that appropriate BRI be maintained to minimize suicidal ideation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBRI: Body roundness index\u003c/p\u003e\n\u003cp\u003eBMI: Body mass index\u003c/p\u003e\n\u003cp\u003eSI: Suicidal ideation\u003c/p\u003e\n\u003cp\u003eWWI: Weight-adjusted-waist index\u003c/p\u003e\n\u003cp\u003eNHANES: National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003eNCHS: National Center for Health Statistics\u003c/p\u003e\n\u003cp\u003ePIR: Income-to-poverty ratio\u003c/p\u003e\n\u003cp\u003eAUC: Area under the curve\u003c/p\u003e\n\u003cp\u003eROC: Receiver operating characteristic curve.\u003c/p\u003e\n\u003cp\u003eIL-6: Interleukin-6\u003c/p\u003e\n\u003cp\u003eTNF-\u0026alpha;: Tumor necrosis factor-alpha\u003c/p\u003e\n\u003cp\u003eHPA: Hypothalamic-pituitary-adrenal\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Center for Health Statistics Ethics Review Board of the U.S. CDC approved the NHANES protocols, and all participants gave their written informed consent for the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupport for this research came from the National Natural Science Foundation of China (No. 82105043).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData – Data Collection \u0026amp; Analysis: Miao-yu Ye, Di Zhang, Qian-ming Yi\u003c/p\u003e\n\u003cp\u003eFigures \u0026amp; Tables – draft: Miao-yu Ye, Di Zhang \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWriting – original draft: Miao-yu Ye, Jun-jie Chen\u003c/p\u003e\n\u003cp\u003eWriting – review \u0026amp; editing: Liu Wu, Jian Luo\u003c/p\u003e\n\u003cp\u003eWriting – revise \u0026amp; editing: Miao-yu Ye, Di Zhang,\u0026nbsp;Jia-zhi Gao\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no known competing financial interests or personal relationships which may have influenced the authors' work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSilverman MM, Berman AL.2014. 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Psychol Res Behav Manag 17:2587-2601.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Body roundness index, Suicidal ideation, Cross-sectional study, Mental health projection","lastPublishedDoi":"10.21203/rs.3.rs-5583719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5583719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eImportance:\u003c/h2\u003e \u003cp\u003eEmerging research suggests a complex interplay between physical health metrics and mental health outcomes, including suicidal ideation. The body roundness index (BRI), a novel measure of body size, may offer insights into this relationship. This study seeks to investigate the intricate connection between BRI and suicidal ideation in a sample that reflects the entire U.S. population-working to validate the potential of BRI as a predictor of suicidal ideation.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eData from the NHANES spanning 2013 to 2018 was used in this cross-sectional study to analyze a sample of 14,058 participants. BRI was calculated based on height and waist circumference measurements. Suicidal ideation was assessed using the Mental Health-Depression Screener Questionnaire ninth question. To investigate potential non-linear relationships between BRI and suicidal ideation, we employed generalized additive models and smooth curve fitting, and used ROC curves to compare the predictive ability of BMI and BRI for suicidal ideation. Subgroup analyses and interaction tests were utilized to study the moderating effects of categorical covariates.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eMultiple logistic regression analysis revealed a significant positive correlation between increased BRI and heightened suicidal ideation, with a nonlinear pattern persisting even after adjustment for covariates. This nonlinear relationship was reinforced by a sharp increase in the prevalence of suicidal ideation when the BRI exceeded 6.7. Based on ROC curve analysis, BRI demonstrates a slightly stronger predictive ability for suicide ideation compared to BMI. Subgroup analyses confirmed the relative robustness of this association across populations, including age, education and PRI, etc. However, the interaction between the two was influenced by gender, coronary artery disease and angina pectoris.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eIn conclusion, our cross-sectional study reveals a significant positive correlation between increased BRI and heightened suicidal ideation, with a sharp increase in prevalence when BRI exceeds 6.7. It is recommended that appropriate BRI be maintained to minimize suicidal ideation.\u003c/p\u003e","manuscriptTitle":"The relationship between body roundness index (BRI) and suicidal ideation: Evidence from NHANES 2013-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-26 08:55:34","doi":"10.21203/rs.3.rs-5583719/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-08T06:14:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-07T17:50:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185391406442745075384962254949280961336","date":"2025-03-27T15:05:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-25T12:49:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228656678641813314371481706514184144482","date":"2025-03-25T12:46:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-25T01:25:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-24T12:30:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2025-03-20T08:28:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"75f6e660-c5ae-4ba9-9694-2a1efb85d667","owner":[],"postedDate":"March 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T16:01:29+00:00","versionOfRecord":{"articleIdentity":"rs-5583719","link":"https://doi.org/10.1186/s12888-025-06834-z","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2025-04-17 15:57:44","publishedOnDateReadable":"April 17th, 2025"},"versionCreatedAt":"2025-03-26 08:55:34","video":"","vorDoi":"10.1186/s12888-025-06834-z","vorDoiUrl":"https://doi.org/10.1186/s12888-025-06834-z","workflowStages":[]},"version":"v1","identity":"rs-5583719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5583719","identity":"rs-5583719","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}