(Un)Healthy Neuroticism and Eating Behaviors: A Study of the Nathan Kline Institute for Psychiatric Research - Rockland Sample

(Un)Healthy Neuroticism and Eating Behaviors: A Study of the Nathan Kline Institute for Psychiatric Research - Rockland Sample | 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 Article (Un)Healthy Neuroticism and Eating Behaviors: A Study of the Nathan Kline Institute for Psychiatric Research - Rockland Sample Isabel Arend, Kenneth Yuen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4789671/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Trait neuroticism is associated with maladaptive behaviors and mortality risk. However, high levels of both neuroticism and conscientiousness (i.e., healthy neuroticism) are associated with various positive health behaviors. Eating behavior is a modifiable risk factor for obesity and metabolic diseases. This study investigates the cross-sectional and longitudinal associations between healthy neuroticism and eating behaviors. Data from the Nathan Kline Institute for Psychiatric Research - Rockland Sample included 712 adults with complete assessments of personality, eating behaviors, and clinical metabolic markers. Linear and mixed linear regression models examined cross-sectional and longitudinal associations of eating behaviors and personality, adjusting for socio-demographics (age, sex, education), sleep quality, body mass index (BMI), and metabolic markers. Healthy neuroticism predicted disinhibition and hunger dimensions of eating behavior cross-sectionally, a result that withstood the inclusion of disease burden and clinical metabolic markers. Longitudinally, healthy neuroticism didn't predict changes in eating behavior. Greater conscientiousness scores were associated with increase in restraint, and greater hunger scores with increase neuroticism over time. These findings provide the first evidence that neuroticism is associated with less maladaptive eating behavior tendencies when modulated by conscientiousness. The implications of these associations for potential bidirectional relationships between eating behavior, metabolic health, and personality are discussed. Biological sciences/Psychology Health sciences/Risk factors personality healthy neuroticism eating behavior metabolism Figures Figure 1 Introduction The Big Five trait model of personality 1 has revealed psychological phenotypes that may predispose individuals to mental and physical health conditions 2 . Trait neuroticism, defined as individuals' tendency to experience negative emotions, has been found to be associated with different psychological and health outcomes 3 – 6 , including increased risk of mortality 7 . Individuals high in neuroticism have been found to be more likely to engage in unhealthy behaviors such as smoking and drinking 6 , 8 , sedentary behavior 9 , and maladaptive eating patterns 10 – 12 . However, discrepancies have continuously emerged from the literature, with studies showing either null or positive associations between neuroticism and health outcomes 8 , 13 – 16 , including lower mortality 17 . The 'healthy neuroticism' concept describes the circumstances under which neuroticism can lead to positive health outcomes when associated with high levels of trait conscientiousness 18 , 19 . Trait conscientiousness reflects an individual's tendency towards industriousness, self-discipline, and goal-oriented behavior, and it is often associated with positive health behaviors and outcomes such as adherence to a healthy diet, physical activity, and prescribed treatment 20 . Healthy neuroticism resulting from the neuroticism by conscientiousness interaction describes that certain psychological features of anxiety and vigilance could lead to self-care and preventive behavioral practices when associated with optimum levels of conscientiousness. Trait neuroticism could serve as a driving force that enables individuals to act in a beneficial manner concerning health. Healthy neuroticism is associated with reduced levels of inflammatory biomarkers 21 , less smoking following the onset of disease 22 , and less mortality in women 23 . Despite some findings revealing weak or no evidence of healthy neuroticism in different health outcomes 24 , a recent large-scale coordinated data analysis of 15 longitudinal studies (N = 54.000) reported healthy neuroticism to be associated with reduced smoking and physical activity 25 , supporting the role of this concept in health behaviors in the general population. Identifying psychological factors that prompt individuals to engage in health behaviors is crucial in the context of overweight and obesity, which are global health concerns, with alarming growing numbers in the Western world 26 . Preventing obesity and related metabolic outcomes involve long-term adherence to healthy and physical activity. Engagement in healthy lifestyle behaviors is highly associated with an individual's personality traits. Trait neuroticism is associated with overweight and obesity in different populations and age groups 10 , 27 – 30 , with maladaptive eating behaviors 12 , 31 , 32 , and unhealthy food choices 11 . Self-reporting eating behaviors are reliable indicators of eating tendencies 33 . The Three Factor Eating Questionnaire (TFEQ) 34 provides three dimensions of eating behavior: disinhibition (loss of control over eating), hunger (subjective feelings of hunger and food cravings), and restraint (control over food intake to manage weight). Similar self-report instruments provide comparable eating behavior tendencies, such as the shorter version of the TFEQ 35 , and the Dutch Eating Behavior Questionnaire 35 . Studies show that disinhibition and hunger predict body weight as measured by BMI in various populations 36 – 41 , including individuals with obesity, and metabolic conditions such as T2D 42 , 43 . Moreover, disinhibition and hunger dimensions are found to distinguish objective food choices in the general population 12 , 31 , 44 , and with greater energy and saturated fat intake 36 , 37 , 45 . Disinhibition and hunger positively and significantly correlate with neuroticism in different populations 10 , 31 . Eating restraint dimension is associated with greater consumption of fruits and vegetables 46 , and a reliable predictor of successful weight loss 47 , weight loss maintenance 48 , and positively correlated with conscientiousness 10 . A recent meta-analysis examining eating behavior patterns as predictors of objectively measured energy intake and long-term energy balance revealed that eating behaviors constitute a valuable phenotypic marker for food overconsumption and obesity 45 . Despite its importance in metabolic health, the association between healthy neuroticism and (mal)adaptive eating behaviors have not been studied. This study aims to examine: 1) whether neuroticism, conscientiousness, and their interaction predict eating behavior patterns, specifically whether neuroticism is linked with lower disinhibition and hunger and with higher eating restraint (self-control over eating); 2) whether the association persists beyond cardiovascular, metabolic health, and disease burden; and 3) whether neuroticism predicts eating behavior over time. To reach this goal, we capitalize on the Nathan Kline Institute for Psychiatric Research - Rockland Sample data (NKI-RS), which constitutes a community sample of more than 1300 healthy individuals at baseline, presented with longitudinal assessments of psychological, psychiatric, and metabolic data 49 . Method NKI Participants The Nathan Kline Rockland Sample includes participants from Rockland County, a population of 311,687 located 24 km northwest of New York City 49 . The racial demographics of Rockland County are 72.5% White, 17.4% African American, 6% Asian, 1.2% Native American, 0.34% Native Hawaiian, 2.5% Other Race. This resembles those of the United States Census (U.S. Census Bureau, 2009). Participants recruitment was mainly through flyers posted at schools, shopping malls, community centers, and other locations within Rockland County. We examined data from a subset of the adult participants ( N = 712), ages 21 to 75 years, who had complete metabolic data and underwent semi-structured diagnostic psychiatric interviews, including the personality and eating behavior questionnaire administered by the NKI research team. The subset included 247 males and 465 females (see Table 1 ). All methods were carried out in accordance with the Institutional Review Board (IRB) at the Nathan Kline Institute. Approval was obtained for this project at the Nathan Kline Institute IRB (Phase I #226781 and Phase II #239708) and at Montclair State University (Phase I #000983A and Phase II #000983B). Written informed consent was obtained for all study participants. Questionnaires Eating behaviors We used the Three Factor Eating Questionnaire (TFEQ) 50 , a validated 51-item instrument designed to measure three dimensions of eating behavior: disinhibition, which reflects the tendency to lose control over eating in the presence of palatable foods or emotional stress; hunger which reflects subjective feelings of hunger and food cravings; and restraint which reflects conscious control or restriction of food intake to control body weight. The questionnaire provides 21 questions for restraint, 16 for disinhibition, and 14 for hunger score. The instrument contains 36 yes/no items and 15 on a 1–4 response scale. Higher scores denote higher levels of disinhibited eating, hunger, and restrained eating. Personality assessment The NEO-ffi-3 Personality Inventory– constitutes a widely used instrument that assesses individuals for five domains of personality openness 1 : openness, extraversion, conscientiousness, agreeableness, and neuroticism. The present research focused on the main effects and interaction of neuroticism and conscientiousness 24 , 51 . Subjective sleep The Pittsburgh Sleep Quality Index (PSQI) measures sleep quality, disturbances, and typical sleeping habits retrospectively over the previous month 52 . The PSQI generates an overall sleep quality score (higher scores indicate poorer sleep quality) from seven component scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The present study used overall sleep quality score (0 = better; 3 = worse). Disease burden We built upon previous studies that examined disease status and personality traits 24 , 51 . Data were obtained from the Medical History Questionnaire-Adults provided in the NKI-RS database. MHQ includes the participant's self-report on several illnesses and health conditions (answered with yes/no). NKI-RS medical history data collection involves cross-referencing check with medication forms to ensure the accuracy of self-reports. The included diseases are stroke, head injury, heart attack, heart condition (e.g., valve disease, coronary disease), lung disease (e.g., bronchitis, emphysema), cancer, type 2 diabetes, hyperthyroidism, hypothyroidism, arthritis. The total number of conditions was calculated, where higher scores representing higher disease burden. Data analysis The study examines associations between personality traits, eating behavior, and metabolic markers through cross-sectional and longitudinal analyses, investigating potential causal relationships and reverse causality. Cross-sectional analysis Linear regression models taking eating behavior as an outcome were estimated using ordinary least squares. Model 1 examined associations between eating behavior and personality traits while controlling for sociodemographic covariates (i.e., age, sex, and education), sleep quality, and BMI, which have been previously associated with eating behaviors 10 , 39 , 53 , 54 . Model 2 examined the neuroticism by conscientiousness interaction as a predictor of eating behavior above and beyond each personality's main effects, sociodemographic covariates, sleep quality, and BMI. To examine whether the neuroticism by conscientiousness interaction could be driven by disease burden or an individual's metabolic health 19 , 22 , 23 , Model 3 included a measure of disease burden, and Model 4 included metabolic markers (i.e., waist circumference, glucose, HLD, triglycerides, systolic and diastolic blood pressure). For all analyses, continuous covariates were standardized. Sex was coded as 0 = Women, 1 = Men. Data were analyzed using the R program (R version 4.1.2 and R Studio version 2021.09.1 + 172). Multiple regression models were performed using the "lm" function from package "stats." Assumption checks were performed using the R "performance" package 55 . Traits associations and eating behaviors We examined the impact of neuroticism and conscientiousness on eating behavior, focusing on congruity effects 56 . This involved investigating whether specific patterns of association between these traits predict eating behavior by parametrically comparing their congruity (i.e., both traits low or both high; the \(\:{a}_{1}\) coefficient) and incongruity (i.e., one trait low and the other high; the \(\:{a}_{3}\) coefficient) across different eating patterns (see Schönbrodt et al. (2018) for a review of these coefficients). This analysis was complemented by a Johnson-Neyman analysis aimed at examining the point of significance at which conscientiousness's influence on eating patterns was affected by levels of neuroticism. Longitudinal analysis Mixed models examined the longitudinal associations between personality traits and eating behavior using "lme" function in R. Models were adjusted for sociodemographic covariates, sleep quality, and BMI (see Table 3 ). Eating behavior dimensions were examined in separate models as outcome variables and neuroticism x conscientiousness x session interaction as predictors. Due to model convergence issues, the disinhibition dimension could not be analyzed longitudinally. We also explored whether baseline eating behavior scores were associated with personality changes over time (reverse causality). In separate models, eating behavior (i.e., disinhibition, hunger, and restraint) x session were predictors of longitudinal changes in neuroticism and conscientiousness. Results Description of the sample at baseline The study included 712 NKI participants, of whom 67% were females. The mean age of participants at baseline was 48 years ( SD = 15.81), and the mean BMI was 27.80 ( SD = 5.55), with 35% with healthy weight, 30% with overweight, and 35% with obesity (BMI > 30). Table 1 presents the full sample descriptive. Of note, we examine the five indicators of metabolic syndrome that are associated with increased risk of T2D and cardiovascular conditions 57 . Standard categorization for each indicator was performed based on the following values: Ethnicity-specific waist circumference < = 88 (F) and = 1.7 mmol/L; HDL < = 1. 29 (F) and = 130 mmHg; diastolic blood pressure > = 85 mmHg, or treatment of previously diagnosed hypertension; fasting plasma glucose > = 5.6 mmol/L or previously diagnosed T2D. The percentage of individuals presenting metabolic syndrome in our sample was 27% ( N = 192). The breakdown of individuals with each metabolic indicator was as follows: obesity = 30% ( N = 211), waist circumference = 41% ( N = 292), blood pressure = 28% ( N = 197), glucose = .07% ( N = 50), triglycerides = 15% ( N = 110), HDL = 16% ( N = 113). The distribution of reported diseases/health conditions was: none = 44% ( N = 312), 1 = 32% ( N = 229), 2 = 16% ( N = 113), 3 = 6% ( N = 42), 4 or more = 2% ( N = 16). Table 1 Characteristics of the participants and descriptive of baseline measures (N = 712). Mean Standard Deviation Range Age (years) 48.01 15.81 21–75 Sex (% female) 65 --- --- Years of Education 15.63 2.24 9–24 Race (%) - White - Black or African American - Other (American Indian, Asian, Pacific Islander) 78 13 9 --- --- --- --- --- --- BMI (kg/m 2 ) 27.80 5.55 18.5–65.65 Waist circumference (cm) Glucose Cholesterol (mmol/L) 91.39 92.54 5.74 14.83 22.18 1.21 61–140 50–368 0.10–12.35 HDL (mmol/L) Triglyceride (mmol/L) 1.61 1.22 0.47 0.99 0.70–3.7 0.08–16.68 Systolic blood pressure (mmHg) 120.80 16.86 78–219 Diastolic blood pressure (mmHg) 75.18 10.94 41–122 Disease burden 0.91 (0–4) 1.06 0–7 Metabolic Syndrome (% > 2 indicators) 27 ---- ---- Three Factor Eating Questionnaire Disinhibition 5.14 3.48 0–15 Hunger 4.22 3.27 0–16 Restraint 9.21 4.87 0–21 Personality traits Neuroticism 18.53 8.23 0–44 Conscientiousness 34.89 7.22 5–48 Associations of personality traits with baseline eating behaviors Table 3 displays the estimates of multiple regression models. Model 1 examined the association between eating behavior patterns and personality traits. Model 2 specifically examined neuroticism by conscientiousness interaction beyond their main effects. Model 3 and Model 4 examined whether disease burden and metabolic markers modify these associations. All models adjusted for sociodemographic variables (i.e., age, sex, and education), sleep quality, and BMI. Model 1 revealed neuroticism to reliably predict all eating behavior dimensions and conscientiousness to predict only restraint (see Table 3 ), so neuroticism scores were associated with an increase in maladaptive eating behavior, as shown for disinhibition restrain ( b = 0.33, p < 0.01) and hunger ( b = 0.26, p < 0.01). Conscientiousness was associated with increased eating restraint ( b = 0.14, p < 0.01). Table 2 Associations of personality traits and baseline eating behaviors. Model 1 Disinhibition Hunger Restraint Predictors Estimates Z value p Estimates Z value p Estimates Z value p Intercept -0.21 *** -3.77 < 0.01 0.04 0.76 0.45 -0.12 * -1.99 0.05 Sleep 0.01 0.33 0.74 0.06 1.70 0.09 -0.06 -1.73 0.08 BMI 0.40 *** 12.24 < 0.01 0.26 *** 7.50 < 0.01 0.08 * 2.10 0.04 Neurot 0.33 *** 8.85 < 0.01 0.26 *** 6.28 < 0.01 0.15 *** 3.56 < 0.01 Consci -0.03 -0.99 0.32 -0.01 -0.32 0.75 0.14 *** 3.43 < 0.01 R 2 / R 2 adj 0.299 / 0.292 0.176 / 0.168 0.096 / 0.087 Model 2 Disinhibition Hunger Restraint Predictors Estimates Z value p Estimates Z value p Estimates Z value p Intercept -0.23 *** -4.11 < 0.01 0.01 0.09 0.93 -0.11 -1.70 0.09 Sleep 0.01 0.38 0.71 0.06 1.78 0.07 -0.06 -1.75 0.08 BMI 0.40 *** 12.25 < 0.01 0.26 *** 7.53 < 0.01 0.08 * 2.11 0.04 Neurot 0.33 *** 8.66 < 0.01 0.25 *** 6.01 < 0.01 0.16 *** 3.65 < 0.01 Consci -0.02 -0.48 0.63 0.02 0.46 0.65 0.12 ** 3.04 < 0.01 N x C -0.06 * -2.07 0.04 -0.09 *** -3.31 < 0.01 0.04 1.27 0.20 R 2 / R 2 adj 0.303 / 0.295 0.188 / 0.179 0.098 / 0.088 Note. Models are adjusted for sociodemographic covariates (age, sex, education), overall sleep quality, and BMI. Neuroticism = Neurot; Conscientiousness = Consci; Overall Sleep Quality = Sleep; Body Mass Index = BMI. Model 2 revealed (see Table 2 ) that the neuroticism by conscientiousness interaction was significant for disinhibition ( b = -0.06, p = 0.040) and for hunger ( b = -0.09, p < 0.01) but not for restraint ( b = 0.04, p = 0.20). Comparing Model 1 and 2 revealed a significant difference for disinhibition, ( F (1,703) = 4.30, p = .04, \(\:{\Delta\:}{R}^{2}=0.004\) ), for hunger ( F (1, 703)=10.95, p = 0.009, \(\:{\Delta\:}{R}^{2}=0.02\) ), but not for restraint ( F (1,703) = 1.62, p = 0.20, \(\:{\Delta\:}{R}^{2}=0.002\) ). Model 2 revealed that the interaction pattern was unaffected by the inclusion of disease burden (see Supplemental Table 1), which was further confirmed by comparing Model 2 and Model 3 for disinhibition ( F (1, 703) = 0.27, p = 0.60, \(\:{\Delta\:}{R}^{2}=0.003\) ), for hunger, ( F (1, 703)=1.93, p = 0.17, \(\:{\Delta\:}{R}^{2}=0.002\) ), and for restraint, ( F (1, 703)=0.88, p = 0.35, \(\:{\Delta\:}{R}^{2}=0.001\) ). Model 4 revealed the neuroticism by conscientiousness interaction was unaffected by the inclusion of metabolic markers (see Supplemental Table 2), which was further confirmed by comparing Model 2 and 4 for disinhibition ( F (6, 697) = 0.64, p = 0.70, \(\:{\Delta\:}{R}^{2}=0.004\) ), for hunger ( F (6,697)=0.77, p = 0.60, \(\:{\Delta\:}{R}^{2}=0.005\) ), and for restraint ( F (6,697) = 0.84, p = 0.54, \(\:{\Delta\:}{R}^{2}=0.004\) ). To prevent unnecessary extension of the results section, we present congruity and point of significance analysis only for Model 2, which examines the neuroticism by conscientiousness interaction while controlling for sociodemographic covariates, sleep quality, and BMI. Neuroticism and conscientiousness similarity scores To clarify the relationship between neuroticism and conscientiousness, Fig. 1 depicts the levels of each eating behavior dimension across all values of both traits. Neuroticism and conscientiousness are plotted on the Y and X axes, respectively, while eating behavior is represented on the Z axis. We parametrically compared the effects of similarity between neuroticism and conscientiousness on eating behavior through a similarity analysis (i.e., both traits high vs. both traits low and one of the traits high and the other low), A significant increase in disinhibition ( \(\:{a}_{1}=0.31\) , t (703) = 4.99, p < 0.0001) and hunger scores ( \(\:{a}_{1}=0.26\) , t (703) = 3.93, p < 0.0001), were observed for congruity between traits, that is, both trait neuroticism and conscientiousness high versus both traits low. An increase in disinhibition ( \(\:{a}_{3}=0.34\) , t (703) =8.57, p < 0.0001; see Fig. 1 A) and in hunger scores ( \(\:{a}_{3}=0.23\) , t (703) = 5.24, p < 0.0001; see Fig. 1 B) was also observed for incongruency between traits, that is, one of the traits high and the other low. Overall, for both disinhibition and hunger, greater scores were observed when neuroticism was high and conscientiousness was low, reflecting the negative effect of neuroticism on maladaptive eating behavior. A Johnson-Neyman analysis of regions of significance (Fig. 1 D) showed that the association between conscientiousness and disinhibition was negative for values of neuroticism higher than 1.18 (i.e., high scores in conscientiousness are associated with less disinhibited eating for those with high scores in neuroticism). A similar analysis (Fig. 1 E) showed that the relationship between conscientiousness and hunger was positive for values of neuroticism lower than − 0.87 and negative (i.e., high scores in conscientiousness being associated with less hunger), for values of neuroticism higher than 1.10 (with no clear associations inside the interval of [-0.87, 1.10]). Therefore, for both disinhibition and hunger, the negative slope represents higher values of neuroticism (i.e., greater than 1.18 for disinhibition and greater than 1.10 for hunger) being associated with less maladaptive eating behavior when conscientiousness was high. This pattern of results is consistent with the proposal of healthy neuroticism. An increase in restraint was observed when both neuroticism and conscientiousness were high as opposed to when both were low ( \(\:{a}_{1}=0.28\) , t (703) = 3.99, p < 0.0001), but no increase was observed for incongruency between traits ( \(\:{a}_{3}=0.03\) , t (703) = 0.70, p = 0.73; see Fig. 1 C). Therefore, this finding also reflects the positive effect of healthy neuroticism on adaptive eating behavior. Associations of personality traits with longitudinal eating behaviors Mixed models adjusting for sociodemographic covariates, sleep quality, and BMI examined associations of baseline personality scores with longitudinal eating behavior dimensions. Models included the neuroticism x conscientiousness x session interaction, with session as a continuous variable, including two follow-up periods, concluding with a 3-year follow-up. Results are presented in Table 3 . A significant three-way interaction was found for hunger. However, contrasts aimed at examining slope differences for neuroticism and conscientiousness over time (session) were not significant (all p > 0.05). Therefore, while significant, the source of this three-way interaction could not be verified statistically (see Table 3 ). For restraint, there was a significant conscientiousness by session interaction, indicating an increase in restraint scores over time for high values of conscientiousness (see Table 3 ). Reverse causality was explored using Mixed models adjusting for sociodemographic covariates, sleep quality, and BMI. In separate models, we examined associations between eating behavior and longitudinal personality. The complete results are presented in Supplementary Materials (Supplemental Tables 3–5). The only significant finding was the associations of hunger with longitudinal neuroticism ( b = − 0.05, p = 0.044). That is, hunger scores at baseline were associated with an increase in neuroticism over time. No other interactions were significant. Table 3 Associations of personality traits with longitudinal eating behaviors. Hunger Restraint Predictors Estimates Z-value p Estimates Z-value p (Intercept) -0.05 -0.75 0.46 -0.08 -1.13 0.26 Sleep 0.08 ** 2.76 0.01 -0.01 -0.49 0.62 BMI 0.25 *** 7.39 < 0.01 0.07 1.83 0.07 Neurot 0.26 *** 4.35 < 0.01 0.11 1.87 0.06 Session 0.02 0.62 0.54 -0.01 -0.51 0.61 Consci 0.02 0.30 0.76 0.05 0.86 0.39 Neurot x Session -0.02 -0.64 0.52 0.03 0.81 0.42 Consci x Session 0.00 0.06 0.95 0.06 * 2.01 0.04 Neurot x Consci -0.16 *** -3.66 < 0.01 0.06 1.49 0.14 N x C x Session 0.07 * 2.38 0.02 -0.03 -1.35 0.18 Note. * p < 0.05 ** p < 0.01 *** p < 0.001 Discussion Previous studies have shown both positive and negative effects of neuroticism on health behaviors and risk of mortality. This study is the first to investigate whether healthy neuroticism, defined as the interaction between neuroticism and conscientiousness, reliably predicts eating behaviors beyond the main effects of each trait. The analysis controlled for sociodemographic covariates, sleep quality, BMI, disease burden, and indicators of metabolic health. Additionally, the present study explored longitudinal relationships between personality and eating behaviors for the first time. Cross-sectionally, our results show both the overall negative effects of neuroticism on eating behaviors and the positive effects of neuroticism derived from its association with conscientiousness, which is consistent with the proposal of healthy neuroticism 21 , 23 , 25 , 58 . Multiple regression models indicated that neuroticism by conscientiousness interaction reliably predicted disinhibition (loss of control over eating) and hunger (subjective feeling of hunger and cravings). Congruity analysis further revealed that similarity between traits (i.e., high scores in both traits) was associated with greater disinhibition, hunger, and restraint. The dissimilarity between traits (i.e., neuroticism high and conscientiousness low versus the opposite) was associated with high disinhibition and hunger but not with high restraint scores. Surface plots were utilized to illustrate the neuroticism by conscientiousness interaction pattern, effectively displaying eating behavior scores across the full range of neuroticism and conscientiousness values. Point of significance analysis identified specific neuroticism values (1.10 for disinhibition and 1.18 for hunger) where healthy neuroticism was evident. Taken together, our findings corroborate those of previous studies showing associations between neuroticism and maladaptive eating 12 , 27 , 31 , and between conscientiousness and eating restraint 59 – 62 . Our study provides new evidence that healthy neuroticism is associated with reduced scores in maladaptive eating behaviors such as disinhibition and hunger, while promoting increased self-regulatory eating restraint. Early studies suggested that eating restraint could precede disordered eating behaviors 41 , 63 , 64 . Progress in eating behavior research repeatedly demonstrates the regulatory nature of restraint through its association with various anthropometric measures, and also with high intake of healthy foods, weight control and maintenance 10 , 27 , 65 . Consistent with our hypothesis, our results indicate that high values in both traits significantly correlate with greater eating restraint compared to high conscientiousness alone. Our results were unaltered by including disease burden and markers of metabolic health. Previous studies have reported associations between neuroticism and disease burden 19 , 23 , 66 , no associations between personality traits with metabolic markers such as glucose levels and cholesterol 24 , but significant associations with inflammatory markers 21 . In the context of eating behaviors, one could argue that disease or worsening of metabolic status could drive changes in self-reported eating behavior. Individuals with high levels of neuroticism are indeed more attentive to physical symptoms and more prone to report them 23 . Our results do not support the idea that disease burden or metabolic status alone drove the relationship between personality and self-report eating behavior. However, it is important to note that individuals having one or more diseases ( N = 400) had significantly higher scores on eating restraint than those reporting no disease ( t (710) = -2.18, p = 0.030). In contrast, individuals with metabolic syndrome did not differ on any of the three eating behaviors dimensions. None of these effects were associated with personality, but future studies could explore this issue further by examining specific subclinical and clinical populations. The point of significance analysis reveals that the pattern of healthy neuroticism observed for disinhibition and hunger occurs for about less than a third of the sample. Thus, the effects of healthy neuroticism do not appear to span a wide range of neuroticism values, at least for the NKI sample. One potential reason for this pattern is the relatively high correlation ( r = − .40) between neuroticism and conscientiousness found in this study, compared to the correlation reported in a previous study on healthy neuroticism ( r = − .17) 21 . Consequently, a smaller proportion of individuals exhibited congruity between traits in our sample. Longitudinally, we did not find baseline personality traits to predict changes in disinhibition or hunger scores over time. Although a significant three-way interaction was found for hunger, the source of the interaction could not be statistically verified. We found that baseline levels of conscientiousness were associated with increased restraint over time. Concerning conscientiousness, our findings align with the notion that features like self-discipline and goal-oriented characteristic of conscientiousness may foster adaptive eating behavior 30 , 45 . Our analysis of reverse causality, examining how eating behavior relates to longitudinal personality changes, found that only hunger was associated with changes in neuroticism over time. Overall, our longitudinal findings do not definitively support a bidirectional association between personality and eating behaviors, but they highlight the need of further investigation in this area. Previous studies indicate that eating behaviors tend to remain relatively stable over time 65 , 67 , except for restraint which may increase following weight changes 65 , 67 . Other studies suggest that eating behaviors can change over time in response to life circumstances such as the social restrictions imposed by COVID-19 68 , life stress 69 , and efforts to maintain weight loss 45 , with individuals who successfully maintain weight loss showing increase in restraint over time. Our result reveal, for the first time, that healthy neuroticism is associated with eating behaviors independently of each personality trait, disease burden and clinical metabolic markers. These findings have significant implications for both clinical and preventive health psychology settings, contributing to the understanding and potential intervention strategies aimed at improving health outcomes through psychological pathways. Moving forward, it will be important to identify the mechanisms underlying these associations. Healthy neuroticism influences health outcomes through both behavioral and physiological pathways 21 , 23 , 25 including individuals' neurobiological response to stress 70 . Individuals who are high in neuroticism are more vulnerable to negative emotions, and are also more prone to enter stress-induced situations 19 . Oxidative stress from psychosocial stressors or overconsumption of glucose and lipids 71 – 73 triggers physiological responses affecting brain areas controlling not only eating behavior but other functions 74 . Our findings demonstrating that hunger predicts changes in neuroticism over time suggest the possibility that maladaptive eating behavior may precede (and not only result from) changes in affect, reward, expectancy and personality 74 . The role played by healthy neuroticism in these bidirectional effects requires further investigation. The strength of our study lies in leveraging the Nathan Kline Institute for Psychiatric Research - Rockland Sample data, a large community sample of healthy individuals with comprehensive anthropometric, psychiatric, health history, cardiovascular, and metabolic data. This allowed us to establish associations between personality and eating behaviors both cross-sectionally and longitudinally, while controlling for numerous potential confounders. Furthermore, our study innovates by examining healthy neuroticism through a combination of multiple regression models, congruity analysis, and region of significance analysis. Integrating these methods enabled us to systematically compare associations between personality and eating behavior across the entire distribution of values. However, our study is limited by a relatively short follow-up period. Given the slow progression of metabolic dysregulation and its potential behavioral effects, it's possible that the full impact of personality on eating behavior over time was not fully captured. Finally, our study utilized a self-report measure of eating behavior tendencies, which has been validated in various populations. Future research could enhance our understanding by incorporating assessments of food choices, albeit also self-report based, they would allow for the assessment of behaviors related to energy consumption and healthy eating choices. Declarations Declaration of competing interest: None. Author Contribution I.A.: Conceptualization, data curation, formal analyses, methodology, writing—original draft, writing—review & editing. K.Y.: data curation, visualization, writing—review & editing. Acknowledgement We thank Dr. Mattan Ben-Shahar for his valuable guidance and input on this report's methodology and statistical methods. Data Availability Data Availability: The data used in this study are publicly available and can be accessed through the Nathan Kline Institute for Psychiatric Research (NKI) Rockland Sample. The NKI dataset is hosted on the NIMH Data Archive (NDA) and can be accessed by authorized users. In order to request access to the data, please contact the corresponding author through [email protected] ; or NKI data representative through [email protected] References McCrae, R. R. & Costa, Jr., P. T. Brief Versions of the NEO-PI-3. Journal of Individual Differences 28, 116–128 (2007). Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A. & Goldberg, L. R. The Power of Personality: The Comparative Validity of Personality Traits, Socioeconomic Status, and Cognitive Ability for Predicting Important Life Outcomes. Perspect Psychol Sci 2, 313–345 (2007). Lahey, B. B. Public health significance of neuroticism. American Psychologist 64, 241–256 (2009). Manning, K. J., Chan, G. & Steffens, D. C. Neuroticism Traits Selectively Impact Long Term Illness Course and Cognitive Decline in Late-Life Depression. The American Journal of Geriatric Psychiatry 25, 220–229 (2017). Terracciano, A. et al. Neuroticism, Depressive Symptoms, and Serum BDNF. Psychosomatic Medicine 73, 638–642 (2011). Terracciano, A. & Costa, P. T. Smoking and the Five-Factor Model of personality. Addiction 99, 472–481 (2004). O’Súilleabháin, P. S. et al. Personality pathways to mortality: Interleukin-6 links conscientiousness to mortality risk. Brain, Behavior, and Immunity 93, 238–244 (2021). Turiano, N. A., Whiteman, S. D., Hampson, S. E., Roberts, B. W. & Mroczek, D. K. Personality and substance use in midlife: Conscientiousness as a moderator and the effects of trait change. Journal of Research in Personality 46, 295–305 (2012). Allen, M. S., Walter, E. E. & McDermott, M. S. Personality and sedentary behavior: A systematic review and meta-analysis. Health Psychology 36, 255–263 (2017). 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Personality and Individual Differences 77, 13–17 (2015). Llewellyn, C. & Wardle, J. Behavioral susceptibility to obesity: Gene–environment interplay in the development of weight. Physiology & Behavior 152, 494–501 (2015). Blandine De Lauzon et al. The Three-Factor Eating Questionnaire-R18 Is Able to Distinguish among Different Eating Patterns in a General Population. The Journal of Nutrition 134, 2372–2380 (2004). Van Strien, T., Frijters, J. E. R., Bergers, G. P. A. & Defares, P. B. The Dutch Eating Behavior Questionnaire (DEBQ) for assessment of restrained, emotional, and external eating behavior. Int. J. Eat. Disord. 5, 295–315 (1986). Cornelis, M. C. et al. Obesity susceptibility loci and uncontrolled eating, emotional eating and cognitive restraint behaviors in men and women. Obesity 22, (2014). French, S. A., Epstein, L. H., Jeffery, R. W., Blundell, J. E. & Wardle, J. Eating behavior dimensions. Associations with energy intake and body weight. A review. 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C. Association of Dysfunctional Eating Patterns and Metabolic Risk Factors for Cardiovascular Disease among Latinos. Journal of the Academy of Nutrition and Dietetics 118, 849–856 (2018). Tsartsapakis, I. & Zafeiroudi, A. Personality Traits and Healthy Eating Habits and Behaviors: A Narrative Review. JESR 14, 11 (2024). Dakin, C. et al. Do eating behavior traits predict energy intake and body mass index? A systematic review and meta-analysis. Obesity Reviews 24, e13515 (2023). Langlois, F. et al. Ghrelin levels are associated with hunger as measured by the Three-Factor Eating Questionnaire in healthy young adults. Physiology & Behavior 104, 373–377 (2011). Filiatrault, M.-L., Chaput, J.-P., Drapeau, V. & Tremblay, A. Eating behavior traits and sleep as determinants of weight loss in overweight and obese adults. Nutr & Diabetes 4, e140–e140 (2014). Papini, N. M. et al. Examination of three-factor eating questionnaire subscale scores on weight loss and weight loss maintenance in a clinical intervention. BMC Psychol 10, 101 (2022). Nooner, K. B. et al. The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry. Front. Neurosci. 6, (2012). Stunkard, A. J. & Messick, S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. Journal of Psychosomatic Research 29, 71–83 (1985). Stephan, Y., Sutin, A. R., Luchetti, M., Aschwanden, D. & Terracciano, A. Personality and aging-related immune phenotype. Psychoneuroendocrinology 153, 106113 (2023). Buysse, D. J., Reynolds III, C. F., Monk, T. H., Berman, S. R. & Kupfer, D. J. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry research 28, 193–213 (1989). Blumfield, M. L., Bei, B., Zimberg, I. Z. & Cain, S. W. Dietary disinhibition mediates the relationship between poor sleep quality and body weight. Appetite 120, 602–608 (2018). Konttinen, H., Van Strien, T., Männistö, S., Jousilahti, P. & Haukkala, A. Depression, emotional eating and long-term weight changes: a population-based prospective study. Int J Behav Nutr Phys Act 16, 28 (2019). Lüdecke, D., Ben-Shachar, M., Patil, I., Waggoner, P. & Makowski, D. performance: An R Package for Assessment, Comparison and Testing of Statistical Models. JOSS 6, 3139 (2021). Schönbrodt, F. D., Humberg, S. & Nestler, S. Testing Similarity Effects with Dyadic Response Surface Analysis. Eur J Pers 32, 627–641 (2018). Alberti, K. G. M. M., Zimmet, P. & Shaw, J. Metabolic syndrome—a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabetic Medicine 23, 469–480 (2006). Montgomery, M. K. Mitochondrial Dysfunction and Diabetes: Is Mitochondrial Transfer a Friend or Foe? Biology 8, 33 (2019). Bottera, A. R., Kambanis, P. E. & De Young, K. P. Persistence: A key factor in understanding the circumstances under which dietary restraint predicts restriction of caloric intake. Eat Behav 43, 101563 (2021). de Lauzon-Guillain, B. et al. Is restrained eating a risk factor for weight gain in a general population? 83, 132–138 (2006). Johnson, F., Pratt, M. & Wardle, J. Dietary restraint and self-regulation in eating behavior. Int J Obes 36, 665–674 (2012). McLean, J. A. & Barr, S. I. Cognitive dietary restraint is associated with eating behaviors, lifestyle practices, personality characteristics and menstrual irregularity in college women. Appetite 40, 185–192 (2003). Herman, C. P. & Mack, D. Restrained and unrestrained eating1. J Personality 43, 647–660 (1975). Polivy, J. & Herman, P. C. Dieting and binging: A causal analysis. American Psychologist 40, 193–201 (1985). Konttinen, H. et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4789671","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":348256293,"identity":"c413b1f7-f37c-4293-82f8-72913e035f30","order_by":0,"name":"Isabel Arend","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYLACHhDB3gAkDCxI0cJzAKRFghQtEglgkrBq/tmHjz14w3DH3uDm86sbfhRIMPC3dyfg1SJxLi3dcA7Ds8QNt3PKbvYAHSZx5uwG/Nac4TGT5mE4nGBwOyftBg9Qi4FELn4t8mf4v4G0AB12Ju3mH2K0GJzhYQNpYdxwg/3YbaJsMTzDZiY5x+BZ4swzOWy3ZQwkeAj6Re4M8zOJNxV37PmOH392880fGzn+9l4C3oc47wCDwgEeAxCThwjlYHCAQb6B/QGxqkfBKBgFo2CEAQBgvkgoyC6lWAAAAABJRU5ErkJggg==","orcid":"","institution":"Sheba Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Arend","suffix":""},{"id":348256294,"identity":"23e3ee6d-d853-4a3c-bcce-7bc93d50209e","order_by":1,"name":"Kenneth Yuen","email":"","orcid":"","institution":"Leibniz Institute for Resilience Research (LIR)","correspondingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"","lastName":"Yuen","suffix":""}],"badges":[],"createdAt":"2024-07-23 14:49:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4789671/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4789671/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-85750-4","type":"published","date":"2025-02-18T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63922800,"identity":"dd3af52a-5462-4d5b-951b-5eaf08231bfe","added_by":"auto","created_at":"2024-09-03 20:34:59","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":510744,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePanels A – C show the levels of each eating behavior dimension across all values of both traits. \u0026nbsp;Neuroticism and conscientiousness are plotted on the Y and X axes, respectively, while eating behavior is represented on the Z axis. Panels D and E show point of significance results with light blue identifying statistically significant (positive/negative) associations involving neuroticism and conscientiousness.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4789671/v1/d1257add5b40b2f05f2b76d7.jpeg"},{"id":77053793,"identity":"4e4a9ab1-87db-492e-b69c-bd55fdb7be75","added_by":"auto","created_at":"2025-02-24 16:30:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1520114,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4789671/v1/636319c4-6250-4e03-ada1-312940d1d2d8.pdf"},{"id":63922801,"identity":"28ce5886-dc64-412f-a712-eeb79b997213","added_by":"auto","created_at":"2024-09-03 20:34:59","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28837,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4789671/v1/1cedbdc9c01126bbdbac592c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"(Un)Healthy Neuroticism and Eating Behaviors: A Study of the Nathan Kline Institute for Psychiatric Research - Rockland Sample","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Big Five trait model of personality \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e has revealed psychological phenotypes that may predispose individuals to mental and physical health conditions \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Trait neuroticism, defined as individuals' tendency to experience negative emotions, has been found to be associated with different psychological and health outcomes \u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, including increased risk of mortality \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Individuals high in neuroticism have been found to be more likely to engage in unhealthy behaviors such as smoking and drinking \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, sedentary behavior \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and maladaptive eating patterns \u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, discrepancies have continuously emerged from the literature, with studies showing either null or positive associations between neuroticism and health outcomes \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, including lower mortality \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The 'healthy neuroticism' concept describes the circumstances under which neuroticism can lead to positive health outcomes when associated with high levels of trait conscientiousness \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Trait conscientiousness reflects an individual's tendency towards industriousness, self-discipline, and goal-oriented behavior, and it is often associated with positive health behaviors and outcomes such as adherence to a healthy diet, physical activity, and prescribed treatment \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Healthy neuroticism resulting from the neuroticism by conscientiousness interaction describes that certain psychological features of anxiety and vigilance could lead to self-care and preventive behavioral practices when associated with optimum levels of conscientiousness. Trait neuroticism could serve as a driving force that enables individuals to act in a beneficial manner concerning health. Healthy neuroticism is associated with reduced levels of inflammatory biomarkers \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, less smoking following the onset of disease \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and less mortality in women \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Despite some findings revealing weak or no evidence of healthy neuroticism in different health outcomes \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, a recent large-scale coordinated data analysis of 15 longitudinal studies (N\u0026thinsp;=\u0026thinsp;54.000) reported healthy neuroticism to be associated with reduced smoking and physical activity \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, supporting the role of this concept in health behaviors in the general population.\u003c/p\u003e \u003cp\u003eIdentifying psychological factors that prompt individuals to engage in health behaviors is crucial in the context of overweight and obesity, which are global health concerns, with alarming growing numbers in the Western world \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Preventing obesity and related metabolic outcomes involve long-term adherence to healthy and physical activity. Engagement in healthy lifestyle behaviors is highly associated with an individual's personality traits. Trait neuroticism is associated with overweight and obesity in different populations and age groups \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, with maladaptive eating behaviors \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, and unhealthy food choices \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSelf-reporting eating behaviors are reliable indicators of eating tendencies \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The Three Factor Eating Questionnaire (TFEQ) \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e provides three dimensions of eating behavior: disinhibition (loss of control over eating), hunger (subjective feelings of hunger and food cravings), and restraint (control over food intake to manage weight). Similar self-report instruments provide comparable eating behavior tendencies, such as the shorter version of the TFEQ \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, and the Dutch Eating Behavior Questionnaire \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Studies show that disinhibition and hunger predict body weight as measured by BMI in various populations \u003csup\u003e\u003cspan additionalcitationids=\"CR37 CR38 CR39 CR40\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, including individuals with obesity, and metabolic conditions such as T2D \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Moreover, disinhibition and hunger dimensions are found to distinguish objective food choices in the general population \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, and with greater energy and saturated fat intake \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDisinhibition and hunger positively and significantly correlate with neuroticism in different populations \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Eating restraint dimension is associated with greater consumption of fruits and vegetables \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, and a reliable predictor of successful weight loss \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, weight loss maintenance \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, and positively correlated with conscientiousness \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. A recent meta-analysis examining eating behavior patterns as predictors of objectively measured energy intake and long-term energy balance revealed that eating behaviors constitute a valuable phenotypic marker for food overconsumption and obesity \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite its importance in metabolic health, the association between healthy neuroticism and (mal)adaptive eating behaviors have not been studied. This study aims to examine: 1) whether neuroticism, conscientiousness, and their interaction predict eating behavior patterns, specifically whether neuroticism is linked with lower disinhibition and hunger and with higher eating restraint (self-control over eating); 2) whether the association persists beyond cardiovascular, metabolic health, and disease burden; and 3) whether neuroticism predicts eating behavior over time. To reach this goal, we capitalize on the Nathan Kline Institute for Psychiatric Research - Rockland Sample data (NKI-RS), which constitutes a community sample of more than 1300 healthy individuals at baseline, presented with longitudinal assessments of psychological, psychiatric, and metabolic data \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eNKI Participants\u003c/h2\u003e \u003cp\u003eThe Nathan Kline Rockland Sample includes participants from Rockland County, a population of 311,687 located 24 km northwest of New York City \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The racial demographics of Rockland County are 72.5% White, 17.4% African American, 6% Asian, 1.2% Native American, 0.34% Native Hawaiian, 2.5% Other Race. This resembles those of the United States Census (U.S. Census Bureau, 2009). Participants recruitment was mainly through flyers posted at schools, shopping malls, community centers, and other locations within Rockland County. We examined data from a subset of the adult participants (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;712), ages 21 to 75 years, who had complete metabolic data and underwent semi-structured diagnostic psychiatric interviews, including the personality and eating behavior questionnaire administered by the NKI research team. The subset included 247 males and 465 females (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All methods were carried out in accordance with the Institutional Review Board (IRB) at the Nathan Kline Institute. Approval was obtained for this project at the Nathan Kline Institute IRB (Phase I #226781 and Phase II #239708) and at Montclair State University (Phase I #000983A and Phase II #000983B). Written informed consent was obtained for all study participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eQuestionnaires\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eEating behaviors\u003c/h2\u003e \u003cp\u003eWe used the Three Factor Eating Questionnaire (TFEQ) \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, a validated 51-item instrument designed to measure three dimensions of eating behavior: disinhibition, which reflects the tendency to lose control over eating in the presence of palatable foods or emotional stress; hunger which reflects subjective feelings of hunger and food cravings; and restraint which reflects conscious control or restriction of food intake to control body weight. The questionnaire provides 21 questions for restraint, 16 for disinhibition, and 14 for hunger score. The instrument contains 36 yes/no items and 15 on a 1\u0026ndash;4 response scale. Higher scores denote higher levels of disinhibited eating, hunger, and restrained eating.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePersonality assessment\u003c/h2\u003e \u003cp\u003eThe NEO-ffi-3 Personality Inventory\u0026ndash; constitutes a widely used instrument that assesses individuals for five domains of personality openness \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e: openness, extraversion, conscientiousness, agreeableness, and neuroticism. The present research focused on the main effects and interaction of neuroticism and conscientiousness \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSubjective sleep\u003c/h2\u003e \u003cp\u003eThe Pittsburgh Sleep Quality Index (PSQI) measures sleep quality, disturbances, and typical sleeping habits retrospectively over the previous month \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. The PSQI generates an overall sleep quality score (higher scores indicate poorer sleep quality) from seven component scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The present study used overall sleep quality score (0\u0026thinsp;=\u0026thinsp;better; 3\u0026thinsp;=\u0026thinsp;worse).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDisease burden\u003c/h2\u003e \u003cp\u003eWe built upon previous studies that examined disease status and personality traits \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Data were obtained from the Medical History Questionnaire-Adults provided in the NKI-RS database. MHQ includes the participant's self-report on several illnesses and health conditions (answered with yes/no). NKI-RS medical history data collection involves cross-referencing check with medication forms to ensure the accuracy of self-reports. The included diseases are stroke, head injury, heart attack, heart condition (e.g., valve disease, coronary disease), lung disease (e.g., bronchitis, emphysema), cancer, type 2 diabetes, hyperthyroidism, hypothyroidism, arthritis. The total number of conditions was calculated, where higher scores representing higher disease burden.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe study examines associations between personality traits, eating behavior, and metabolic markers through cross-sectional and longitudinal analyses, investigating potential causal relationships and reverse causality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCross-sectional analysis\u003c/h2\u003e \u003cp\u003eLinear regression models taking eating behavior as an outcome were estimated using ordinary least squares. Model 1 examined associations between eating behavior and personality traits while controlling for sociodemographic covariates (i.e., age, sex, and education), sleep quality, and BMI, which have been previously associated with eating behaviors \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Model 2 examined the neuroticism by conscientiousness interaction as a predictor of eating behavior above and beyond each personality's main effects, sociodemographic covariates, sleep quality, and BMI.\u003c/p\u003e \u003cp\u003eTo examine whether the neuroticism by conscientiousness interaction could be driven by disease burden or an individual's metabolic health \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, Model 3 included a measure of disease burden, and Model 4 included metabolic markers (i.e., waist circumference, glucose, HLD, triglycerides, systolic and diastolic blood pressure).\u003c/p\u003e \u003cp\u003eFor all analyses, continuous covariates were standardized. Sex was coded as 0\u0026thinsp;=\u0026thinsp;Women, 1\u0026thinsp;=\u0026thinsp;Men. Data were analyzed using the R program (R version 4.1.2 and R Studio version 2021.09.1\u0026thinsp;+\u0026thinsp;172). Multiple regression models were performed using the \"lm\" function from package \"stats.\" Assumption checks were performed using the R \"performance\" package \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTraits associations and eating behaviors\u003c/h2\u003e \u003cp\u003eWe examined the impact of neuroticism and conscientiousness on eating behavior, focusing on congruity effects \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. This involved investigating whether specific patterns of association between these traits predict eating behavior by parametrically comparing their congruity (i.e., both traits low or both high; the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{1}\\)\u003c/span\u003e\u003c/span\u003e coefficient) and incongruity (i.e., one trait low and the other high; the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{3}\\)\u003c/span\u003e\u003c/span\u003e coefficient) across different eating patterns (see Sch\u0026ouml;nbrodt et al. (2018) for a review of these coefficients). This analysis was complemented by a Johnson-Neyman analysis aimed at examining the point of significance at which conscientiousness's influence on eating patterns was affected by levels of neuroticism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLongitudinal analysis\u003c/h2\u003e \u003cp\u003eMixed models examined the longitudinal associations between personality traits and eating behavior using \"lme\" function in R. Models were adjusted for sociodemographic covariates, sleep quality, and BMI (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Eating behavior dimensions were examined in separate models as outcome variables and neuroticism x conscientiousness x session interaction as predictors. Due to model convergence issues, the disinhibition dimension could not be analyzed longitudinally.\u003c/p\u003e \u003cp\u003eWe also explored whether baseline eating behavior scores were associated with personality changes over time (reverse causality). In separate models, eating behavior (i.e., disinhibition, hunger, and restraint) x session were predictors of longitudinal changes in neuroticism and conscientiousness.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDescription of the sample at baseline\u003c/h2\u003e \u003cp\u003eThe study included 712 NKI participants, of whom 67% were females. The mean age of participants at baseline was 48 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15.81), and the mean BMI was 27.80 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.55), with 35% with healthy weight, 30% with overweight, and 35% with obesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;30). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the full sample descriptive. Of note, we examine the five indicators of metabolic syndrome that are associated with increased risk of T2D and cardiovascular conditions \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Standard categorization for each indicator was performed based on the following values: Ethnicity-specific waist circumference\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;88 (F) and \u0026lt;\u0026thinsp;=\u0026thinsp;102 (M), \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e; triglycerides. \u0026gt; = 1.7 mmol/L; HDL\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;1. 29 (F) and \u0026lt;\u0026thinsp;=\u0026thinsp;1.3 (M); systolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;130 mmHg; diastolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;85 mmHg, or treatment of previously diagnosed hypertension; fasting plasma glucose\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;5.6 mmol/L or previously diagnosed T2D. The percentage of individuals presenting metabolic syndrome in our sample was 27% (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;192). The breakdown of individuals with each metabolic indicator was as follows: obesity\u0026thinsp;=\u0026thinsp;30% (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;211), waist circumference\u0026thinsp;=\u0026thinsp;41% (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;292), blood pressure\u0026thinsp;=\u0026thinsp;28% (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;197), glucose\u0026thinsp;=\u0026thinsp;.07% (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50), triglycerides\u0026thinsp;=\u0026thinsp;15% (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;110), HDL\u0026thinsp;=\u0026thinsp;16% (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;113). The distribution of reported diseases/health conditions was: none\u0026thinsp;=\u0026thinsp;44% (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;312), 1\u0026thinsp;=\u0026thinsp;32% (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;229), 2\u0026thinsp;=\u0026thinsp;16% (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;113), 3\u0026thinsp;=\u0026thinsp;6% (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;42), 4 or more\u0026thinsp;=\u0026thinsp;2% (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16).\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\u003e\u003cem\u003eCharacteristics of the participants and descriptive of baseline measures (N\u0026thinsp;=\u0026thinsp;712).\u003c/em\u003e\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (% female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (%)\u003c/p\u003e \u003cp\u003e- White\u003c/p\u003e \u003cp\u003e- Black or African American\u003c/p\u003e \u003cp\u003e- Other (American Indian, Asian, Pacific Islander)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003cp\u003e13\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e---\u003c/p\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.5\u0026ndash;65.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003cp\u003eCholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.39\u003c/p\u003e \u003cp\u003e92.54\u003c/p\u003e \u003cp\u003e5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.83\u003c/p\u003e \u003cp\u003e22.18\u003c/p\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u0026ndash;140\u003c/p\u003e \u003cp\u003e50\u0026ndash;368\u003c/p\u003e \u003cp\u003e0.10\u0026ndash;12.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70\u0026ndash;3.7\u003c/p\u003e \u003cp\u003e0.08\u0026ndash;16.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78\u0026ndash;219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u0026ndash;122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic Syndrome (% \u0026gt; 2 indicators)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e----\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThree Factor Eating Questionnaire\u003c/b\u003e\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\u003eDisinhibition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHunger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRestraint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePersonality traits\u003c/b\u003e\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\u003eNeuroticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026ndash;48\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=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of personality traits with baseline eating behaviors\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the estimates of multiple regression models. Model 1 examined the association between eating behavior patterns and personality traits. Model 2 specifically examined neuroticism by conscientiousness interaction beyond their main effects. Model 3 and Model 4 examined whether disease burden and metabolic markers modify these associations. All models adjusted for sociodemographic variables (i.e., age, sex, and education), sleep quality, and BMI.\u003c/p\u003e \u003cp\u003eModel 1 revealed neuroticism to reliably predict all eating behavior dimensions and conscientiousness to predict only restraint (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), so neuroticism scores were associated with an increase in maladaptive eating behavior, as shown for disinhibition restrain (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and hunger (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Conscientiousness was associated with increased eating restraint (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\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\u003e\u003cem\u003eAssociations of personality traits and baseline eating behaviors.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eDisinhibition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHunger\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eRestraint\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003evalue\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003evalue\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003evalue\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.12\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;/ R\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;adj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.299 / 0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.176 / 0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.096 / 0.087\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eDisinhibition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHunger\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eRestraint\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003evalue\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003evalue\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003evalue\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.23\u0026nbsp;\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u0026nbsp;\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.08\u0026nbsp;\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33\u0026nbsp;\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u0026nbsp;\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN x C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u0026nbsp;\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.09\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;/ R\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;adj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.303 / 0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.188 / 0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.098 / 0.088\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 \u003cem\u003eNote.\u003c/em\u003e Models are adjusted for sociodemographic covariates (age, sex, education), overall sleep quality, and BMI. Neuroticism\u0026thinsp;=\u0026thinsp;Neurot; Conscientiousness\u0026thinsp;=\u0026thinsp;Consci; Overall Sleep Quality\u0026thinsp;=\u0026thinsp;Sleep; Body Mass Index\u0026thinsp;=\u0026thinsp;BMI.\u003c/p\u003e \u003cp\u003eModel 2 revealed (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) that the neuroticism by conscientiousness interaction was significant for disinhibition (\u003cem\u003eb\u003c/em\u003e = -0.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040) and for hunger (\u003cem\u003eb\u003c/em\u003e = -0.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but not for restraint (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20). Comparing Model 1 and 2 revealed a significant difference for disinhibition, (\u003cem\u003eF\u003c/em\u003e(1,703)\u0026thinsp;=\u0026thinsp;4.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.04, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{R}^{2}=0.004\\)\u003c/span\u003e\u003c/span\u003e), for hunger (\u003cem\u003eF\u003c/em\u003e(1, 703)=10.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{R}^{2}=0.02\\)\u003c/span\u003e\u003c/span\u003e), but not for restraint (\u003cem\u003eF\u003c/em\u003e(1,703)\u0026thinsp;=\u0026thinsp;1.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{R}^{2}=0.002\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eModel 2 revealed that the interaction pattern was unaffected by the inclusion of disease burden (see Supplemental Table\u0026nbsp;1), which was further confirmed by comparing Model 2 and Model 3 for disinhibition (\u003cem\u003eF\u003c/em\u003e(1, 703)\u0026thinsp;=\u0026thinsp;0.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.60, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{R}^{2}=0.003\\)\u003c/span\u003e\u003c/span\u003e), for hunger, (\u003cem\u003eF\u003c/em\u003e(1, 703)=1.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{R}^{2}=0.002\\)\u003c/span\u003e\u003c/span\u003e), and for restraint, (\u003cem\u003eF\u003c/em\u003e(1, 703)=0.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.35, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{R}^{2}=0.001\\)\u003c/span\u003e\u003c/span\u003e). Model 4 revealed the neuroticism by conscientiousness interaction was unaffected by the inclusion of metabolic markers (see Supplemental Table\u0026nbsp;2), which was further confirmed by comparing Model 2 and 4 for disinhibition (\u003cem\u003eF\u003c/em\u003e(6, 697)\u0026thinsp;=\u0026thinsp;0.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.70, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{R}^{2}=0.004\\)\u003c/span\u003e\u003c/span\u003e), for hunger (\u003cem\u003eF\u003c/em\u003e(6,697)=0.77, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.60, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{R}^{2}=0.005\\)\u003c/span\u003e\u003c/span\u003e), and for restraint (\u003cem\u003eF\u003c/em\u003e(6,697)\u0026thinsp;=\u0026thinsp;0.84, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.54, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{R}^{2}=0.004\\)\u003c/span\u003e\u003c/span\u003e). To prevent unnecessary extension of the \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003eresults\u003c/span\u003e section, we present congruity and point of significance analysis only for Model 2, which examines the neuroticism by conscientiousness interaction while controlling for sociodemographic covariates, sleep quality, and BMI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNeuroticism and conscientiousness similarity scores\u003c/h2\u003e \u003cp\u003eTo clarify the relationship between neuroticism and conscientiousness, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the levels of each eating behavior dimension across all values of both traits. Neuroticism and conscientiousness are plotted on the Y and X axes, respectively, while eating behavior is represented on the Z axis.\u003c/p\u003e \u003cp\u003eWe parametrically compared the effects of similarity between neuroticism and conscientiousness on eating behavior through a similarity analysis (i.e., both traits high vs. both traits low and one of the traits high and the other low), A significant increase in disinhibition (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{1}=0.31\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003et\u003c/em\u003e (703) = 4.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and hunger scores (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{1}=0.26\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003et\u003c/em\u003e (703) = 3.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), were observed for congruity between traits, that is, both trait neuroticism and conscientiousness high \u003cem\u003eversus\u003c/em\u003e both traits low. An increase in disinhibition (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{3}=0.34\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003et\u003c/em\u003e (703) =8.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and in hunger scores (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{3}=0.23\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003et\u003c/em\u003e (703) = 5.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) was also observed for incongruency between traits, that is, one of the traits high and the other low. Overall, for both disinhibition and hunger, greater scores were observed when neuroticism was high and conscientiousness was low, reflecting the negative effect of neuroticism on maladaptive eating behavior.\u003c/p\u003e \u003cp\u003eA Johnson-Neyman analysis of regions of significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD) showed that the association between conscientiousness and disinhibition was negative for values of neuroticism higher than 1.18 (i.e., high scores in conscientiousness are associated with less disinhibited eating for those with high scores in neuroticism). A similar analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) showed that the relationship between conscientiousness and hunger was positive for values of neuroticism lower than \u0026minus;\u0026thinsp;0.87 and negative (i.e., high scores in conscientiousness being associated with less hunger), for values of neuroticism higher than 1.10 (with no clear associations inside the interval of [-0.87, 1.10]). Therefore, for both disinhibition and hunger, the negative slope represents higher values of neuroticism (i.e., greater than 1.18 for disinhibition and greater than 1.10 for hunger) being associated with less maladaptive eating behavior when conscientiousness was high. This pattern of results is consistent with the proposal of healthy neuroticism.\u003c/p\u003e \u003cp\u003eAn increase in restraint was observed when both neuroticism and conscientiousness were high as opposed to when both were low (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{1}=0.28\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003et\u003c/em\u003e (703) = 3.99, \u003cem\u003ep\u003c/em\u003e \u0026lt;\u0026thinsp;0.0001), but no increase was observed for incongruency between traits (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{3}=0.03\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003et\u003c/em\u003e (703) = 0.70, \u003cem\u003ep\u003c/em\u003e =\u0026thinsp;0.73; see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Therefore, this finding also reflects the positive effect of healthy neuroticism on adaptive eating behavior.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of personality traits with longitudinal eating behaviors\u003c/h2\u003e \u003cp\u003eMixed models adjusting for sociodemographic covariates, sleep quality, and BMI examined associations of baseline personality scores with longitudinal eating behavior dimensions. Models included the neuroticism x conscientiousness x session interaction, with session as a continuous variable, including two follow-up periods, concluding with a 3-year follow-up. Results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A significant three-way interaction was found for hunger. However, contrasts aimed at examining slope differences for neuroticism and conscientiousness over time (session) were not significant (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Therefore, while significant, the source of this three-way interaction could not be verified statistically (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For restraint, there was a significant conscientiousness by session interaction, indicating an increase in restraint scores over time for high values of conscientiousness (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReverse causality was explored using Mixed models adjusting for sociodemographic covariates, sleep quality, and BMI. In separate models, we examined associations between eating behavior and longitudinal personality. The complete results are presented in Supplementary Materials (Supplemental Tables\u0026nbsp;3\u0026ndash;5). The only significant finding was the associations of hunger with longitudinal neuroticism (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044). That is, hunger scores at baseline were associated with an increase in neuroticism over time. No other interactions were significant.\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\u003eAssociations of personality traits with longitudinal eating behaviors.\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eHunger\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRestraint\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eZ-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eZ-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u0026nbsp;\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u0026nbsp;\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.26\u0026nbsp;\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurot x Session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsci x Session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u0026nbsp;\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurot x Consci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.16\u0026nbsp;\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN x C x Session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u0026nbsp;\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026nbsp;\u0026nbsp;\u0026nbsp;** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u0026nbsp;\u0026nbsp;\u0026nbsp;*** p\u0026thinsp;\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 \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious studies have shown both positive and negative effects of neuroticism on health behaviors and risk of mortality. This study is the first to investigate whether healthy neuroticism, defined as the interaction between neuroticism and conscientiousness, reliably predicts eating behaviors beyond the main effects of each trait. The analysis controlled for sociodemographic covariates, sleep quality, BMI, disease burden, and indicators of metabolic health. Additionally, the present study explored longitudinal relationships between personality and eating behaviors for the first time.\u003c/p\u003e \u003cp\u003eCross-sectionally, our results show both the overall negative effects of neuroticism on eating behaviors and the positive effects of neuroticism derived from its association with conscientiousness, which is consistent with the proposal of healthy neuroticism \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Multiple regression models indicated that neuroticism by conscientiousness interaction reliably predicted disinhibition (loss of control over eating) and hunger (subjective feeling of hunger and cravings). Congruity analysis further revealed that similarity between traits (i.e., high scores in both traits) was associated with greater disinhibition, hunger, and restraint. The dissimilarity between traits (i.e., neuroticism high and conscientiousness low \u003cem\u003eversus\u003c/em\u003e the opposite) was associated with high disinhibition and hunger but not with high restraint scores. Surface plots were utilized to illustrate the neuroticism by conscientiousness interaction pattern, effectively displaying eating behavior scores across the full range of neuroticism and conscientiousness values. Point of significance analysis identified specific neuroticism values (1.10 for disinhibition and 1.18 for hunger) where healthy neuroticism was evident.\u003c/p\u003e \u003cp\u003eTaken together, our findings corroborate those of previous studies showing associations between neuroticism and maladaptive eating \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, and between conscientiousness and eating restraint \u003csup\u003e\u003cspan additionalcitationids=\"CR60 CR61\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Our study provides new evidence that healthy neuroticism is associated with reduced scores in maladaptive eating behaviors such as disinhibition and hunger, while promoting increased self-regulatory eating restraint. Early studies suggested that eating restraint could precede disordered eating behaviors \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Progress in eating behavior research repeatedly demonstrates the regulatory nature of restraint through its association with various anthropometric measures, and also with high intake of healthy foods, weight control and maintenance \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Consistent with our hypothesis, our results indicate that high values in both traits significantly correlate with greater eating restraint compared to high conscientiousness alone.\u003c/p\u003e \u003cp\u003eOur results were unaltered by including disease burden and markers of metabolic health. Previous studies have reported associations between neuroticism and disease burden \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, no associations between personality traits with metabolic markers such as glucose levels and cholesterol \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, but significant associations with inflammatory markers \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In the context of eating behaviors, one could argue that disease or worsening of metabolic status could drive changes in self-reported eating behavior. Individuals with high levels of neuroticism are indeed more attentive to physical symptoms and more prone to report them \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Our results do not support the idea that disease burden or metabolic status alone drove the relationship between personality and self-report eating behavior. However, it is important to note that individuals having one or more diseases (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;400) had significantly higher scores on eating restraint than those reporting no disease (\u003cem\u003et\u003c/em\u003e (710) = -2.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030). In contrast, individuals with metabolic syndrome did not differ on any of the three eating behaviors dimensions. None of these effects were associated with personality, but future studies could explore this issue further by examining specific subclinical and clinical populations.\u003c/p\u003e \u003cp\u003eThe point of significance analysis reveals that the pattern of healthy neuroticism observed for disinhibition and hunger occurs for about less than a third of the sample. Thus, the effects of healthy neuroticism do not appear to span a wide range of neuroticism values, at least for the NKI sample. One potential reason for this pattern is the relatively high correlation (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.40) between neuroticism and conscientiousness found in this study, compared to the correlation reported in a previous study on healthy neuroticism (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.17) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Consequently, a smaller proportion of individuals exhibited congruity between traits in our sample.\u003c/p\u003e \u003cp\u003eLongitudinally, we did not find baseline personality traits to predict changes in disinhibition or hunger scores over time. Although a significant three-way interaction was found for hunger, the source of the interaction could not be statistically verified. We found that baseline levels of conscientiousness were associated with increased restraint over time. Concerning conscientiousness, our findings align with the notion that features like self-discipline and goal-oriented characteristic of conscientiousness may foster adaptive eating behavior \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur analysis of reverse causality, examining how eating behavior relates to longitudinal personality changes, found that only hunger was associated with changes in neuroticism over time. Overall, our longitudinal findings do not definitively support a bidirectional association between personality and eating behaviors, but they highlight the need of further investigation in this area. Previous studies indicate that eating behaviors tend to remain relatively stable over time \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, except for restraint which may increase following weight changes \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Other studies suggest that eating behaviors can change over time in response to life circumstances such as the social restrictions imposed by COVID-19 \u003csup\u003e68\u003c/sup\u003e, life stress \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, and efforts to maintain weight loss \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, with individuals who successfully maintain weight loss showing increase in restraint over time.\u003c/p\u003e \u003cp\u003eOur result reveal, for the first time, that healthy neuroticism is associated with eating behaviors independently of each personality trait, disease burden and clinical metabolic markers. These findings have significant implications for both clinical and preventive health psychology settings, contributing to the understanding and potential intervention strategies aimed at improving health outcomes through psychological pathways. Moving forward, it will be important to identify the mechanisms underlying these associations. Healthy neuroticism influences health outcomes through both behavioral and physiological pathways \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e including individuals' neurobiological response to stress \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Individuals who are high in neuroticism are more vulnerable to negative emotions, and are also more prone to enter stress-induced situations \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Oxidative stress from psychosocial stressors or overconsumption of glucose and lipids \u003csup\u003e\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e triggers physiological responses affecting brain areas controlling not only eating behavior but other functions \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Our findings demonstrating that hunger predicts changes in neuroticism over time suggest the possibility that maladaptive eating behavior may precede (and not only result from) changes in affect, reward, expectancy and personality \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. The role played by healthy neuroticism in these bidirectional effects requires further investigation.\u003c/p\u003e \u003cp\u003eThe strength of our study lies in leveraging the Nathan Kline Institute for Psychiatric Research - Rockland Sample data, a large community sample of healthy individuals with comprehensive anthropometric, psychiatric, health history, cardiovascular, and metabolic data. This allowed us to establish associations between personality and eating behaviors both cross-sectionally and longitudinally, while controlling for numerous potential confounders. Furthermore, our study innovates by examining healthy neuroticism through a combination of multiple regression models, congruity analysis, and region of significance analysis. Integrating these methods enabled us to systematically compare associations between personality and eating behavior across the entire distribution of values. However, our study is limited by a relatively short follow-up period. Given the slow progression of metabolic dysregulation and its potential behavioral effects, it's possible that the full impact of personality on eating behavior over time was not fully captured. Finally, our study utilized a self-report measure of eating behavior tendencies, which has been validated in various populations. Future research could enhance our understanding by incorporating assessments of food choices, albeit also self-report based, they would allow for the assessment of behaviors related to energy consumption and healthy eating choices.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e Declaration of competing interest:\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI.A.: Conceptualization, data curation, formal analyses, methodology, writing\u0026mdash;original draft, writing\u0026mdash;review \u0026amp; editing. K.Y.: data curation, visualization, writing\u0026mdash;review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Dr. Mattan Ben-Shahar for his valuable guidance and input on this report's methodology and statistical methods.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData Availability: The data used in this study are publicly available and can be accessed through the Nathan Kline Institute for Psychiatric Research (NKI) Rockland Sample. The NKI dataset is hosted on the NIMH Data Archive (NDA) and can be accessed by authorized users. In order to request access to the data, please contact the corresponding author through [email protected]; or NKI data representative through [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcCrae, R. R. \u0026amp; Costa, Jr., P. T. Brief Versions of the NEO-PI-3. 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Trends in Cognitive Sciences 23, 349\u0026ndash;361 (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"personality, healthy neuroticism, eating behavior, metabolism","lastPublishedDoi":"10.21203/rs.3.rs-4789671/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4789671/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTrait neuroticism is associated with maladaptive behaviors and mortality risk. However, high levels of both neuroticism and conscientiousness (i.e., healthy neuroticism) are associated with various positive health behaviors. Eating behavior is a modifiable risk factor for obesity and metabolic diseases. This study investigates the cross-sectional and longitudinal associations between healthy neuroticism and eating behaviors. Data from the Nathan Kline Institute for Psychiatric Research - Rockland Sample included 712 adults with complete assessments of personality, eating behaviors, and clinical metabolic markers. Linear and mixed linear regression models examined cross-sectional and longitudinal associations of eating behaviors and personality, adjusting for socio-demographics (age, sex, education), sleep quality, body mass index (BMI), and metabolic markers. Healthy neuroticism predicted disinhibition and hunger dimensions of eating behavior cross-sectionally, a result that withstood the inclusion of disease burden and clinical metabolic markers. Longitudinally, healthy neuroticism didn't predict changes in eating behavior. Greater conscientiousness scores were associated with increase in restraint, and greater hunger scores with increase neuroticism over time. These findings provide the first evidence that neuroticism is associated with less maladaptive eating behavior tendencies when modulated by conscientiousness. The implications of these associations for potential bidirectional relationships between eating behavior, metabolic health, and personality are discussed.\u003c/p\u003e","manuscriptTitle":"(Un)Healthy Neuroticism and Eating Behaviors: A Study of the Nathan Kline Institute for Psychiatric Research - Rockland Sample","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-03 20:34:54","doi":"10.21203/rs.3.rs-4789671/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-03T07:11:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-02T21:02:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194317224437477651746165554716414800265","date":"2024-09-22T15:54:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-12T15:22:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198217676074186695971387042065930055","date":"2024-09-04T08:07:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-21T15:07:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-21T15:05:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-14T08:10:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-08T05:33:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-23T14:48:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a47d52f2-d4c3-4879-afbc-140632807c25","owner":[],"postedDate":"September 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36943737,"name":"Biological sciences/Psychology"},{"id":36943738,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-02-24T16:24:11+00:00","versionOfRecord":{"articleIdentity":"rs-4789671","link":"https://doi.org/10.1038/s41598-025-85750-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-02-18 15:57:24","publishedOnDateReadable":"February 18th, 2025"},"versionCreatedAt":"2024-09-03 20:34:54","video":"","vorDoi":"10.1038/s41598-025-85750-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-85750-4","workflowStages":[]},"version":"v1","identity":"rs-4789671","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4789671","identity":"rs-4789671","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}