Associations between different triglyceride glucose index-related obesity indices and eating disorders: results from NHANES 2005–2018

Associations between different triglyceride glucose index-related obesity indices and eating disorders: results from NHANES 2005–2018 | 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 Associations between different triglyceride glucose index-related obesity indices and eating disorders: results from NHANES 2005–2018 He Xiao, Yudie Du, Ke Song, Yixing Ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5317726/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This research aimed to determine the possible links between obesity measures related to the triglyceride glucose (TyG) index and the prevalence of eating disorders (ED) for the United States residents. Methods This observational investigation analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2005–2018. It assessed the relationship of the TyG index, TyG combined with waist circumference (TyG-WC), or TyG combined with body mass index (TyG-BMI) with ED. The analysis employed a multivariable regression model, stratified analyses, and a ROC curve assessment. Results This research included a total of 10,324 adults. In the comprehensive analysis model, the TyG, TyG-BMI, along with TyG-WC all had a significant positive correlation with ED. The adjusted graphical representations revealed a rising trend in the association of TyG-BMI index with ED. Subgroup analyses indicated that individuals with hypertension exhibited even stronger positive associations between these indices and ED. The areas under the curve (AUC) values indicates the value for TyG-related indicators in predicting ED. Conclusions The research uncovered a significant and enduring connection between obesity measures related to the TyG-related index and ED, indicating a robust association of increased insulin resistance with the probability of ED among the U.S. population. eating disorders TyG-associated obesity index TyG index NHANES insulin resistance Figures Figure 1 Figure 2 Figure 3 Introduction There is a pressing need for increased awareness of eating disorders (ED) worldwide, encompassing conditions of bulimia nervosa (BN), binge-eating disorder (BED), and anorexia nervosa (AN). Those are defined by the emergence of irregular eating patterns, disruptions in body weight regulation, and excessive preoccupation with weight [ 1 ]. ED disrupt an individual's social interactions and psychological health [ 2 ]. ED exert significant impacts on the entire spectrum of bodily systems [ 3 ] which are linked to the highest rates specific to their causes among all mental health disorders [ 4 ]. The development of ED stems from a complex interplay of numerous factors [ 5 ]. Athif I et al. [ 6 ] suggested AN is linked to heightened insulin sensitivity, whereas BN and BED are connected to reduced insulin sensitivity. A notably higher proportion of individuals in the binge-drinking cohort exhibited insulin resistance (IR). Akshay K et al. [ 7 ] concluded that IR had a positive correlation with BED while it showed negative correlation with AN. Hence, a profound comprehension of the relationship between IR and ED is essential for effectively managing the associated conditions. Considering the intricacies of conventional IR index evaluations, the triglyceride glucose (TyG) index has been introduced, which gauges insulin sensitivity by utilizing levels of triglycerides (TG) and fasting blood glucose (FBG) [ 8 , 9 ]. Concurrently, a variety of novel indices have been suggested, integrating anthropometric factors associated with obesity to enhance the precision of insulin IR assessment. For example, body mass index (BMI) is a standardized measure for assessing overall adiposity and health status, while waist circumference (WC) serves as a frequently employed metric for gauging central obesity and its metabolic risks [ 10 , 11 ]. Considering the robust association between a high TyG index and an enhanced likelihood of ED, it has been theorized that a new composite index, which includes TyG along with obesity metrics, could be a valuable tool for predicting ED risk in population with IR. Specifically, the composite indicators TyG-WC and TyG-BMI demonstrated a pronounced relationship with metabolic syndrome like diabetes, and other conditions, providing a more precise reflection of disease risk than TyG alone [ 12 – 14 ]. Nevertheless, there is a scarcity of studies exploring the connection between these indices and ED specifically within the American demographic, underscoring the urgent requirement for further investigation to substantiate these associations. Consequently, the research delved into the National Health and Nutrition Examination Survey (NHANES) database to scrutinize a range of TyG-associated indicators, aiming to uncover the association of insulin resistance related to obesity with the propensity for ED. Methods Research methodology and participant selection This research retrieved information on 70,190 participants from the NHANES spanning the years 2005–2018. NHANES applied a multi-stage stratified probability sampling technique to gather comprehensive information regarding health conditions, lifestyle, and nutritional status of population in America [ 15 ]. The complete dataset was rigorously filtered according to exclusionary parameters which involved: (1) aged 60 years; (2) the presence of missing data on ED; (3) incomplete information of TyG, TyG-BMI and TyG-WC and (4) invalid questionnaires. Ultimately, the research involved a total of 10,324 participants. The overall process of selecting participant is exhibited in Fig. 1 . Definition and computation of obesity indices associated with TyG The TyG index assesses IR by integrating FBG and TG levels. These indicators’ measurements were taken at the outset when collected their blood samples at the first time. Concurrently, body weight, height, and WC were recorded after physical examinations conducted at a mobile examination center. The TyG, TyG-WC as well as TyG-BMI indicators, were derived using the subsequent calculations: (1) TyG = ln [triglycerides (mg/dl) × glucose (mg/dl)/2]; (2) BMI = body mass (kg)/height 2 (m 2 ); (3) TyG-WC = TyG×waist circumference; TyG-BMI = TyG×BMI. Eating Status Questionnaires The eating status was evaluated through NHANES questionnaires administered between 2005–2006 and 2017–2018, which inquired about the frequency of experiencing ED symptoms over the preceding half months. Their feedbacks were categorized as "not at all," "several days," "more than half the days," and "nearly every day," with scores allocated from 0 to 3. Assessment of covariates Age and poverty income ratio (PIR) was deemed as a continuous variable. According to gender, participants were grouped into males and females. Race/ethnicity involved in Mexican and non-Mexican Hispanics, non-Hispanic whites and blacks, along with others. They were divided into three groups by education level, including less than high school, high school graduate or equivalent as well as high school graduate or above. Marital status was divided into three groups, including married, widowed/divorced or separated and living with partner. Alcohol consumption was defined as how many alcoholic assumption per day in the past year. Weight, height and WC were assessed when attending a physical examination as continuous type variables. Besides, FBG, insulin, TG, high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were tested at baseline when providing blood samples as continuous variables. Diabetes, hypertension, sleep trouble, congestive heart failure, tumor or malignancy and liver status were assessed (Doctor informed you had any condition yes or no). Furthermore, we incorporated consideration for weight and attempt to lose weight from weight history questionnaires Statistical analyses All data from the NHANES 2005–2006 to 2017–2018 was handled through Empower Stats (version 2.0), along with Microsoft Office Excel. Descriptive statistics were represented as mean ± standard deviation (SD) for continuous variables, and as the count (n) and percentage (%) for categorical variables. Taking into account the complex multistage probability sampling methodology used in the NHANES, the study paid careful attention to underlying impacts of sample weights, stratification, and clustering within the dataset. As a result, the research categorized the TyG index into quartiles and utilized a weighted multivariable logistic regression model to examine the relationships between TyG-associated obesity indicators and ED. To conduct a thorough analysis, we formulated three models into adjustment. Model 1 served as an unadjusted; model 2 was was adjusted for basic demographic variables concerning age, gender, and race; and model 3 subjected to adjustments for multiple possible confounders: age, gender, race, education level, marital status, PIR, alcohol consumption, diabetes, hypertension, sleep trouble, congestive heart failure, liver condition, cancer or malignancy, consideration for weight, attempt to lose weight, WC, BMI, HDL-C, LDL-C, insulin, fasting blood glucose, and fasting triglycerides. Results Baseline information The fundamental features of participants are detailed in Table 1 . We classified never experiencing disordered eating and occasional ED (several days) as non-severe conditions. We classified frequent ED (more than half the days) and almost daily ED as severe conditions. The 10,324 participants exhibited an overall severe ED prevalence of 9.2% in the cohort. It comprised 48.534% males and 51.466% females in gender distribution. It included 18.965% Mexican Americans, 36.938% non-Hispanic whites, 19.366% non-Hispanic blacks, 11.428% with other Hispanic backgrounds, and 13.302% of other ethnicities. Compared to the non-severe ED group, the severe ED group presented with numerous distinct characteristics: higher BMI and WC, greater alcohol intake, higher rates of diabetes, sleep disorders, hypertension, congestive heart failure, and liver conditions, and higher levels of fasting triglycerides, fasting glucose, fasting insulin, TyG, TyG-BMI, as well as TyG-WC. Additionally, individuals in this group were more likely to perceive themselves as overweight or underweight and made more efforts to lose weight. Table 1 Initial demographic and clinical information of the cohort Variety Non-severe (n = 9374) Severe (n = 950) P -value Age (years) 40.294 ± 11.735 40.672 ± 11.682 0.37026 Gender (%) 0.84201 Male 48.175 48.534 Female 51.825 51.466 Race (%) 0.44684 Mexican American 17.360 18.965 Other Hispanic 10.118 11.428 Non-Hispanic White 38.874 36.938 Non-Hispanic Black 20.695 19.366 Other race 12.682 13.302 Educational level (%) 0.86185 Less than high school 22.468 23.406 High school graduate or equivalent 21.636 21.123 More than high school 55.716 55.470 Marital status (%) 0.34750 Married 49.859 52.376 Widowed/divorced or separated 38.967 37.432 Living with partner 11.175 10.192 PIR (%) 2.443 ± 1.570 2.434 ± 1.613 0.87936 Alcohol consumption (%) 0.00018 Light 23.626 19.131 Moderate 45.016 43.104 Heavy 31.358 37.765 Diabetes (%) < 0.00001 Yes 5.881 9.236 No 92.535 87.622 Borderline 1.585 3.142 Hypertension (%) < 0.00001 Yes 23.387 33.017 No 76.613 66.983 Sleep trouble (%) < 0.00001 Yes 24.052 50.157 No 75.948 49.843 Congestive heart failure (%) < 0.00001 Yes 0.820 2.581 No 99.180 97.419 Liver disease (%) < 0.00001 Yes 3.362 6.881 No 96.638 93.119 Cancer or malignancy (%) 0.05865 Yes 4.946 6.440 No 95.054 93.560 Consideration for weight (%) < 0.00001 Overweight 56.079 66.912 Underweight 4.186 9.033 About the right weight 39.735 24.055 Attempt to lose weight (%) 0.00028 Yes 35.285 41.563 No 64.715 58.437 HDL-C (mg/dL) 53.742 ± 15.861 51.302 ± 15.057 0.00002 LDL-C (mg/dL) 115.390 ± 33.963 114.010 ± 36.467 0.26210 Insulin (uU/mL) 12.339 ± 14.327 15.772 ± 16.600 < 0.00001 TG (mg/dL) 125.203 ± 112.786 139.177 ± 124.049 0.00065 FBG (mg/dL) 103.469 ± 29.137 107.445 ± 37.131 0.00021 BMI (kg/cm2) (%) < 0.00001 30 31.464 44.609 WC (cm) 97.913 ± 16.746 102.437 ± 18.336 < 0.00001 TyG 8.551 ± 0.676 8.672 ± 0.721 < 0.00001 TyG-BMI 247.439 ± 67.788 271.075 ± 78.756 < 0.00001 TyG-WC 841.673 ± 181.261 893.545 ± 199.530 < 0.00001 Continuous variables were listed as Mean ± Standard deviation (SD), the P-value was derived using a weighted linear regression model Categorical variables were listed as %, the P-value was derived using a weighted chi-square test Multifactor regression analysis To examine the regression equations for TyG index and tooth loss, we used a multifactor regression analysis and adjusted three models. Associations between TyG-BMI and ED were listed in Table 2 . TyG [β (95% confidence intervals (CI)] = 0.031 (0.005, 0.056), TyG-BMI [β (95% CI] = 0.001 (0.000, 0.002) and TyG-WC [β (95% CI] = 0.000 (0.000, 0.001) indicated a positive correlation with ED in the adjusted model. Concurrently, we employed one-way analysis of variance for trend analysis. The findings indicated a consistent trend across all three models for the TyG-BMI quartiles, suggesting that an enhanced TyG-BMI is correlated with a heightened ED risk. Table 2 Associations between TyG-BMI and ED TyG-BMI (continuous) 0.002 (0.001, 0.002) < 0.00001 0.002 (0.001, 0.002) < 0.00001 0.001 (0.000, 0.002) 0.00471 TyG-BMI (quartile) Q1 Reference Reference Reference Q2 0.028 (-0.014, 0.070) 0.19313 0.028 (-0.014, 0.070) 0.19134 0.019 (-0.030, 0.067) 0.44972 Q3 0.089 (0.047, 0.131) 0.00003 0.089 (0.047, 0.131) 0.00003 0.034 (-0.026, 0.095) 0.26941 Q4 0.266 (0.224, 0.308) < 0.00001 0.265 (0.223, 0.307) < 0.00001 0.086 (0.000, 0.172) 0.04965 P for trend < 0.001 < 0.001 < 0.001 Model 1: No covariates were adjusted; Model 2: Adjusted for gender, age and race; Model 3: Adjusted for all variables: gender, age, race, education level, PIR, marital status, waist, alcohol consumption status, hypertension status, diabetes status, liver condition, congestive heart failure, cancer or malignancy, sleep disorders, HDL, LDL, insulin, consideration for weight and attempt to lose weight. Model 1 sample size: 10324; Model 2 sample size: 10324; Model 3 sample size: 10324 Identification of nonlinear associations Given that prior multivariate analysis revealed a non-linear relationship between the baseline TyG-related index and ED, we utilized a smooth curve fitting technique (penalized spline method) to delve deeper into this correlation which were showed in Fig. 2 . Saturation effect analysis of TyG, TyG-BMI and TyG-WC on ED were demonstrated in Table 3 . In model 1, after adjusting for age, gender, race, BMI, alcohol consumption, education level, hypertension, and other covariates, the morbidity of ED rose by 0.002 (95% Cl = 0.001, 0.003; p < 0.001) with each unit increase in the TyG-BMI index respectively. In model 2, the inflection point was determined as 171.211 (P values for log-likelihood ratio = 0.039). the baseline TyG-BMI index was significantly and positively associated with the ED when TyG-BMI index exceeded 171.211 (β = 0.002; 95% CI = 0.001, 0.003), while TyG-BMI index was negatively associated with ED when TyG-BMI index was above 171.211 (β = -0.003; 95% CI = -0.006, -0.000). Table 3 Threshold effect analysis of TyG-related index on ED OR (95% Cl) P -value Model 1 Total 0.028 (0.002,0.054) 0.0332 Model 2 Inflection point 8.259 TyG index 8.259 0.034 (0.001, 0.067) 0.0422 P for Log-likelihood ratio 0.575 Model 1 Total 0.002 (0.001, 0.003) < 0.0001 Model 2 Inflection point 171.211 TyG-BMI index 171.211 0.002 (0.001, 0.003) < 0.0001 P for Log-likelihood ratio < 0.001 Model 1 Total -0.000 (-0.000, 0.000) 0.1503 Model 2 Inflection point 589.442 TyG-WC index 589.442 -0.000 (-0.000, 0.000) 0.3703 P for Log-likelihood ratio 0.002 Subgroup analyses The study conducted an in-depth examination of the intricate relationships between the TyG-related index and periodontitis through subgroup analyses accounting for gender, race, marital status, educational level, alcohol consumption, diabetes, sleep trouble, hypertension, congestive heart failure, cancer or malignancy, and liver condition. The results of subgroup analyses were listed in Table 4 . A noteworthy correlation between the TyG and TyG-BMI index and ED was identified among participants suffering from hypertension. Table 4 Subgroup analysis between TyG-related index and ED Subgroup β (95%CI) * P for interaction * β (95%CI) ** P for interaction ** β (95%CI) *** P for interaction *** Gender 0.7853 0.7853 0.0475 Male 0.025 (-0.012, 0.062) 0.025 (-0.012, 0.062) -0.000 (-0.000, 0.000) Female 0.032 (-0.004, 0.068) 0.032 (-0.004, 0.068) -0.000 (-0.000, 0.000) Race 0.4207 0.4207 0.1431 Mexican American 0.042 (-0.018, 0.102) 0.042 (-0.018, 0.102) -0.000 (-0.000, 0.000) Other Hispanic -0.026 (-0.108, 0.056) -0.026 (-0.108, 0.056) -0.000 (-0.001, 0.000) Non-Hispanic White 0.051 (-0.108, 0.056) 0.051 (-0.108, 0.056) -0.000 (-0.000, 0.000) Non-Hispanic Black 0.001 (-0.057, 0.058) 0.001 (-0.057, 0.058) -0.000 (-0.000, 0.000) Other race 0.019 (-0.056, 0.095) 0.019 (-0.056, 0.095) -0.001 (-0.001, -0.000) Marital status 0.4059 0.4059 0.2772 Married 0.014 (-0.023, 0.050) 0.014 (-0.023, 0.050) -0.000 (-0.000, -0.000) Widowed/divorced or separated 0.031 (-0.009, 0.072) 0.031 (-0.009, 0.072) -0.000 (-0.000, 0.000) Living with partner 0.070 (-0.007, 0.148) 0.070 (-0.007, 0.148) 0.000 (-0.000,0.001) Education level 0.4422 0.4422 0.3923 Less than high school 0.055 (0.003, 0.108) 0.055 (0.003, 0.108) 0.000 (-0.000,0.000) High school graduate or equivalent 0.022 (-0.033, 0.077) 0.022 (-0.033, 0.077) -0.000 (-0.000, 0.000) More than high school 0.015 (-0.021, 0.051) 0.015 (-0.021, 0.051) -0.000 (-0.000, -0.000) Alcohol consumption 0.6831 0.6831 0.7924 Light 0.046 (-0.015, 0.107) 0.046 (-0.015, 0.107) -0.000 (-0.000, 0.000) Moderate 0.021 (-0.017, 0.058) 0.021 (-0.017, 0.058) -0.000 (-0.000, 0.000) Heavy 0.013 (-0.033, 0.058) 0.013 (-0.033, 0.058) -0.000 (-0.000, 0.000) Diabetes 0.1339 0.1339 0.1996 Yes 0.093 (0.024, 0.162) 0.093 (0.024, 0.162) 0.000 (-0.000, 0.001) No 0.018 (-0.011, 0.047) 0.018(-0.011, 0.047) -0.000 (-0.000, -0.000) Borderline -0.004 (-0.184, 0.177) -0.004 (-0.184, 0.177) 0.000 (-0.001, 0.001) Hypertension 0.0442 0.0442 0.0530 Yes 0.068 (0.021, 0.115) 0.068 (0.021, 0.115) 0.000 (-0.000, 0.000) No 0.013 (-0.017, 0.043) 0.013 (-0.017, 0.043) -0.000 (-0.000, -0.000) Sleep trouble 0.8296 0.8296 0.1677 Yes 0.033 (-0.017, 0.084) 0.033 (-0.017, 0.084) 0.000 (-0.000, 0.000) No 0.027 (-0.002, 0.056) 0.027 (-0.002, 0.056) -0.000 (-0.000, 0.000) Congestive heart failure 0.4022 0.4022 0.3006 Yes 0.117 (-0.095, 0.329) 0.117 (-0.095, 0.329) -0.001 (-0.002, 0.000) No 0.026 (-0.000, 0.052) 0.026 (-0.000, 0.052) -0.000 (-0.000, 0.000) Liver condition 0.0604 0.0604 0.0866 Yes 0.152 (0.021, 0.284) 0.152 (0.021, 0.284) 0.001 (-0.000, 0.001) No 0.024 (-0.003, 0.051) 0.024 (-0.003, 0.051) -0.000 (-0.000, -0.000) Cancer or malignancy 0.3325 0.3325 0.6929 Yes 0.094 (-0.042, 0.230) 0.094 (-0.042, 0.230) -0.000 (-0.001, 0.001) No 0.026 (-0.000, 0.053) 0.026 (-0.000, 0.053) -0.000 (-0.000, 0.000) * TyG; ** TyG-BMI; *** TyG-WC Sensitivity and specificity analysis ROC analysis was conducted to evaluate the prognostic value of TyG-related index for ED. The results of the ROC curves were shown in Table 5 and Fig. 3 . The areas under the curve (AUC) of TyG index, TyG-BMI index and TyG-WC index in predicting ED were 0.550 (95%CI: 0.524, 0.577), 0.587 (95%CI: 0.559, 0.616) and 0.582 (95%CI: 0.555, 0.610), respectively. Since the AUC of TyG-BMI was the largest, its value of predicting ED seemed greater than TyG and TyG-WC index. (P > 0.05). Table 5 Model predictions of association between TyG-related index and ED Objects Cutoff (Sensitivity, Specificity) AUC (95%CI) TyG 0.050 (0.287, 0.798) 0.550 (0.524, 0.577) TyG-BMI 0.046 (0.496, 0.661) 0.587 (0.559, 0.616) TyG-WC 0.044 (0.571, 0.569) 0.582 (0.555, 0.610) Discussion The predominant discoveries of this study revealed that TyG-related index was positively correlated with the occurrence of ED in the full adjustment model. Furthermore, an increased TyG-BMI index is associated with a heightened risk of ED. Subgroup analyses showed that strong relationship between TyG and TyG-BMI index and ED was more likely to be observed among participants with hypertension. Threshold effect analysis indicated that higher TyG-BMI was significantly associated with a higher risk of ED. Results of ROC analysis indicated that the TyG-related index had valid predictive value for ED. Previous studies have indicated that metabolic syndrome associated with insulin resistance (IR) is notably influential in in increasing the risk of ED [ 16 ]. The homeostasis model assessment of insulin resistance (HOMA-IR), is a traditional indicator of IR [ 17 ]. Nevertheless, considering the expense of insulin testing and the constraints of HOMA-IR, there is a need for a more effective, and user-friendly metric. The TyG index offers equivalent or superior results for assessing IR, owing to its ease of computation, sensitivity, and specificity [ 18 – 20 ]. Previous studies have validated the effectiveness of WC and BMI in predicting obesity [ 21 ]. A nationwide cohort study indicated that the combination of obesity indicators with the TyG index could more accurately predict the risk of metabolic syndrome than the use of these metrics alone [ 22 ]. Furthermore, TyG-WC, and TyG-BMI demonstrated greater strength in predicting IR than TyG alone [ 23 ]. Consequently, the integration of obesity metrics with TyG might more precisely capture the relationship between IR and ED. Although the precise physiological processes remain unclear, several elements could account for this link. Flint et al. [ 24 ] deduced that the insulin response following a meal could serve as a significant signal for satiety, and that central nervous system IR in overweight individuals might be responsible for the diminished impact on appetite control. The homeostatic approach to appetite regulation involves feedback which is attributed to the inhibitory action of insulin. Gastrointestinal hormones, such as cholecystokinin, and glucagon-like peptide-1 initiate a series of neural and hormonal signals that operate both peripherally and centrally to facilitate satiation [ 25 ]. These hormones collaborate with the appetite-stimulating hormone ghrelin to manage food intake in a cyclical manner. Insulin provides a steady influence on appetite by fine-tuning the intensity of the cyclical signals [ 25 ]. Peripheral neuroendocrine signals converge in the hypothalamus. Upon transmission to the arcuate nucleus through the nucleus tractus solitarius, these signals activate the orexigenic neurons that express agouti-related peptide (AgRP) and neuropeptide Y (NPY), as well as the anorexigenic pro-opiomelanocortin (POMC) neurons that release alpha-melanocyte-stimulating hormone [ 26 ]. The equilibrium in the release of these functionally opposing neuropeptides confers the satiation [ 27 ]. The ghrelin hormone activates the orexigenic NPY/AgRP neurons in the arcuate nucleus, while glucose and insulin have an inhibitory effect on them. Conversely, the POMC/CART neurons are activated by insulin and glucose, but their activity is suppressed when NPY/AgRP neurons are stimulated [ 27 ]. While glucose and insulin regulate appetite together, there may be specific conditions where glucose homeostasis has a greater impact on eating behavior. The glucostatic theory of appetite, proposed by Mayer [ 28 ], proposed that alterations in the utilization of glucose were crucial for encoding feelings of hunger, with "metabolic hypoglycemia" serving as a trigger for starting a meal. In support of this, some studies assessed the link among glycemic load, glycemic responses and appetite [ 29 ] and suggest that lower glycemic loads and glycemic responses leads to reduced post-meal appetite. Hypoglycemia, common in those with diabetes [ 30 ], is met with strong neuroendocrine reactions aimed at restoring euglycemia [ 31 ]. Hypoglycemia boosts the activity of hypothalamic NPY/AgRP neurons and diminishes that of POMC/CART neurons, leading to overeating [ 32 ]. Beyond its peripheral effects, insulin also centrally modulates neuronal activity in brain areas associated with eating behavior, sensory processing, and reward [ 33 ]. It is hypothesized that brain IR coincides with peripheral resistance in areas for appetite [ 34 ]. Vagal fibers that innervate the gastrointestinal tract are pivotal to the gut-brain axis since fibers facilitates communication between the hypothalamus and peripheral organs, such as the liver and adipose tissue [ 35 ]. However, the expression of vagal gastrointestinal hormone receptors and sensitivity to peripheral stimuli might be diminished in individuals with obesity and diabetes [ 36 ], which implies that an excessive fat, commonly observed in type 2 diabetes (T2D), could interfere with the vagal perception of peripheral appetite signals and their recognition in brain areas critical to ED. The insulin-reward pathways could be compromised in T2D, given that peripheral IR is linked to IR in brain areas that govern the rewarding aspects of food [ 34 ]. Additionally, hippocampal damage has been demonstrated to lead to ED, such as a diminished ability to sense hunger and fullness and a reduced mental representation of food [ 37 ]. This research contributes significant findings that highlight the positive associations between TyG-related index and ED. The study benefits from a substantial sample size and nationwide coverage. Additionally, the analysis controlled for various confounding factors associated with ED, which strengthens the reliability of the outcomes. However, there are limitations to acknowledge. First, the study limits the ability to determine causality between TyG-related index and ED. Second, it is difficult to completely eliminate the potential impact of unmeasured confounders. Lastly, the study's focus on the US population may restrict the applicability of the results to a global setting. Future studies should aim to include larger and more diverse samples to identify the most predictive TyG-related indices for ED and to establish universally applicable threshold values. Conclusion The research confirmed a significant association between the TyG-related index and ED in the U.S. population. This finding highlights the potential of TyG-related obesity indices for assessing the risk of ED and accentuates the importance of IR in the development of ED. Regular monitoring of the TyG-related index could enable clinicians to pinpoint individuals at a higher risk for ED, facilitating the application of targeted preventative measures. However, the precise molecular mechanisms that connect the TyG-related index with ED remain to be fully understood. Future studies should aim to explore the possible biological pathways that associate the TyG-related index with ED and to determine whether interventions that lower the TyG-related index could lead to a reduction in the prevalence of ED. Declarations Ethics approval and consent to participate Not applicable. Competing interests The authors declare no competing interests. Funding This work was supported by the National Natural Science Foundation of China (82370601). Author Contribution H.X. was responsible for the conception and writing. Y.D. contributed to making figures and tables. K.S. performed the investigation. Y.R. reviewed and edited this manuscript. All the authors reviewed the manuscript and agreed to publish it. Acknowledgments Not applicable. Availability of data and material All data for this study can be found on the NHANES website. References Himmerich H, Keeler JL, Davies HL, Tessema SA, Treasure J. The evolving profile of eating disorders and their treatment in a changing and globalised world. Lancet. 2024;403:2671–5. 10.1016/s0140-6736(24)00874-2 . Santomauro DF, Melen S, Mitchison D, Vos T, Whiteford H, Ferrari AJ. The hidden burden of eating disorders: an extension of estimates from the Global Burden of Disease Study 2019. Lancet Psychiatry. 2021;8:320–8. 10.1016/s2215-0366(21)00040-7 . Wu J, Liu J, Li S, Ma H, Wang Y. Trends in the prevalence and disability-adjusted life years of eating disorders from 1990 to 2017: results from the Global Burden of Disease Study 2017. Epidemiol Psychiatr Sci. 2020;29:e191. 10.1017/s2045796020001055 . Global regional. and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry 2022, 9:137–150. 10.1016/s2215-0366(21)00395-3 Culbert KM, Racine SE, Klump KL. Research Review: What we have learned about the causes of eating disorders - a synthesis of sociocultural, psychological, and biological research. J Child Psychol Psychiatry. 2015;56:1141–64. 10.1111/jcpp.12441 . Ilyas A, Hübel C, Stahl D, Stadler M, Ismail K, Breen G, Treasure J, Kan C. The metabolic underpinning of eating disorders: A systematic review and meta-analysis of insulin sensitivity. Mol Cell Endocrinol. 2019;497:110307. 10.1016/j.mce.2018.10.005 . Kumar A, Alam S, Bano S, Prakash R, Jain V. Association of eating disorders with glycaemic control and insulin resistance in patients of type 2 diabetes mellitus. Int J Biochem Mol Biol. 2023;14:40–50. Lee J, Kim B, Kim W, Ahn C, Choi HY, Kim JG, Kim J, Shin H, Kang JG, Moon S. Lipid indices as simple and clinically useful surrogate markers for insulin resistance in the U.S. population. Sci Rep. 2021;11:2366. 10.1038/s41598-021-82053-2 . Son DH, Lee HS, Lee YJ, Lee JH, Han JH. Comparison of triglyceride-glucose index and HOMA-IR for predicting prevalence and incidence of metabolic syndrome. Nutr Metab Cardiovasc Dis. 2022;32:596–604. 10.1016/j.numecd.2021.11.017 . Moosaie F, Abhari SMF, Deravi N, Behnagh AK, Esteghamati S, Firouzabadi FD, Rabizadeh S, Nakhjavani M, Esteghamati A. Waist-To-Height Ratio Is a More Accurate Tool for Predicting Hypertension Than Waist-To-Hip Circumference and BMI in Patients With Type 2 Diabetes: A Prospective Study. Front Public Health. 2021;9. 10.DOI:10.3389/fpubh.2021.726288 . Katzmarzyk PT, Hu G, Cefalu WT, Mire E, Bouchard C. The Importance of Waist Circumference and BMI for Mortality Risk in Diabetic Adults. Diabetes Care. 2013;36:3128–30. 10.2337/dc13-0219 . Li X, Sun M, Yang Y, Yao N, Yan S, Wang L, Hu W, Guo R, Wang Y, Li B. Predictive Effect of Triglyceride Glucose-Related Parameters, Obesity Indices, and Lipid Ratios for Diabetes in a Chinese Population: A Prospective Cohort Study. Front Endocrinol (Lausanne). 2022;13:862919. 10.3389/fendo.2022.862919 . Raimi TH, Dele-Ojo BF, Dada SA, Fadare JO, Ajayi DD, Ajayi EA, Ajayi OA. Triglyceride-Glucose Index and Related Parameters Predicted Metabolic Syndrome in Nigerians. Metab Syndr Relat Disord. 2021;19:76–82. 10.1089/met.2020.0092 . Yan S, Wang D, Jia Y. Comparison of insulin resistance-associated parameters in US adults: a cross-sectional study. Horm (Athens). 2023;22:331–41. 10.1007/s42000-023-00448-4 . Ghimire S, Baral BK, Feng D, Sy FS, Rodriguez R. Is selenium intake associated with the presence of depressive symptoms among US adults? Findings from National Health and Nutrition Examination Survey (NHANES) 2011–2014. Nutrition. 2019;62:169–76. 10.1016/j.nut.2018.12.007 . Berthoud HR. Mind versus metabolism in the control of food intake and energy balance. Physiol Behav. 2004;81:781–93. 10.1016/j.physbeh.2004.04.034 . Tahapary DL, Pratisthita LB, Fitri NA, Marcella C, Wafa S, Kurniawan F, Rizka A, Tarigan TJE, Harbuwono DS, Purnamasari D, Soewondo P. Challenges in the diagnosis of insulin resistance: Focusing on the role of HOMA-IR and Tryglyceride/glucose index. Diabetes Metab Syndr. 2022;16(102581). 10.1016/j.dsx.2022.102581 . Guerrero-Romero F, Villalobos-Molina R, Jiménez-Flores JR, Simental-Mendia LE, Méndez-Cruz R, Murguía-Romero M, Rodríguez-Morán M. Fasting Triglycerides and Glucose Index as a Diagnostic Test for Insulin Resistance in Young Adults. Arch Med Res. 2016;47:382–7. 10.1016/j.arcmed.2016.08.012 . Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6:299–304. 10.1089/met.2008.0034 . Wang S, Shi J, Peng Y, Fang Q, Mu Q, Gu W, Hong J, Zhang Y, Wang W. Stronger association of triglyceride glucose index than the HOMA-IR with arterial stiffness in patients with type 2 diabetes: a real-world single-centre study. Cardiovasc Diabetol. 2021;20(82). 10.1186/s12933-021-01274-x . Park Y, Kim NH, Kwon TY, Kim SG. A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Sci Rep. 2018;8:16753. 10.1038/s41598-018-35073-4 . Gui J, Li Y, Liu H, Guo LL, Li J, Lei Y, Li X, Sun L, Yang L, Yuan T, et al. Obesity- and lipid-related indices as a predictor of obesity metabolic syndrome in a national cohort study. Front Public Health. 2023;11:1073824. 10.3389/fpubh.2023.1073824 . Lim J, Kim J, Koo SH, Kwon GC. Comparison of triglyceride glucose index, and related parameters to predict insulin resistance in Korean adults: An analysis of the 2007–2010 Korean National Health and Nutrition Examination Survey. PLoS ONE. 2019;14:e0212963. 10.1371/journal.pone.0212963 . Flint A, Gregersen NT, Gluud LL, Møller BK, Raben A, Tetens I, Verdich C, Astrup A. Associations between postprandial insulin and blood glucose responses, appetite sensations and energy intake in normal weight and overweight individuals: a meta-analysis of test meal studies. Br J Nutr. 2007;98:17–25. 10.1017/s000711450768297x . Schwartz MW, Seeley RJ, Zeltser LM, Drewnowski A, Ravussin E, Redman LM, Leibel RL. Obesity Pathogenesis: An Endocrine Society Scientific Statement. Endocr Rev. 2017;38:267–96. 10.1210/er.2017-00111 . Morton GJ, Cummings DE, Baskin DG, Barsh GS, Schwartz MW. Central nervous system control of food intake and body weight. Nature. 2006;443:289–95. 10.1038/nature05026 . López-Gambero AJ, Martínez F, Salazar K, Cifuentes M, Nualart F. Brain Glucose-Sensing Mechanism and Energy Homeostasis. Mol Neurobiol. 2019;56:769–96. 10.1007/s12035-018-1099-4 . Mayer J. Glucostatic mechanism of regulation of food intake. N Engl J Med. 1953;249:13–6. 10.1056/nejm195307022490104 . Gonzalez-Anton C, Lopez-Millan B, Rico MC, Sanchez-Rodriguez E, Ruiz-Lopez MD, Gil A, Mesa MD. An enriched, cereal-based bread affects appetite ratings and glycemic, insulinemic, and gastrointestinal hormone responses in healthy adults in a randomized, controlled trial. J Nutr. 2015;145:231–8. 10.3945/jn.114.200386 . Leese GP, Wang J, Broomhall J, Kelly P, Marsden A, Morrison W, Frier BM, Morris AD. Frequency of severe hypoglycemia requiring emergency treatment in type 1 and type 2 diabetes: a population-based study of health service resource use. Diabetes Care. 2003;26:1176–80. 10.2337/diacare.26.4.1176 . Watts AG, Donovan CM. Sweet talk in the brain: glucosensing, neural networks, and hypoglycemic counterregulation. Front Neuroendocrinol. 2010;31:32–43. 10.1016/j.yfrne.2009.10.006 . Sindelar DK, Ste Marie L, Miura GI, Palmiter RD, McMinn JE, Morton GJ, Schwartz MW. Neuropeptide Y is required for hyperphagic feeding in response to neuroglucopenia. Endocrinology. 2004;145:3363–8. 10.1210/en.2003-1727 . Arnold SE, Arvanitakis Z, Macauley-Rambach SL, Koenig AM, Wang HY, Ahima RS, Craft S, Gandy S, Buettner C, Stoeckel LE, et al. Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums. Nat Rev Neurol. 2018;14:168–81. 10.1038/nrneurol.2017.185 . Anthony K, Reed LJ, Dunn JT, Bingham E, Hopkins D, Marsden PK, Amiel SA. Attenuation of insulin-evoked responses in brain networks controlling appetite and reward in insulin resistance: the cerebral basis for impaired control of food intake in metabolic syndrome? Diabetes 2006, 55:2986–92. 10.2337/db06-0376 Cammisotto P, Bendayan M. A review on gastric leptin: the exocrine secretion of a gastric hormone. Anat Cell Biol. 2012;45:1–16. 10.5115/acb.2012.45.1.1 . de Lartigue G. Role of the vagus nerve in the development and treatment of diet-induced obesity. J Physiol. 2016;594:5791–815. 10.1113/jp271538 . Stevenson RJ, Francis HM. The hippocampus and the regulation of human food intake. Psychol Bull. 2017;143:1011–32. 10.1037/bul0000109 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5317726","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369868388,"identity":"519379fc-1d47-47e8-9eb4-1bd13a42dccf","order_by":0,"name":"He Xiao","email":"","orcid":"","institution":"North Sichuan Medical University","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Xiao","suffix":""},{"id":369868389,"identity":"c83b47d9-0841-487a-83ee-edeaf105dfb8","order_by":1,"name":"Yudie Du","email":"","orcid":"","institution":"North Sichuan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yudie","middleName":"","lastName":"Du","suffix":""},{"id":369868390,"identity":"0787ac9b-71ce-4200-822a-d0eab96664e6","order_by":2,"name":"Ke Song","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Song","suffix":""},{"id":369868391,"identity":"ba86a101-3128-4f5e-96a7-c231df1345f4","order_by":3,"name":"Yixing Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACZgaGA2AGewNU5ADRWnhgSglqgQOJBCK1GBznMTxc8OuenLnk28OfbtQwyPHdSGD8XIBPy2G2hMMz+4qNLWfnpUnnHGMwlryRwCw9A68W5gOHeXsSEjfczjFjzm1gSNxwI4GNmQevFsYGkJb6DTfPGH8GaqknQgvQFp4fCQkGN3gMpIFagAwCWiRBfuFtSDDccCbHDOgXCcOZZx42S+PTwnce6B6ePwnyBseBjJwaG3m+48kHP+PTonAASDC2wfkSIG4DHg0MDPJg6T941YyCUTAKRsFIBwDN1U84M8P6xAAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":true,"prefix":"","firstName":"Yixing","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2024-10-23 09:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5317726/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5317726/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67608324,"identity":"aa4c13ea-a00a-4edd-91ef-dbeb9a07ef75","added_by":"auto","created_at":"2024-10-28 05:09:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":290306,"visible":true,"origin":"","legend":"\u003cp\u003eEligible Participant Selection Flowchart\u003c/p\u003e","description":"","filename":"Fig1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5317726/v1/c9be0dab69c0c3dfe7dfa6f1.jpg"},{"id":67608326,"identity":"7a4e9024-6c8d-490f-8578-b4e25a13653f","added_by":"auto","created_at":"2024-10-28 05:09:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":252222,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline fitting for the association between TyG-related index levels with eating disorders. \u003cstrong\u003e(a) \u003c/strong\u003eRestricted cubic spline fitting for the association between TyG index with eating disorders. \u003cstrong\u003e(b) \u003c/strong\u003eRestricted cubic spline fitting for the association between TyG-BMI index with eating disorders. \u003cstrong\u003e(c) \u003c/strong\u003eRestricted cubic spline fitting for the association between TyG-WC index with eating disorders\u003c/p\u003e","description":"","filename":"Fig2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5317726/v1/703f94ba6811ae2256eeaa67.jpg"},{"id":67608325,"identity":"0013fb2e-1797-4c4b-99dc-e90d64198e06","added_by":"auto","created_at":"2024-10-28 05:09:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":271290,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for TyG-related index to predict eating disorders\u003c/p\u003e","description":"","filename":"Fig3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5317726/v1/f7726f3c68d3c38a002c27c6.jpg"},{"id":67626653,"identity":"dcec032b-9899-4cb0-9fe9-36044a506cbb","added_by":"auto","created_at":"2024-10-28 08:02:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1872655,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5317726/v1/9ee34576-69c3-4bb4-bbdc-6d9e98620577.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between different triglyceride glucose index-related obesity indices and eating disorders: results from NHANES 2005–2018","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThere is a pressing need for increased awareness of eating disorders (ED) worldwide, encompassing conditions of bulimia nervosa (BN), binge-eating disorder (BED), and anorexia nervosa (AN). Those are defined by the emergence of irregular eating patterns, disruptions in body weight regulation, and excessive preoccupation with weight [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. ED disrupt an individual's social interactions and psychological health [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. ED exert significant impacts on the entire spectrum of bodily systems [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] which are linked to the highest rates specific to their causes among all mental health disorders [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The development of ED stems from a complex interplay of numerous factors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Athif I et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] suggested AN is linked to heightened insulin sensitivity, whereas BN and BED are connected to reduced insulin sensitivity. A notably higher proportion of individuals in the binge-drinking cohort exhibited insulin resistance (IR). Akshay K et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] concluded that IR had a positive correlation with BED while it showed negative correlation with AN. Hence, a profound comprehension of the relationship between IR and ED is essential for effectively managing the associated conditions.\u003c/p\u003e \u003cp\u003eConsidering the intricacies of conventional IR index evaluations, the triglyceride glucose (TyG) index has been introduced, which gauges insulin sensitivity by utilizing levels of triglycerides (TG) and fasting blood glucose (FBG) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Concurrently, a variety of novel indices have been suggested, integrating anthropometric factors associated with obesity to enhance the precision of insulin IR assessment. For example, body mass index (BMI) is a standardized measure for assessing overall adiposity and health status, while waist circumference (WC) serves as a frequently employed metric for gauging central obesity and its metabolic risks [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Considering the robust association between a high TyG index and an enhanced likelihood of ED, it has been theorized that a new composite index, which includes TyG along with obesity metrics, could be a valuable tool for predicting ED risk in population with IR. Specifically, the composite indicators TyG-WC and TyG-BMI demonstrated a pronounced relationship with metabolic syndrome like diabetes, and other conditions, providing a more precise reflection of disease risk than TyG alone [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNevertheless, there is a scarcity of studies exploring the connection between these indices and ED specifically within the American demographic, underscoring the urgent requirement for further investigation to substantiate these associations. Consequently, the research delved into the National Health and Nutrition Examination Survey (NHANES) database to scrutinize a range of TyG-associated indicators, aiming to uncover the association of insulin resistance related to obesity with the propensity for ED.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch methodology and participant selection\u003c/h2\u003e \u003cp\u003eThis research retrieved information on 70,190 participants from the NHANES spanning the years 2005\u0026ndash;2018. NHANES applied a multi-stage stratified probability sampling technique to gather comprehensive information regarding health conditions, lifestyle, and nutritional status of population in America [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The complete dataset was rigorously filtered according to exclusionary parameters which involved: (1) aged\u0026thinsp;\u0026lt;\u0026thinsp;20 years, aged\u0026thinsp;\u0026gt;\u0026thinsp;60 years; (2) the presence of missing data on ED; (3) incomplete information of TyG, TyG-BMI and TyG-WC and (4) invalid questionnaires. Ultimately, the research involved a total of 10,324 participants. The overall process of selecting participant is exhibited in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition and computation of obesity indices associated with TyG\u003c/h3\u003e\n\u003cp\u003eThe TyG index assesses IR by integrating FBG and TG levels. These indicators\u0026rsquo; measurements were taken at the outset when collected their blood samples at the first time. Concurrently, body weight, height, and WC were recorded after physical examinations conducted at a mobile examination center. The TyG, TyG-WC as well as TyG-BMI indicators, were derived using the subsequent calculations: (1) TyG\u0026thinsp;=\u0026thinsp;ln [triglycerides (mg/dl) \u0026times; glucose (mg/dl)/2]; (2) BMI\u0026thinsp;=\u0026thinsp;body mass (kg)/height\u003csup\u003e2\u003c/sup\u003e(m\u003csup\u003e2\u003c/sup\u003e); (3) TyG-WC\u0026thinsp;=\u0026thinsp;TyG\u0026times;waist circumference; TyG-BMI\u0026thinsp;=\u0026thinsp;TyG\u0026times;BMI.\u003c/p\u003e\n\u003ch3\u003eEating Status Questionnaires\u003c/h3\u003e\n\u003cp\u003eThe eating status was evaluated through NHANES questionnaires administered between 2005\u0026ndash;2006 and 2017\u0026ndash;2018, which inquired about the frequency of experiencing ED symptoms over the preceding half months. Their feedbacks were categorized as \"not at all,\" \"several days,\" \"more than half the days,\" and \"nearly every day,\" with scores allocated from 0 to 3.\u003c/p\u003e\n\u003ch3\u003eAssessment of covariates\u003c/h3\u003e\n\u003cp\u003eAge and poverty income ratio (PIR) was deemed as a continuous variable. According to gender, participants were grouped into males and females. Race/ethnicity involved in Mexican and non-Mexican Hispanics, non-Hispanic whites and blacks, along with others. They were divided into three groups by education level, including less than high school, high school graduate or equivalent as well as high school graduate or above. Marital status was divided into three groups, including married, widowed/divorced or separated and living with partner. Alcohol consumption was defined as how many alcoholic assumption per day in the past year. Weight, height and WC were assessed when attending a physical examination as continuous type variables. Besides, FBG, insulin, TG, high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were tested at baseline when providing blood samples as continuous variables. Diabetes, hypertension, sleep trouble, congestive heart failure, tumor or malignancy and liver status were assessed (Doctor informed you had any condition yes or no). Furthermore, we incorporated consideration for weight and attempt to lose weight from weight history questionnaires\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAll data from the NHANES 2005\u0026ndash;2006 to 2017\u0026ndash;2018 was handled through Empower Stats (version 2.0), along with Microsoft Office Excel. Descriptive statistics were represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for continuous variables, and as the count (n) and percentage (%) for categorical variables. Taking into account the complex multistage probability sampling methodology used in the NHANES, the study paid careful attention to underlying impacts of sample weights, stratification, and clustering within the dataset. As a result, the research categorized the TyG index into quartiles and utilized a weighted multivariable logistic regression model to examine the relationships between TyG-associated obesity indicators and ED. To conduct a thorough analysis, we formulated three models into adjustment. Model 1 served as an unadjusted; model 2 was was adjusted for basic demographic variables concerning age, gender, and race; and model 3 subjected to adjustments for multiple possible confounders: age, gender, race, education level, marital status, PIR, alcohol consumption, diabetes, hypertension, sleep trouble, congestive heart failure, liver condition, cancer or malignancy, consideration for weight, attempt to lose weight, WC, BMI, HDL-C, LDL-C, insulin, fasting blood glucose, and fasting triglycerides.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline information\u003c/h2\u003e \u003cp\u003eThe fundamental features of participants are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We classified never experiencing disordered eating and occasional ED (several days) as non-severe conditions. We classified frequent ED (more than half the days) and almost daily ED as severe conditions. The 10,324 participants exhibited an overall severe ED prevalence of 9.2% in the cohort. It comprised 48.534% males and 51.466% females in gender distribution. It included 18.965% Mexican Americans, 36.938% non-Hispanic whites, 19.366% non-Hispanic blacks, 11.428% with other Hispanic backgrounds, and 13.302% of other ethnicities. Compared to the non-severe ED group, the severe ED group presented with numerous distinct characteristics: higher BMI and WC, greater alcohol intake, higher rates of diabetes, sleep disorders, hypertension, congestive heart failure, and liver conditions, and higher levels of fasting triglycerides, fasting glucose, fasting insulin, TyG, TyG-BMI, as well as TyG-WC. Additionally, individuals in this group were more likely to perceive themselves as overweight or underweight and made more efforts to lose weight.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInitial demographic and clinical information of the cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-severe\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;9374)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;950)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.294\u0026thinsp;\u0026plusmn;\u0026thinsp;11.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.672\u0026thinsp;\u0026plusmn;\u0026thinsp;11.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/divorced or separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIR (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.443\u0026thinsp;\u0026plusmn;\u0026thinsp;1.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.434\u0026thinsp;\u0026plusmn;\u0026thinsp;1.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorderline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep trouble (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCongestive heart failure (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiver disease (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer or malignancy (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConsideration for weight (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbout the right weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAttempt to lose weight (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.742\u0026thinsp;\u0026plusmn;\u0026thinsp;15.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.302\u0026thinsp;\u0026plusmn;\u0026thinsp;15.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.390\u0026thinsp;\u0026plusmn;\u0026thinsp;33.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.010\u0026thinsp;\u0026plusmn;\u0026thinsp;36.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsulin (uU/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.339\u0026thinsp;\u0026plusmn;\u0026thinsp;14.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.772\u0026thinsp;\u0026plusmn;\u0026thinsp;16.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.203\u0026thinsp;\u0026plusmn;\u0026thinsp;112.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139.177\u0026thinsp;\u0026plusmn;\u0026thinsp;124.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFBG (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.469\u0026thinsp;\u0026plusmn;\u0026thinsp;29.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.445\u0026thinsp;\u0026plusmn;\u0026thinsp;37.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/cm2) (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWC (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.913\u0026thinsp;\u0026plusmn;\u0026thinsp;16.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.437\u0026thinsp;\u0026plusmn;\u0026thinsp;18.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.551\u0026thinsp;\u0026plusmn;\u0026thinsp;0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.672\u0026thinsp;\u0026plusmn;\u0026thinsp;0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247.439\u0026thinsp;\u0026plusmn;\u0026thinsp;67.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e271.075\u0026thinsp;\u0026plusmn;\u0026thinsp;78.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-WC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e841.673\u0026thinsp;\u0026plusmn;\u0026thinsp;181.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e893.545\u0026thinsp;\u0026plusmn;\u0026thinsp;199.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eContinuous variables were listed as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard deviation (SD), the P-value was derived using a weighted linear regression model\u003c/p\u003e \u003cp\u003eCategorical variables were listed as %, the P-value was derived using a weighted chi-square test\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultifactor regression analysis\u003c/h3\u003e\n\u003cp\u003eTo examine the regression equations for TyG index and tooth loss, we used a multifactor regression analysis and adjusted three models. Associations between TyG-BMI and ED were listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. TyG [β (95% confidence intervals (CI)]\u0026thinsp;=\u0026thinsp;0.031 (0.005, 0.056), TyG-BMI [β (95% CI]\u0026thinsp;=\u0026thinsp;0.001 (0.000, 0.002) and TyG-WC [β (95% CI]\u0026thinsp;=\u0026thinsp;0.000 (0.000, 0.001) indicated a positive correlation with ED in the adjusted model. Concurrently, we employed one-way analysis of variance for trend analysis. The findings indicated a consistent trend across all three models for the TyG-BMI quartiles, suggesting that an enhanced TyG-BMI is correlated with a heightened ED risk.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between TyG-BMI and ED\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI (continuous)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002 (0.001, 0.002)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002 (0.001, 0.002)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001 (0.000, 0.002)\u003c/p\u003e \u003cp\u003e0.00471\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI (quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.028 (-0.014, 0.070)\u003c/p\u003e \u003cp\u003e0.19313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028 (-0.014, 0.070)\u003c/p\u003e \u003cp\u003e0.19134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019 (-0.030, 0.067)\u003c/p\u003e \u003cp\u003e0.44972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.089 (0.047, 0.131)\u003c/p\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.089 (0.047, 0.131)\u003c/p\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034 (-0.026, 0.095)\u003c/p\u003e \u003cp\u003e0.26941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.266 (0.224, 0.308)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.265 (0.223, 0.307)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086 (0.000, 0.172)\u003c/p\u003e \u003cp\u003e0.04965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 1: No covariates were adjusted; Model 2: Adjusted for gender, age and race; Model 3: Adjusted for all variables: gender, age, race, education level, PIR, marital status, waist, alcohol consumption status, hypertension status, diabetes status, liver condition, congestive heart failure, cancer or malignancy, sleep disorders, HDL, LDL, insulin, consideration for weight and attempt to lose weight.\u003c/p\u003e \u003cp\u003eModel 1 sample size: 10324; Model 2 sample size: 10324; Model 3 sample size: 10324\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of nonlinear associations\u003c/h2\u003e \u003cp\u003eGiven that prior multivariate analysis revealed a non-linear relationship between the baseline TyG-related index and ED, we utilized a smooth curve fitting technique (penalized spline method) to delve deeper into this correlation which were showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Saturation effect analysis of TyG, TyG-BMI and TyG-WC on ED were demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In model 1, after adjusting for age, gender, race, BMI, alcohol consumption, education level, hypertension, and other covariates, the morbidity of ED rose by 0.002 (95% Cl\u0026thinsp;=\u0026thinsp;0.001, 0.003; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with each unit increase in the TyG-BMI index respectively. In model 2, the inflection point was determined as 171.211 (P values for log-likelihood ratio\u0026thinsp;=\u0026thinsp;0.039). the baseline TyG-BMI index was significantly and positively associated with the ED when TyG-BMI index exceeded 171.211 (β\u0026thinsp;=\u0026thinsp;0.002; 95% CI\u0026thinsp;=\u0026thinsp;0.001, 0.003), while TyG-BMI index was negatively associated with ED when TyG-BMI index was above 171.211 (β = -0.003; 95% CI = -0.006, -0.000).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect analysis of TyG-related index on ED\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% Cl) \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.028 (0.002,0.054) 0.0332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u0026thinsp;\u0026lt;\u0026thinsp;8.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.011 (-0.056, 0.078) 0.7493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u0026thinsp;\u0026gt;\u0026thinsp;8.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.034 (0.001, 0.067) 0.0422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003efor Log-likelihood ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002 (0.001, 0.003)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI index\u0026thinsp;\u0026lt;\u0026thinsp;171.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.003 (-0.006, -0.000) 0.0276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI index\u0026thinsp;\u0026gt;\u0026thinsp;171.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002 (0.001, 0.003)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003efor Log-likelihood ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000) 0.1503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e589.442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC index\u0026thinsp;\u0026lt;\u0026thinsp;589.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002 (-0.004, -0.001) 0.0014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC index\u0026thinsp;\u0026gt;\u0026thinsp;589.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000) 0.3703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003efor Log-likelihood ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003eThe study conducted an in-depth examination of the intricate relationships between the TyG-related index and periodontitis through subgroup analyses accounting for gender, race, marital status, educational level, alcohol consumption, diabetes, sleep trouble, hypertension, congestive heart failure, cancer or malignancy, and liver condition. The results of subgroup analyses were listed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. A noteworthy correlation between the TyG and TyG-BMI index and ED was identified among participants suffering from hypertension.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analysis between TyG-related index and ED\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95%CI) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ (95%CI) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction **\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ (95%CI) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.025 (-0.012, 0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025 (-0.012, 0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.032 (-0.004, 0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032 (-0.004, 0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.042 (-0.018, 0.102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042 (-0.018, 0.102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.026 (-0.108, 0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.026 (-0.108, 0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.001, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.051 (-0.108, 0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051 (-0.108, 0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001 (-0.057, 0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001 (-0.057, 0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019 (-0.056, 0.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019 (-0.056, 0.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.001 (-0.001, -0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014 (-0.023, 0.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014 (-0.023, 0.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, -0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/divorced or separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.031 (-0.009, 0.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031 (-0.009, 0.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.070 (-0.007, 0.148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.070 (-0.007, 0.148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.000 (-0.000,0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.055 (0.003, 0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055 (0.003, 0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.000 (-0.000,0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.022 (-0.033, 0.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022 (-0.033, 0.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.015 (-0.021, 0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015 (-0.021, 0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, -0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.046 (-0.015, 0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046 (-0.015, 0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.021 (-0.017, 0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021 (-0.017, 0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013 (-0.033, 0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013 (-0.033, 0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.093 (0.024, 0.162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.093 (0.024, 0.162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.000 (-0.000, 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.018 (-0.011, 0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018(-0.011, 0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, -0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorderline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004 (-0.184, 0.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004 (-0.184, 0.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.000 (-0.001, 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.068 (0.021, 0.115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068 (0.021, 0.115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013 (-0.017, 0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013 (-0.017, 0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, -0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep trouble\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033 (-0.017, 0.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.033 (-0.017, 0.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.027 (-0.002, 0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027 (-0.002, 0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.117 (-0.095, 0.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.117 (-0.095, 0.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.001 (-0.002, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026 (-0.000, 0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026 (-0.000, 0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.152 (0.021, 0.284)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.152 (0.021, 0.284)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.001 (-0.000, 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.024 (-0.003, 0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024 (-0.003, 0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, -0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer or malignancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.094 (-0.042, 0.230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.094 (-0.042, 0.230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.001, 0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026 (-0.000, 0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026 (-0.000, 0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-0.000 (-0.000, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003e TyG; \u003csup\u003e**\u003c/sup\u003e TyG-BMI; *** TyG-WC\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity and specificity analysis\u003c/h2\u003e \u003cp\u003eROC analysis was conducted to evaluate the prognostic value of TyG-related index for ED. The results of the ROC curves were shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The areas under the curve (AUC) of TyG index, TyG-BMI index and TyG-WC index in predicting ED were 0.550 (95%CI: 0.524, 0.577), 0.587 (95%CI: 0.559, 0.616) and 0.582 (95%CI: 0.555, 0.610), respectively. Since the AUC of TyG-BMI was the largest, its value of predicting ED seemed greater than TyG and TyG-WC index. (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel predictions of association between TyG-related index and ED\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCutoff (Sensitivity, Specificity)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.050 (0.287, 0.798)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.550 (0.524, 0.577)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.046 (0.496, 0.661)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.587 (0.559, 0.616)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.044 (0.571, 0.569)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.582 (0.555, 0.610)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe predominant discoveries of this study revealed that TyG-related index was positively correlated with the occurrence of ED in the full adjustment model. Furthermore, an increased TyG-BMI index is associated with a heightened risk of ED. Subgroup analyses showed that strong relationship between TyG and TyG-BMI index and ED was more likely to be observed among participants with hypertension. Threshold effect analysis indicated that higher TyG-BMI was significantly associated with a higher risk of ED. Results of ROC analysis indicated that the TyG-related index had valid predictive value for ED.\u003c/p\u003e \u003cp\u003ePrevious studies have indicated that metabolic syndrome associated with insulin resistance (IR) is notably influential in in increasing the risk of ED [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The homeostasis model assessment of insulin resistance (HOMA-IR), is a traditional indicator of IR [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Nevertheless, considering the expense of insulin testing and the constraints of HOMA-IR, there is a need for a more effective, and user-friendly metric. The TyG index offers equivalent or superior results for assessing IR, owing to its ease of computation, sensitivity, and specificity [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have validated the effectiveness of WC and BMI in predicting obesity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A nationwide cohort study indicated that the combination of obesity indicators with the TyG index could more accurately predict the risk of metabolic syndrome than the use of these metrics alone [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, TyG-WC, and TyG-BMI demonstrated greater strength in predicting IR than TyG alone [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Consequently, the integration of obesity metrics with TyG might more precisely capture the relationship between IR and ED. Although the precise physiological processes remain unclear, several elements could account for this link.\u003c/p\u003e \u003cp\u003eFlint et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] deduced that the insulin response following a meal could serve as a significant signal for satiety, and that central nervous system IR in overweight individuals might be responsible for the diminished impact on appetite control. The homeostatic approach to appetite regulation involves feedback which is attributed to the inhibitory action of insulin. Gastrointestinal hormones, such as cholecystokinin, and glucagon-like peptide-1 initiate a series of neural and hormonal signals that operate both peripherally and centrally to facilitate satiation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These hormones collaborate with the appetite-stimulating hormone ghrelin to manage food intake in a cyclical manner. Insulin provides a steady influence on appetite by fine-tuning the intensity of the cyclical signals [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Peripheral neuroendocrine signals converge in the hypothalamus. Upon transmission to the arcuate nucleus through the nucleus tractus solitarius, these signals activate the orexigenic neurons that express agouti-related peptide (AgRP) and neuropeptide Y (NPY), as well as the anorexigenic pro-opiomelanocortin (POMC) neurons that release alpha-melanocyte-stimulating hormone [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The equilibrium in the release of these functionally opposing neuropeptides confers the satiation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The ghrelin hormone activates the orexigenic NPY/AgRP neurons in the arcuate nucleus, while glucose and insulin have an inhibitory effect on them. Conversely, the POMC/CART neurons are activated by insulin and glucose, but their activity is suppressed when NPY/AgRP neurons are stimulated [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile glucose and insulin regulate appetite together, there may be specific conditions where glucose homeostasis has a greater impact on eating behavior. The glucostatic theory of appetite, proposed by Mayer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], proposed that alterations in the utilization of glucose were crucial for encoding feelings of hunger, with \"metabolic hypoglycemia\" serving as a trigger for starting a meal. In support of this, some studies assessed the link among glycemic load, glycemic responses and appetite [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and suggest that lower glycemic loads and glycemic responses leads to reduced post-meal appetite. Hypoglycemia, common in those with diabetes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], is met with strong neuroendocrine reactions aimed at restoring euglycemia [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Hypoglycemia boosts the activity of hypothalamic NPY/AgRP neurons and diminishes that of POMC/CART neurons, leading to overeating [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond its peripheral effects, insulin also centrally modulates neuronal activity in brain areas associated with eating behavior, sensory processing, and reward [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. It is hypothesized that brain IR coincides with peripheral resistance in areas for appetite [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Vagal fibers that innervate the gastrointestinal tract are pivotal to the gut-brain axis since fibers facilitates communication between the hypothalamus and peripheral organs, such as the liver and adipose tissue [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, the expression of vagal gastrointestinal hormone receptors and sensitivity to peripheral stimuli might be diminished in individuals with obesity and diabetes [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which implies that an excessive fat, commonly observed in type 2 diabetes (T2D), could interfere with the vagal perception of peripheral appetite signals and their recognition in brain areas critical to ED. The insulin-reward pathways could be compromised in T2D, given that peripheral IR is linked to IR in brain areas that govern the rewarding aspects of food [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, hippocampal damage has been demonstrated to lead to ED, such as a diminished ability to sense hunger and fullness and a reduced mental representation of food [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis research contributes significant findings that highlight the positive associations between TyG-related index and ED. The study benefits from a substantial sample size and nationwide coverage. Additionally, the analysis controlled for various confounding factors associated with ED, which strengthens the reliability of the outcomes. However, there are limitations to acknowledge. First, the study limits the ability to determine causality between TyG-related index and ED. Second, it is difficult to completely eliminate the potential impact of unmeasured confounders. Lastly, the study's focus on the US population may restrict the applicability of the results to a global setting. Future studies should aim to include larger and more diverse samples to identify the most predictive TyG-related indices for ED and to establish universally applicable threshold values.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe research confirmed a significant association between the TyG-related index and ED in the U.S. population. This finding highlights the potential of TyG-related obesity indices for assessing the risk of ED and accentuates the importance of IR in the development of ED. Regular monitoring of the TyG-related index could enable clinicians to pinpoint individuals at a higher risk for ED, facilitating the application of targeted preventative measures. However, the precise molecular mechanisms that connect the TyG-related index with ED remain to be fully understood. Future studies should aim to explore the possible biological pathways that associate the TyG-related index with ED and to determine whether interventions that lower the TyG-related index could lead to a reduction in the prevalence of ED.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82370601).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.X. was responsible for the conception and writing. Y.D. contributed to making figures and tables. K.S. performed the investigation. Y.R. reviewed and edited this manuscript. All the authors reviewed the manuscript and agreed to publish it.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAvailability of data and material\u003c/h2\u003e \u003cp\u003eAll data for this study can be found on the NHANES website.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHimmerich H, Keeler JL, Davies HL, Tessema SA, Treasure J. The evolving profile of eating disorders and their treatment in a changing and globalised world. Lancet. 2024;403:2671\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(24)00874-2\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(24)00874-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantomauro DF, Melen S, Mitchison D, Vos T, Whiteford H, Ferrari AJ. The hidden burden of eating disorders: an extension of estimates from the Global Burden of Disease Study 2019. Lancet Psychiatry. 2021;8:320\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s2215-0366(21)00040-7\u003c/span\u003e\u003cspan address=\"10.1016/s2215-0366(21)00040-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J, Liu J, Li S, Ma H, Wang Y. Trends in the prevalence and disability-adjusted life years of eating disorders from 1990 to 2017: results from the Global Burden of Disease Study 2017. Epidemiol Psychiatr Sci. 2020;29:e191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/s2045796020001055\u003c/span\u003e\u003cspan address=\"10.1017/s2045796020001055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal regional. and national burden of 12 mental disorders in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet Psychiatry\u003c/em\u003e 2022, 9:137\u0026ndash;150.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s2215-0366(21)00395-3\u003c/span\u003e\u003cspan address=\"10.1016/s2215-0366(21)00395-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCulbert KM, Racine SE, Klump KL. Research Review: What we have learned about the causes of eating disorders - a synthesis of sociocultural, psychological, and biological research. J Child Psychol Psychiatry. 2015;56:1141\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jcpp.12441\u003c/span\u003e\u003cspan address=\"10.1111/jcpp.12441\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIlyas A, H\u0026uuml;bel C, Stahl D, Stadler M, Ismail K, Breen G, Treasure J, Kan C. The metabolic underpinning of eating disorders: A systematic review and meta-analysis of insulin sensitivity. Mol Cell Endocrinol. 2019;497:110307. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.mce.2018.10.005\u003c/span\u003e\u003cspan address=\"10.1016/j.mce.2018.10.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar A, Alam S, Bano S, Prakash R, Jain V. Association of eating disorders with glycaemic control and insulin resistance in patients of type 2 diabetes mellitus. Int J Biochem Mol Biol. 2023;14:40\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee J, Kim B, Kim W, Ahn C, Choi HY, Kim JG, Kim J, Shin H, Kang JG, Moon S. Lipid indices as simple and clinically useful surrogate markers for insulin resistance in the U.S. population. Sci Rep. 2021;11:2366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-82053-2\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-82053-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSon DH, Lee HS, Lee YJ, Lee JH, Han JH. Comparison of triglyceride-glucose index and HOMA-IR for predicting prevalence and incidence of metabolic syndrome. Nutr Metab Cardiovasc Dis. 2022;32:596\u0026ndash;604. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.numecd.2021.11.017\u003c/span\u003e\u003cspan address=\"10.1016/j.numecd.2021.11.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoosaie F, Abhari SMF, Deravi N, Behnagh AK, Esteghamati S, Firouzabadi FD, Rabizadeh S, Nakhjavani M, Esteghamati A. Waist-To-Height Ratio Is a More Accurate Tool for Predicting Hypertension Than Waist-To-Hip Circumference and BMI in Patients With Type 2 Diabetes: A Prospective Study. Front Public Health. 2021;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.DOI:10.3389/fpubh.2021.726288\u003c/span\u003e\u003cspan address=\"10.DOI:10.3389/fpubh.2021.726288\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatzmarzyk PT, Hu G, Cefalu WT, Mire E, Bouchard C. The Importance of Waist Circumference and BMI for Mortality Risk in Diabetic Adults. Diabetes Care. 2013;36:3128\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc13-0219\u003c/span\u003e\u003cspan address=\"10.2337/dc13-0219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Sun M, Yang Y, Yao N, Yan S, Wang L, Hu W, Guo R, Wang Y, Li B. Predictive Effect of Triglyceride Glucose-Related Parameters, Obesity Indices, and Lipid Ratios for Diabetes in a Chinese Population: A Prospective Cohort Study. Front Endocrinol (Lausanne). 2022;13:862919. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2022.862919\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2022.862919\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaimi TH, Dele-Ojo BF, Dada SA, Fadare JO, Ajayi DD, Ajayi EA, Ajayi OA. Triglyceride-Glucose Index and Related Parameters Predicted Metabolic Syndrome in Nigerians. Metab Syndr Relat Disord. 2021;19:76\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/met.2020.0092\u003c/span\u003e\u003cspan address=\"10.1089/met.2020.0092\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan S, Wang D, Jia Y. Comparison of insulin resistance-associated parameters in US adults: a cross-sectional study. Horm (Athens). 2023;22:331\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s42000-023-00448-4\u003c/span\u003e\u003cspan address=\"10.1007/s42000-023-00448-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhimire S, Baral BK, Feng D, Sy FS, Rodriguez R. Is selenium intake associated with the presence of depressive symptoms among US adults? Findings from National Health and Nutrition Examination Survey (NHANES) 2011\u0026ndash;2014. Nutrition. 2019;62:169\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.nut.2018.12.007\u003c/span\u003e\u003cspan address=\"10.1016/j.nut.2018.12.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerthoud HR. Mind versus metabolism in the control of food intake and energy balance. Physiol Behav. 2004;81:781\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.physbeh.2004.04.034\u003c/span\u003e\u003cspan address=\"10.1016/j.physbeh.2004.04.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTahapary DL, Pratisthita LB, Fitri NA, Marcella C, Wafa S, Kurniawan F, Rizka A, Tarigan TJE, Harbuwono DS, Purnamasari D, Soewondo P. Challenges in the diagnosis of insulin resistance: Focusing on the role of HOMA-IR and Tryglyceride/glucose index. Diabetes Metab Syndr. 2022;16(102581). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.dsx.2022.102581\u003c/span\u003e\u003cspan address=\"10.1016/j.dsx.2022.102581\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuerrero-Romero F, Villalobos-Molina R, Jim\u0026eacute;nez-Flores JR, Simental-Mendia LE, M\u0026eacute;ndez-Cruz R, Murgu\u0026iacute;a-Romero M, Rodr\u0026iacute;guez-Mor\u0026aacute;n M. Fasting Triglycerides and Glucose Index as a Diagnostic Test for Insulin Resistance in Young Adults. Arch Med Res. 2016;47:382\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.arcmed.2016.08.012\u003c/span\u003e\u003cspan address=\"10.1016/j.arcmed.2016.08.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimental-Mend\u0026iacute;a LE, Rodr\u0026iacute;guez-Mor\u0026aacute;n M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6:299\u0026ndash;304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/met.2008.0034\u003c/span\u003e\u003cspan address=\"10.1089/met.2008.0034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Shi J, Peng Y, Fang Q, Mu Q, Gu W, Hong J, Zhang Y, Wang W. Stronger association of triglyceride glucose index than the HOMA-IR with arterial stiffness in patients with type 2 diabetes: a real-world single-centre study. Cardiovasc Diabetol. 2021;20(82). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-021-01274-x\u003c/span\u003e\u003cspan address=\"10.1186/s12933-021-01274-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark Y, Kim NH, Kwon TY, Kim SG. A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Sci Rep. 2018;8:16753. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-018-35073-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-35073-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGui J, Li Y, Liu H, Guo LL, Li J, Lei Y, Li X, Sun L, Yang L, Yuan T, et al. Obesity- and lipid-related indices as a predictor of obesity metabolic syndrome in a national cohort study. Front Public Health. 2023;11:1073824. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2023.1073824\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2023.1073824\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim J, Kim J, Koo SH, Kwon GC. Comparison of triglyceride glucose index, and related parameters to predict insulin resistance in Korean adults: An analysis of the 2007\u0026ndash;2010 Korean National Health and Nutrition Examination Survey. PLoS ONE. 2019;14:e0212963. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0212963\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0212963\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlint A, Gregersen NT, Gluud LL, M\u0026oslash;ller BK, Raben A, Tetens I, Verdich C, Astrup A. Associations between postprandial insulin and blood glucose responses, appetite sensations and energy intake in normal weight and overweight individuals: a meta-analysis of test meal studies. Br J Nutr. 2007;98:17\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/s000711450768297x\u003c/span\u003e\u003cspan address=\"10.1017/s000711450768297x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwartz MW, Seeley RJ, Zeltser LM, Drewnowski A, Ravussin E, Redman LM, Leibel RL. Obesity Pathogenesis: An Endocrine Society Scientific Statement. Endocr Rev. 2017;38:267\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/er.2017-00111\u003c/span\u003e\u003cspan address=\"10.1210/er.2017-00111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorton GJ, Cummings DE, Baskin DG, Barsh GS, Schwartz MW. Central nervous system control of food intake and body weight. Nature. 2006;443:289\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature05026\u003c/span\u003e\u003cspan address=\"10.1038/nature05026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Gambero AJ, Mart\u0026iacute;nez F, Salazar K, Cifuentes M, Nualart F. Brain Glucose-Sensing Mechanism and Energy Homeostasis. Mol Neurobiol. 2019;56:769\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12035-018-1099-4\u003c/span\u003e\u003cspan address=\"10.1007/s12035-018-1099-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMayer J. Glucostatic mechanism of regulation of food intake. N Engl J Med. 1953;249:13\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/nejm195307022490104\u003c/span\u003e\u003cspan address=\"10.1056/nejm195307022490104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonzalez-Anton C, Lopez-Millan B, Rico MC, Sanchez-Rodriguez E, Ruiz-Lopez MD, Gil A, Mesa MD. An enriched, cereal-based bread affects appetite ratings and glycemic, insulinemic, and gastrointestinal hormone responses in healthy adults in a randomized, controlled trial. J Nutr. 2015;145:231\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3945/jn.114.200386\u003c/span\u003e\u003cspan address=\"10.3945/jn.114.200386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeese GP, Wang J, Broomhall J, Kelly P, Marsden A, Morrison W, Frier BM, Morris AD. Frequency of severe hypoglycemia requiring emergency treatment in type 1 and type 2 diabetes: a population-based study of health service resource use. Diabetes Care. 2003;26:1176\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/diacare.26.4.1176\u003c/span\u003e\u003cspan address=\"10.2337/diacare.26.4.1176\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatts AG, Donovan CM. Sweet talk in the brain: glucosensing, neural networks, and hypoglycemic counterregulation. Front Neuroendocrinol. 2010;31:32\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.yfrne.2009.10.006\u003c/span\u003e\u003cspan address=\"10.1016/j.yfrne.2009.10.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSindelar DK, Ste Marie L, Miura GI, Palmiter RD, McMinn JE, Morton GJ, Schwartz MW. Neuropeptide Y is required for hyperphagic feeding in response to neuroglucopenia. Endocrinology. 2004;145:3363\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/en.2003-1727\u003c/span\u003e\u003cspan address=\"10.1210/en.2003-1727\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArnold SE, Arvanitakis Z, Macauley-Rambach SL, Koenig AM, Wang HY, Ahima RS, Craft S, Gandy S, Buettner C, Stoeckel LE, et al. Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums. Nat Rev Neurol. 2018;14:168\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrneurol.2017.185\u003c/span\u003e\u003cspan address=\"10.1038/nrneurol.2017.185\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnthony K, Reed LJ, Dunn JT, Bingham E, Hopkins D, Marsden PK, Amiel SA. Attenuation of insulin-evoked responses in brain networks controlling appetite and reward in insulin resistance: the cerebral basis for impaired control of food intake in metabolic syndrome? \u003cem\u003eDiabetes\u003c/em\u003e 2006, 55:2986\u0026ndash;92.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/db06-0376\u003c/span\u003e\u003cspan address=\"10.2337/db06-0376\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCammisotto P, Bendayan M. A review on gastric leptin: the exocrine secretion of a gastric hormone. Anat Cell Biol. 2012;45:1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5115/acb.2012.45.1.1\u003c/span\u003e\u003cspan address=\"10.5115/acb.2012.45.1.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Lartigue G. Role of the vagus nerve in the development and treatment of diet-induced obesity. J Physiol. 2016;594:5791\u0026ndash;815. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1113/jp271538\u003c/span\u003e\u003cspan address=\"10.1113/jp271538\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevenson RJ, Francis HM. The hippocampus and the regulation of human food intake. Psychol Bull. 2017;143:1011\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/bul0000109\u003c/span\u003e\u003cspan address=\"10.1037/bul0000109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"eating disorders, TyG-associated obesity index, TyG index, NHANES, insulin resistance","lastPublishedDoi":"10.21203/rs.3.rs-5317726/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5317726/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis research aimed to determine the possible links between obesity measures related to the triglyceride glucose (TyG) index and the prevalence of eating disorders (ED) for the United States residents.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis observational investigation analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2005\u0026ndash;2018. It assessed the relationship of the TyG index, TyG combined with waist circumference (TyG-WC), or TyG combined with body mass index (TyG-BMI) with ED. The analysis employed a multivariable regression model, stratified analyses, and a ROC curve assessment.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis research included a total of 10,324 adults. In the comprehensive analysis model, the TyG, TyG-BMI, along with TyG-WC all had a significant positive correlation with ED. The adjusted graphical representations revealed a rising trend in the association of TyG-BMI index with ED. Subgroup analyses indicated that individuals with hypertension exhibited even stronger positive associations between these indices and ED. The areas under the curve (AUC) values indicates the value for TyG-related indicators in predicting ED.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe research uncovered a significant and enduring connection between obesity measures related to the TyG-related index and ED, indicating a robust association of increased insulin resistance with the probability of ED among the U.S. population.\u003c/p\u003e","manuscriptTitle":"Associations between different triglyceride glucose index-related obesity indices and eating disorders: results from NHANES 2005–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-28 05:09:45","doi":"10.21203/rs.3.rs-5317726/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9a9a27b-768b-41ba-bf00-0beaf70d311c","owner":[],"postedDate":"October 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-28T07:54:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-28 05:09:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5317726","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5317726","identity":"rs-5317726","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}