Appetitive Trait Profiles in Late Life: Links to Eating Disorder Psychopathology and Psychosocial Well-Being

Appetitive Trait Profiles in Late Life: Links to Eating Disorder Psychopathology and Psychosocial Well-Being | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Appetitive Trait Profiles in Late Life: Links to Eating Disorder Psychopathology and Psychosocial Well-Being Gui Chen, Qiling Peng, Xingwei Luo, Shaowu Li, Yunyi Cheng, Urvashi Dixit, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9173767/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective Appetitive traits shape eating behaviors across the lifespan, yet their patterns and health correlates in later life remain poorly understood. This study used latent profile analysis (LPA) to identify distinct profiles of appetitive traits among older adults and to examine their sociodemographic and oral health correlates, as well as their associations with eating disorder symptoms and psychosocial outcomes. Method A sample of 579 Chinese older adults (aged 60–93 years) was included. LPA was conducted to derive appetitive trait profiles, followed by multinomial logistic regression and Bolck–Croon–Hagenaars (BCH) analyses to examine predictors and health outcomes. Results Four profiles were identified: Low Appetitive Traits (16.1%), Moderate Appetitive Traits with Emotional Under-Eating (29.0%), Moderate Appetitive Traits (44.9%), and High Conflicted Appetitive Traits (10.0%). Oral health emerged as a consistent correlate of profile membership, alongside age, marital status, residence, and education, whereas sex and number of natural teeth were not associated with memberships. Profiles differed significantly in psychological distress, disordered eating, ARFID symptoms, and health-related quality of life. The High Conflicted Appetitive Traits profile was characterized by the greatest eating-related psychopathology and poorest psychosocial outcomes, whereas the Low Appetitive Traits profile showed the most favorable health profile. Conclusions These findings demonstrate substantial heterogeneity in appetitive traits in later life and highlight their close links with oral health, sociodemographic factors, and psychological well-being. Identifying high-risk appetitive profiles may inform targeted interventions integrating nutritional guidance, oral health promotion, and psychosocial support to promote healthy aging. appetitive traits latent profile analysis older adults eating disorder health-related quality of life Figures Figure 1 Plain English Summary Little is known about appetitive traits and their health connections among older adults. In this study, we surveyed 579 Chinese adults aged 60 to 93 years and identified four distinct appetitive trait profiles. These profiles differed in both eating-related symptoms and well-being. Older adults in the High Conflicted Appetitive Traits group showed the highest levels of eating disorder symptoms, ARFID symptoms, psychological distress, and lower quality of life. In contrast, those in the Low Appetitive Traits group generally experienced better health outcomes. Oral health was also associated with specific appetite profiles. These findings have implications for recognizing higher-risk profiles to design tailored interventions for appetitive traits to improve eating behaviors and well-being of older adults. 1. Introduction Appetite plays a central role in regulating dietary intake by shaping food-related bodily cues (e.g., hunger and satiety cues) and food preferences [ 1 ]. In older adulthood, notable physiological changes occur, including reduced sensitivity to taste and smell, altered gastrointestinal function, and oral health problems such as tooth loss, which can significantly impact older adults’ appetite and eating patterns [ 2 , 3 ]. These shifts may compromise nutritional intake and increase vulnerability to adverse health outcomes, including physical frailty, poor mental health, and diminished quality of life [ 4 – 7 ]. China, like many countries globally, is facing a rapidly aging population [ 8 ]. Because nutrition is a modifiable determinant of health in later life, promoting healthy eating behaviors among older adults is central to supporting active aging and reducing age-related declines in health [ 9 ]. Therefore, it is crucial to understand the intricate relationships between appetitive traits and health outcomes in aging populations to develop targeted interventions that address older adults’ health needs. 1.1. Appetitive Traits and Health Outcomes Appetitive traits are biologically based tendencies shaped and modified by environmental influences, and together these factors account for individual differences in eating behaviors [ 10 , 11 ]. To facilitate a comprehensive assessment of appetitive traits in adults, Hunot et al. [ 1 ] developed the Adult Eating Behavior Questionnaire (AEBQ), which captures eight dimensions, including four food approach appetitive traits (hunger, food responsiveness, emotional over-eating, enjoyment of food) and four food avoidance appetitive traits (satiety responsiveness, emotional undereating, food fussiness, and slowness in eating). Related research on appetitive traits suggests that they are associated with both the quantity and quality of dietary intake. For instance, picky eating, external eating, and emotional overeating are more strongly linked to consumption of energy-dense foods such as high-sugar and high-fat foods [ 12 – 14 ], whereas enjoyment of food is associated with healthier dietary patterns rich in fruits, vegetables, and protein [ 15 ]. Additionally, two traits, food responsiveness and satiety responsiveness, are particularly relevant to food intake: higher food responsiveness is associated with more frequent eating, whereas lower satiety responsiveness is linked to reduced food intake [ 16 ]. Research on children and adolescents consistently demonstrates significant associations between appetitive traits, eating disorders, and body mass index (BMI) [ 10 , 17 , 18 ]. For example, a systematic review of 27 studies using child and baby eating behavior questionnaires found that food-approach appetitive traits, reflecting heightened responsiveness to external food cues, are associated with higher BMI z-scores, whereas food-avoidance traits, characterized by reduced or restrictive appetite, are associated with lower BMI z-scores [ 19 ]. Beyond weight outcomes, growing longitudinal evidence suggests that appetitive traits are also implicated in the development of eating disorder symptoms across the lifespan. For example, a large European longitudinal study found that greater food responsiveness in early childhood was associated with higher odds of disordered eating behaviors (e.g., binge eating) in adolescence [ 17 ]. Extending this evidence to later life, research in Chinese older adults has shown that higher emotional under-eating and greater satiety responsiveness at baseline were prospectively associated with higher eating disorder symptomatology at one-year follow-up [ 20 ]. Across age groups, appetitive traits also play a critical role in shaping individuals’ emotional states and overall mental well-being. In the general population, traits such as emotional eating and external eating have been linked to mental health conditions, including depression and anxiety symptoms and greater loneliness and stress [ 14 , 21 ]. Emotional eating often serves as a maladaptive coping mechanism to manage negative emotions; however, rather than alleviating these emotions, it frequently exacerbates them, creating a cycle of emotional distress [ 22 ]. 1.2. Appetite Traits Change with Age Appetitive traits develop early in childhood and often remain relatively stable over time [ 23 , 24 ]. However, aging brings marked changes in certain traits, such as food fussiness, food responsiveness, and enjoyment of food, driven by biological, psychological, and environmental influences [ 25 , 26 ]. For example, children often exhibit higher levels of food responsiveness and food enjoyment, driven by their increased growth and energy needs. However, with age, these traits often decline due to metabolic changes, reduced sensory acuity, and shifts in taste preferences [ 3 ]. Furthermore, older adults often experience loss of appetite associated with hormonal changes, reduced sensory perception, and altered taste functioning [ 27 , 28 ]. This age-related decline in appetite, often referred to as the “anorexia of aging,” is characterized by reduced motivation to eat and increased food avoidance, which can contribute to reduced energy intake, increased risk of malnutrition, and undesirable weight loss [ 29 , 30 ]. Appetite may also decrease transiently during acute illness, further compounding nutritional vulnerability. Beyond these biological changes, appetite and eating behaviors in later life are often compounded by psychological and social factors, such as depression symptoms, social isolation, and chronic health conditions, which further affect eating behaviors and food intake [ 27 , 31 ]. As a result of these interrelated physiological, psychological, and social influences, older adults often exhibit reduced energy intake and altered dietary patterns, including a noticeable shift in food preferences, typically away from sweet or high-calorie food, reflecting age-related changes in appetitive traits [ 32 – 34 ]. Oral health plays a particularly important role: chewing difficulties, tooth loss, or denture problems can make eating less enjoyable and lead to food-avoidance behaviors [ 35 – 37 ]. These shifts may result in lower energy intake, poorer diet quality, and increased nutritional vulnerability. Despite this, little is known about how combinations of appetitive traits manifest in older adults. 1.3. A Latent Profile Perspective on Appetitive Traits Although much is known about individual appetitive traits, people often exhibit co-occurring tendencies (e.g., high food responsiveness combined with emotional undereating). Person-centered methods, such as latent profile analysis (LPA), enable researchers to capture these complex combinations by identifying subgroups that share similar trait patterns. Previous LPA studies, mostly in children, adolescents, and young adults, have revealed heterogeneous profiles that are differentially associated with outcomes such as dietary behaviors, weight status, and psychosocial well-being [ 38 – 44 ]. For example, some studies focused on emotional eating traits, such as emotional overeating and undereating [ 43 ], while others concentrated on traits related to food avoidance and picky eating [ 40 , 43 ]. Although these studies provide valuable insights into specific aspects of eating behavior, they may not capture the full complexity of appetitive traits across a broader spectrum. In contrast, few studies have examined the full range of appetitive traits using the AEBQ [ 39 , 41 , 45 ]. Existing research has identified varying numbers of latent classes across samples, suggesting that latent profile patterns may differ by population. For instance, a study conducted by Coakley et al. [ 39 ] identified four latent classes among university students, while a study by Chen et al. [ 45 ] found only three latent classes in a sample of final-year students. These findings collectively underscore the population-specific variability and diversity of appetitive traits. To our knowledge, no studies have examined latent profiles of appetitive traits among older adults to date, despite the unique physiological and social contexts of this population. 1.4. The Present Study This study sought to address these gaps by applying LPA to a community sample of Chinese older adults. Specifically, our objectives were threefold. First, we identified latent subgroups based on eight appetitive traits assessed by the AEBQ [ 1 ]. Second, we examined demographic and oral health factors associated with profile membership. Third, we examined whether the identified profiles differed in disordered eating symptoms, avoidant/restrictive food intake disorder (ARFID) symptoms, psychological distress, health-related quality of life (HRQoL), and BMI. By integrating demographic, psychological, and oral health perspectives, this study provides an initial characterization of appetite patterns in later life and offers preliminary evidence to inform future longitudinal research and the development of targeted interventions to support healthy eating and psychosocial well-being in aging populations. 2. Method 2.1. Procedures and Participants The study was approved by the Research Ethics Committee of [ redacted for peer review ] (No. redacted for peer review ), and all participants provided informed consent. Older adults were recruited through face-to-face household interviews conducted by trained psychology students following standardized administration procedures. During these visits, participants completed the questionnaires, and interviewers provided standardized instructions and assistance as needed (e.g., clarifying item wording) without influencing responses. Inclusion criteria were (a) age \(\:\ge\:\) 60 years, consistent with commonly used demographic conventions that aligned with China’s legal definition of “older persons” as citizens aged 60 years and above (National People’s Congress Standing Committee, 2018), and (b) ability to complete the survey independently. Individuals with mobility limitations or communication difficulties were excluded to minimize participant burden. Each interview lasted approximately 60 minutes. A total of 612 individuals were approached for the study. Of these, 25 were excluded due to incomplete questionnaires (i.e., more than one scale was not completed), and eight were younger than 60 years, leaving 579 valid cases (320 women, 55.3%; 259 men, 44.7%), aged 60–93 years ( M = 71.02, SD = 7.96). Additional demographic characteristics included marital status (currently married or co-habiting: n = 429, 74.1%; without a spouse: n = 150, 25.9%), occupational status (retired: n = 423, 73.1%; employed [any current paid work, part-time or full-time]: n = 156, 26.9%), residence (rural: n = 501, 86.5%; urban: n = 78, 13.5%), and educational attainment (Low, illiterate, or primary-school education: n = 381, 65.8%; Medium, middle- or high-school education: n = 182, 31.4%; High, university degree or higher: n = 16, 2.8%). 2.2. Measures 2.2.1. Appetitive Traits The Adult Eating Behavior Questionnaire (AEBQ) is a 35-item self-report questionnaire assessing appetitive traits in adults [ 1 ]. It includes eight subscales: four for food-approach traits (Hunger, Enjoyment of Food, Emotional Over-Eating, and Food Responsiveness) and four food-avoidance traits (Slowness in Eating, Emotional Under-Eating, Satiety Responsiveness, and Food Fussiness). The scale uses a five-point response scale, ranging from 1 (strongly disagree) to 5 (strongly agree). A mean score for each subscale was calculated, with higher scores indicating greater endorsement of appetitive traits. The AEBQ demonstrated good reliability and validity in both Western [ 1 , 10 , 46 ] and Chinese contexts [ 47 ] and has been used in Chinese older adults [ 20 ]. In the present study, the Cronbach’s α values for the subscales of Enjoyment of Food, Hunger, Emotional Over-Eating, Food Responsiveness, Food Fussiness, Emotional Under-Eating, Slowness in Eating, and Satiety Responsiveness were 0.78, 0.85, 0.94, 0.68, 0.60, 0.95, 0.68, and 0.75, respectively. The eight AEBQ scales were used to identify latent profiles using LPA. 2.2.2. Eating Disorder Symptoms In this study, two validated tools were used to assess ED symptoms. The 12-item short form of the Eating Disorder Examination Questionnaire (EDE-QS) [ 48 ] was used to assess traditional, thinness-oriented ED symptoms. The EDE-QS consists of 12 items; each rated on a 4-point response scale ranging from 0 (0 day/never) to 3 (6–7 days/markedly). Scores of all items are summed to yield a total score, with a higher score indicating a higher level of traditional, thinness-oriented ED symptomatology. The Chinese version of the EDE-QS was validated in Chinese samples [ 49 ] and has been used in older Chinese adults [ 20 , 50 ]. Cronbach’s α for the present older adult sample was 0.88. The Nine-Item ARFID Screen (NIAS; [ 51 ]) was used to assess symptoms of Avoidant/Restrictive Food Intake Disorder (ARFID). The NIAS comprises nine items to evaluate the core presentations of ARFID (avoidance of foods, poor appetite, and fear of negative consequences) [ 51 ]. Each item is rated on a 5-point Likert scale ranging from 0 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater severity of ARFID symptoms. Scores for each subscale are calculated by summing the responses to the corresponding items, and a total NIAS score is derived by summing all item scores. The NIAS has been validated in Chinese samples [ 43 ] and used with Chinese older adults [ 20 ]. In the present study, the Chinese version of the NIAS, validated by He and colleagues [ 52 ], was used, with Cronbach’s alphas of 0.89, 0.79, 0.77, and 0.85 for the total scale, picky eating, appetite, and fear subscales, respectively. 2.2.3. Health-Related Quality of Life The SF-8 Health Survey (SF-8) was used to assess participants’ health-related quality of life (HRQoL). The eight items cover eight separate components of HRQoL named general health (GH; overall perceived health), physical function (PF; ability to perform physical activities), role physical (RP; limitations of usual daily activities due to physical health problems), bodily pain (BP; pain intensity), vitality (VT; energy level, e.g., feeling energetic versus tired), social function (SF; interference of health with social interactions with family and friends), mental health (MH; psychological distress and well-being), and role emotional (RE; limitations in usual daily activities or housework due to emotional problems) [ 53 , 54 ]. The Chinese version of SF-8 also demonstrated good psychometric properties in Chinese general populations [ 54 – 56 ], including older adults [ 55 ]. For scoring, the original score of all items can be converted to the percentage standard score, according to the following scoring formula [ 56 ]: ( x - min) / (max - min) × 100, where x is the observed score, and min and max are the lowest and highest possible values for that item. Higher scores indicate better health for that component. The average percentage standard score across eight items was used to compute the total score, with higher scores indicating higher HRQoL [ 53 , 54 ]. In this study, the Cronbach's α for the total scale was 0.87. 2.2.4. Psychological Distress The Chinese version of the 6-item Kessler Psychological Distress Scale (K6) was used to assess psychological distress symptoms (e.g., nervousness, worthlessness, and hopelessness) during the past 30 days [ 57 , 58 ]. Each item is rated on a 5-point scale, with scores ranging from 0 (none of the time) to 4 (all the time). Summing the six items yields a total score range of 0 to 24, with higher scores indicating greater psychological distress. The Chinese version of the K6 showed good psychometric properties in multicultural environments [ 57 ], including application to Chinese older adults [ 58 ]. In this study, the K6 had a Cronbach's \(\:\:\text{a}\text{l}\text{p}\text{h}\text{a}\:\) of 0.87. 2.2.5. Geriatric Oral Health The Chinese version of the Geriatric Oral Health Assessment Index Scale (GOHAI) was used to assess the oral health-related quality of life among older adults. The GOHAI contains 12 items that evaluate three dimensions: physical functioning (4 items), pain and discomfort (3 items), and psychosocial functioning (5 items). Participants respond on a 5-point response scale ranging from 1 (always) to 5 (never). The GOHAI score was calculated by summing the scores of 12 items, with higher scores indicating better oral health status. The Chinese version of GOHAI demonstrated acceptable reliability and validity [ 59 ]. In this study, the Chinese version yielded a Cronbach’s \(\:\:\alpha\:\:\) of 0.92. 2.2.6. Demographic Variables A self-administered questionnaire was developed for this study to collect participants’ demographic and oral health information, including sex, age, marital status, occupational status, residence, education, and the number of natural teeth. In the original questionnaire, marital status was categorized as “single,” “married,” “divorced,” and “widowed”; education had four levels: “illiterate or primary school,” “junior high school,” “senior high school or secondary school”, and “university and above”; occupational status included “retired,” “working,” and “employed”; and residence was classified as “urban” or “rural.” When analyzing these data, “single,” “divorced,” and “widowed” were combined into “no spouse,” the occupational statuses of “working” and “employed” were combined into “employed,” and educational levels were merged into three ordered categories: low (“illiterate or primary school” and “junior high school”), medium (“senior high school or secondary school”), and high (“university and above”). 2.3. Data Analysis Analyses were conducted using SPSS 26.0 [ 60 ] and Mplus 8.3 [ 61 ]. Descriptive statistics (means, standard deviations, and ranges) were computed for study variables. LPA was then conducted using the robust maximum likelihood estimator (MLR). To ensure the highest log-likelihood value was discovered during model estimation, we used 500 random starts and 20 iterations. Model selection was based on multiple criteria: lower AIC, BIC, and SABIC values, LMRT and BLRT tests, and entropy values [ 61 , 62 ]. Specifically, lower values of AIC, BIC, and SABIC indicate a better model fit. Entropy is an indicator of classification accuracy; a higher value indicates greater accuracy in this classification. The LMRT and BLRT are the significance tests that compare the k-class model to the k-1 class. If p < 0.5, it indicates the k-class model is a better fit. Solutions with classes representing < 5% of the sample were rejected as spurious [ 62 ]. After selecting the best model, latent appetitive trait patterns were named based on their score characteristics. Differences across the eight subscales of the Adult Eating Behavior Questionnaire (AEBQ) among latent appetitive trait profiles were examined using one-way analysis of variance (ANOVA), with effect sizes reported for significant findings. Partial eta-squares (η2) were used for the difference effect size, for which values of 0.01, 0.06, and 0.14 were reflective of small, medium, and large effect sizes, respectively [ 63 ]. Then, a robust three-step approach using multinomial logistic regression, proposed by Asparouhov and Muthén [ 64 ], was used to examine the demographic risk factors associated with potential appetitive trait patterns. The resulting odds ratios quantify the extent to which each covariate increases or decreases the likelihood of belonging to a particular profile relative to the reference profile, thereby identifying salient risk factors for each latent profile. Lastly, we employed the Bolck-Croon-Hagenaars method (BCH; [ 65 ]), a three-step weighting approach for testing distal outcome differences across latent profiles while accounting for classification uncertainty, to investigate whether the potential profiles differed in terms of health-related outcomes. Global Wald χ² tests evaluated overall differences among potential appetitive traits patterns, followed by pair-wise contrasts to pinpoint specific between-class disparities in health-related outcomes. 2.4. Declaration of Generative AI in Scientific Writing During the preparation of this work, the authors used ChatGPT 5.2 to improve the language of the manuscript. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the content of the published article. 3. Results 3.1. Descriptive Statistics Descriptive statistics for all study variables and demographic characteristics are presented in Table 1 . Insert Table 1 Here Table 1 Demographic characteristics and descriptive statistics by sex. Variables Total Women ( n = 259) Men ( n = 320) \(\:{\chi\:}^{2}\) / t b Effect size c n (%)/ M ( SD ) a n (%)/ M ( SD ) a n (%)/ M ( SD ) a Age (years) 71.38 (7.08) 71.74 (7.03) 71.08 (7.12) 1.11 0.09 BMI (kg/m 2 ) 21.65 (3.23) 21.840 (3.14) 21.49 (3.30) 1.30 0.11 Material status 26.73 *** Have no spouse 429 (74.1) 210 (65.6) 219 (84.6) 6 Have a spouse 150 (25.9) 110 (34.4) 40 (15.4) 6 Residence 0.58 Rural 501(13.5) 221 (85.3) 280 (87.5) 46 Urban 78 (13.5) 38 (14.7) 40 (12.5) 46 Occupation 0.48 Retired 424 (73.2) 186 (71.8) 238 (74.4) 39 In employment 155 (26.8) 73 (28.2) 82 (25.6) 39 Education level 63.45 *** Low 381 (65.8) 126 (48.6) 255 (79.7) 4 Medium 125 (21.6) 79 (30.5) 46 (14.4) 7 High 73 (12.6) 54 (20.8) 19 (5.9) 7 AEBQ 98.51 (16.38) 98.96 (17.44) 98.07 (14.37) .06 Enjoyment of food 10.62 (2.02) 10.58 (2.02) 10.65 (2.02) − .39 − .03 Emotional over-eating 12.63 (4.55) 13.11 (5.10) 12.14 (3.99) 2.57* .21 Food responsiveness 11.26 (2.94) 11.17 (2.79) 11.35 (3.07) .15 − .06 Hunger 13.24 (5.87) 13.41 (5.62) 12.60 (4.11) 1.94 .16 Food fussiness 13.58 (2.81) 13.46 (2.70) 13.69 (2.91) − .95 − .08 Emotional under-eating 15.51 (4.90) 15.42 (4.45) 15.59 (5.35) -1.07 − .03 Slowness in eating 11.57 (3.88) 11.49 (2.93) 11.65 (4.07) − .53 − .05 Satiety responsiveness 10.43 (3.08) 10.42 (3.32) 10.44 (2.84) − .09 − .01 Oral health 26.24 (8.90) 26.09 (9.23) 26.39 (8.57) − .40 − .03 Number of teeth 19.81 (10.02) 20.22 (10.08) 19.40 (9.93) .98 .08 K6 6.27 (4.71) 6.46 (4.94) 6.09 (4.49) .92 .08 SF-8 67.92 (15.47) 69.22 (15.55) 66.62 (15.38) 2.01 * .17 EDE-QS 5.086 (7.84) 6.05 (9.00) 4.15 (6.70) 2.80 ** .25 NIAS 16.96 (7.85) 16.97(7.92) 16.94 (7.78) .04 .00 Picky eating 5.074 (2.97) 5.12 (3.02) 5.02 (2.93) .42 .04 Appetite 5.77 (2.88) 5.69 (2.88) 5.84 (2.89) − .61 − .05 Fear 6.12 (3.25) 6.15 (3.21) 6.08 (3.29) .26 .02 Note: a % for categorical variables, means and standard deviations for continuous variables; b Chi-squared tests for categorical variables, independent t-tests for continuous variables. c NNT for categorical variables, Cohen’s d for continuous variables. d Calculated based on self-reported height and weight. * p < .05, ** p < .01, *** p < .001. AEBQ, The Adult Eating Behavior Questionnaire; NIAS, Nine Item for ARFID Scale; EDE-QS, The 12-item short form of the Eating Disorder Examination Questionnaire; K6, The Chinese version of the 6-item Kessler Psychological Distress Scale; SF-8, The SF-8 Health Survey; M, Mean; SD, Standard Deviation; BMI, Body Mass Index. 3.2. Latent Profile Identification Latent profile analysis models with one to six profiles were tested. Fit indices are shown in Table 2 . Although the five- and six-profile models demonstrated lower AIC, BIC, and SABIC values and a significant BLRT, they included a class representing < 5% of the sample, suggesting overextraction. The four-profile solution demonstrated strong model fit (significant LMRT and BLRT, high entropy), theoretical interpretability, and all class sizes ≥ 5%. Therefore, the four-profile model was selected as the optimal model. Insert Table 2 Here Table 2 Fit indices and class proportions for 1- to 6-profile models. Classes LL AIC BIC SABIC Entropy LMRT p-value BLRT p-value Profile proportions 1 -6568.520 13169.040 13238.820 13188.027 2 -6335.396 12720.792 12829.824 12750.459 0.733 < 0.001 0.05 > 0.05 0.08/0.64/0.28 4 -6136.500 12359.001 12546.537 12410.028 0.822 < 0.05 0.05 < 0.001 0.04/0.27/0.26/0.38/0.05 6 -6019.680 12161.360 12427.400 12233.749 0.845 < 0.05 < 0.001 0.02/0.04/0.26/0.25/0.05/0.38 Note: LL, Log-Likelihood; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; SABIC, Sample-Size Adjusted Bayesian Information Criterion; LMRT, Lo-Mendell-Rubin Adjusted Likelihood Ratio Test; BLRT, Bootstrap Likelihood Ratio Test. 3.3. Latent Profile Characteristics and Interpretation Figure 1 illustrates the standardized appetitive trait patterns across the four latent profiles. Most appetitive traits showed substantial variability between profiles, whereas enjoyment of food and food fussiness remained relatively similar across groups. Consistent with this visual pattern, Table 3 shows that all eight traits differed significantly across profiles ( p s < .010), with the largest effect sizes observed for emotional under-eating (η² = .72), emotional over-eating (η² = .51), hunger (η² = .50), food responsiveness (η² = .44), and satiety responsiveness (η² = .33). In contrast, differences in enjoyment of food and food fussiness, although statistically significant, had negligible effect sizes (η² = .02), indicating they contributed minimally to distinguishing profiles. Based on the traits showing meaningful variation, the four profiles were labeled as follows: Low Appetitive Traits ( n = 93; 16.1%), characterized by low scores on all food-approach and avoidance traits; Moderate Appetitive Traits with Emotional Under-Eating ( n = 168; 29.0%), with moderate levels across traits and elevated emotional under-eating; Moderate Appetitive Traits ( n = 260; 44.9%), reflecting moderate and balanced appetite characteristics; and High Conflicted Appetitive Traits ( n = 58; 10.0%), marked by simultaneously high food-approach and food-avoidance tendencies, reflecting ambivalence toward eating. Insert Table 3 Here Table 3 Descriptive statistics of the four latent profiles. Low Appetitive Traits Moderate Appetitive Traits with Emotional Under-eating Moderate Appetitive Traits High Conflicted Appetitive Traits 16.1% ( n = 93) Mean ± SD 29.0% ( n = 168) Mean ± SD 44.9% ( n = 260) Mean ± SD 10.0% ( n = 58) Mean ± SD F Partial η2 Enjoyment of food -0.23 ± 1.09 0.06 ± 1.06 -0.03 ± 0.95 0.37 ± 0.72 6.32 *** 0.02 Emotional over-eating -0.84 ± 0.74 -0.71 ± 0.59 0.50 ± 0.71 1.17 ± 0.85 193.81 ** 0.51 Food responsiveness -1.11 ± 0.85 -0.20 ± 0.77 0.20 ± 0.71 1.45 ± 0.72 140.18 *** 0.44 Hunger -1.02 ± 0.66 -0.41 ± 0.72 0.28 ± 0.72 1.58 ± 0.68 208.60 *** 0.50 Food fussiness -0.17 ± 1.13 -0.13 ± 1.04 0.16 ± 0.97 -0.07 ± 0.64 4.15 ** 0.02 Emotional under-eating -1.45 ± 0.63 0.99 ± 0.46 -0.29 ± 0.54 0.76 ± 0.45 472.37 *** 0.72 Slowness in eating -0.38 ± 0.92 0.00 ± 1.21 0.00 ± 0.88 0.60 ± 0.59 23.20 *** 0.06 Satiety responsiveness -0.81 ± 0.75 -0.14 ± 0.92 0.05 ± 0.82 1.46 ± 0.65 133.02 *** 0.33 Note: ** p < .01, *** p < .001. 3.4. Demographic Risk Factors for Latent Profiles Multinomial logistic regression (using a three-step method) examined whether demographic and oral health variables predicted membership in the four latent profiles (Table 4 ). Oral health scores differed significantly across the four profiles, with the highest scores in the Low Appetitive Traits profile, followed by Moderate Appetite with Emotional Under-Eating , Moderate Appetitive Traits , and High Conflicted Appetitive Traits ; correspondingly, poorer oral health was associated with a greater likelihood of belonging to the latter three profiles rather than the Low Appetitive Traits profile. Older age and rural residence were associated with higher odds of membership in the Moderate Appetite with Emotional Under-Eating profile relative to Low Appetitive Traits . Individuals without a spouse were more likely to belong to the High Conflicted Appetite Traits profile compared with both Low Appetitive Traits and Moderate Appetitive Traits . Higher education also increased the likelihood of membership in High Conflicted Appetite Traits relative to the two moderate appetite profiles. Sex and the number of natural teeth were not significantly associated with profile membership. Insert Table 4 here Table 4 Demographic variables associated with latent profile membership in the multinomial logistic regression model. ② vs ① ③ vs ① ④ vs ① ② vs ③ ② vs ④ ④ vs ③ B SE OR B SE OR B SE OR B SE OR B SE OR B SE OR Teeth Numbers 0.02 0.02 1.02 0.02 0.02 1.02 0.02 0.03 1.02 -0.002 0.01 1.00 -0.004 0.02 0.99 0.002 0.02 1.00 Oral Health -0.05 * 0.02 0.95 -0.13 *** 0.02 0.88 -0.22 *** 0.03 0.88 0.07 *** 0.02 1.08 0.16 *** 0.03 1.17 -0.09 ** 0.03 0.92 Age 0.01 0.02 1.01 -0.05 * 0.03 0.95 -0.02 0.03 0.98 0.06 ** 0.02 1.07 0.04 0.03 1.04 0.03 0.03 1.03 BMI -0.02 0.05 0.98 0.003 0.05 1.00 0.12 0.07 1.12 -0.03 0.04 0.97 -0.14 0.06 0.87 0.11 * 0.06 1.12 Sex Female 0.51 0.31 1.66 0.09 0.31 1.09 -0.22 0.47 0.81 0.42 0.27 1.53 0.72 0.44 2.06 -0.30 0.43 0.74 Residence Rural 0.96 * 0.45 2.61 0.34 0.39 1.40 0.59 0.70 1.81 0.62 0.44 1.86 0.37 0.72 1.44 0.26 0.66 1.29 Material status No spouse 0.64 0.40 1.90 0.38 0.41 1.46 1.33 * 0.53 3.77 0.27 0.29 1.31 -0.68 0.44 0.51 0.95 * 0.44 2.59 Education level Medium 0.96 0.60 2.62 1.05 0.54 2.84 1.20 0.92 3.32 -0.08 0.46 0.92 -0.24 0.83 0.79 0.16 0.81 1.17 High 0.49 0.80 1.63 -0.85 1.00 0.43 2.00 * 0.94 7.36 1.37 0.99 3.81 -1.51 0.81 0.22 2.84 ** 0.93 17.17 Note: Sex (0 = male, 1 = female); residence (0 = urban, 1 = rural); Material status (0 = having a spouse, 1 = no spouse); Education level (0 = low; 1 = medium; 2 = high); The comparison between latent groups is made with the latent group after “vs” as the reference; The “0” level of each demographic variable as the reference group for comparison; Abbreviations: OR, odds ratio; SE, approximate standard error. * p < .05., ** p < .01, *** p < .01. ① = Low Appetite Traits, ② = Moderate Appetitive Traits with Emotional Under-eating, ③ = Moderate Appetitive Traits, and ④ = High Conflicted Appetitive Traits. 3.5. Profile Differences in Health-Related Problems Table 5 presents the comparisons of the four appetitive trait patterns on various health-related measures. Significant differences were observed for psychological distress (K6: χ ² = 66.47, p < .001), health-related quality of life (SF-8: χ ² = 23.41, p < .001), oral health-related quality of life (GOHAI: χ ² = 88.96, p < .001) disordered eating symptoms (EDE-QS: χ ² = 115.80, p < .001), and Avoidant/Restrictive Food Intake Disorder symptoms (ARFID: χ ² = 82.36, p < .001). Significant group differences were also observed for three subscales of the NIAS: picky eating (χ² = 90.12, p < .001), low appetite (χ² = 64.95, p < .001), and fear of aversive consequences (χ² = 52.66, p < .001). In contrast, no significant differences were found for BMI ( χ ² = 2.07, p = 0.334). Insert Table 5 here Table 5 Profile differences in health-related problems. Low Appetitive Traits Moderate Appetitive Traits with Emotional Under-eating Moderate Appetitive Traits High Conflicted Appetitive Traits 16.1% ( n = 93) Mean ( SE ) 29.0% ( n = 168) Mean ( SE ) 44.9% ( n = 260) Mean ( SE ) 10.0% ( n = 58) Mean ( SE ) Approximate Chi-Square K6 3.70(0.41) a 5.46(0.34) b 6.82(0.33) c 9.99(0.77) d 66.47 *** SF-8 75.06(1.70) a 66.14 (1.40) b 67.30(0.99) b 63.29(2.36) b 23.41 *** EDE-QS 1.16(0.37) a 1.36(0.32) a 7.07(0.59) b 12.22(1.70) c 115.80 *** ARFID 12.67(0.81) a 15.01(0.60) b 17.70(0.43) c 25.81(1.36) d 82.36 *** Picky eating 3.66(0.30) a 3.90(0.24) a 5.64(0.18) b 8.00(0.49) c 90.12 *** Appetite 4.29(0.32) a 5.41(0.24) b 5.85(0.17) b 8.78(0.47) c 64.95 *** Fear 4.72(0.35) a 5.70(0.29) b 6.20(0.20) b 9.03(0.50) c 52.66 *** Oral Health 50.91(0.95) a 47.54(0.71) b 43.68(0.61) c 36.32(1.63) d 88.96 *** BMI 21.87 (0.37) a 21.30 (0.28) a 21.65 (0.22) a 22.27 (0.51) a 3.40 Note: Different superscript letters (a, b, c, d) within a row indicate significant pairwise differences between profiles at p < .05. SE, standard error, *** p < .001. Abbreviations: ARFID, Avoidant/Restrictive Food Intake Disorder; EDE-QS, The 12-item short form of the Eating Disorder Examination Questionnaire; K6, The Chinese version of the 6-item Kessler Psychological Distress Scale; SF-8, The SF-8 Health Survey; BMI, Body Mass Index. Specifically, for psychological distress, individuals in the High Conflicted Appetitive Traits group exhibited the highest levels of distress, exceeding those in the Low Appetitive Traits (χ² = 52.33, p < .001), Moderate Appetitive with Emotional Under-Eating (χ² = 28.78, p < .001), and Moderate Appetitive Traits groups (χ² = 13.30, p < .001). Moreover, the Moderate Appetitive Traits group reported significantly higher distress than the Moderate Appetitive with Emotional Under-Eating group (χ² = 7.05, p = .008), and the latter also exceeded the Low Appetitive Traits group (χ² = 10.71, p < .001). Regarding health-related quality of life (SF-8), the Low Appetitive Traits group reported the highest scores, significantly higher than those of the High Conflicted Appetitive Traits (χ² = 16.42, p < .001), Moderate Appetitive with Emotional Under-eating (χ² = 16.06, p < .001), and Moderate Appetitive Traits groups (χ² = 14.46, p .132). For disordered eating symptoms, both EDE-QS and ARFID scores revealed a clear gradient: the High Conflicted Appetitive Traits group scored significantly higher than all other groups (all p s < .007) on both measures. The Moderate Appetitive Traits group also scored higher than both the Moderate Appetitive with Emotional Under-eating and the Low Appetitive Traits groups (all p s < .001) on both measures. For EDE-QS scores, no significant difference emerged between the Moderate Appetitive with Emotional Under-eating and the Low Appetitive Traits groups (χ² = 0.16, p = .686). In contrast, for ARFID scores, the Moderate Appetitive with Emotional Under-eating group scored significantly higher than the Low Appetitive Traits group (χ² = 5.29, p = .022). 4. Discussion Older adults experience unique psychological, physiological, and social influences on appetite that differ markedly from those of the general population, highlighting the importance of examining their specific appetitive trait patterns and related health outcomes. To address gaps in prior research, the present study employed a person-centered approach, using LPA to identify distinct patterns of appetitive traits and examined their associations with health-related factors among older Chinese adults. Across profiles, the most clinically concerning pattern was the High Conflicted Appetitive Traits profile, which was characterized by elevated eating-related psychopathology and poorer psychosocial well-being, whereas the Low Appetitive Traits profile generally showed the most favorable quality-of-life indicators. Oral health emerged as a robust correlate of profile membership, highlighting the relevance of age-related functional factors in shaping appetite patterns in later life. Below, we interpret these profiles in relation to prior work and discuss implications for assessment and intervention. Among the four latent profiles, the largest group, Moderate Appetitive Traits , showed balanced levels of food approach and food avoidance traits, indicating relatively stable appetitive characteristics in later life. In contrast, the High Conflicted Appetitive Traits group displayed high scores in both food approach and food avoidance traits, except for food fussiness, reflecting ambivalent eating tendencies, and was linked to higher disordered eating, increased psychological distress, and poorer health-related and oral health-related quality of life. At the opposite end, the Low Appetitive Traits group reported the lowest scores across all food-approach and food-avoidance traits. These features align with the “anorexia of aging” phenomenon, where older adults naturally experience a decline in appetite. As people age, age-related changes in bodily function and metabolism may contribute to reduced appetite and greater food avoidance [ 35 , 66 ], thereby decreasing food enjoyment. Lastly, the Moderate Appetitive with Emotional Under-Eating group exhibited moderate avoidance traits and significant emotional under-eating, suggesting a need for further investigation into its dietary patterns and potential health risks. These patterns may also reflect the cultural context of Chinese older adults, for whom eating is strongly embedded in family and social life, potentially sustaining approach motivations even when aging-related constraints (e.g., sensory decline and oral discomfort) increase avoidance. Cohort-related experiences, such as historical food scarcity and frugal norms, may further heighten the salience of eating, contributing to ambivalent appetitive tendencies in later life. Comparison studies in children and young adults [ 39 , 41 ] suggest that the four-profile solution in older adults aligns with the latent profile structure observed across the lifespan, indicating that co-occurring food approach and avoidance traits are a consistent phenomenon. Similar to younger populations, older adults showed both a low-appetite subgroup and a high-ambivalence subgroup, with the latter resembling “food seekers and avoiders” patterns previously linked to emotional reactivity and disordered eating risk [ 39 ]. Consistent with the findings on latent profiles of negative emotional eating reported by He & Chen et al. [ 44 ], which focused solely on emotional under-eating and emotional over-eating, the present study also revealed similar emotional eating patterns, suggesting that negative emotional eating clusters into comparable configurations across age groups. Notably, emotional under-eating was more prominent among older adults, likely reflecting age-related changes in appetite regulation, psychosocial stressors, and physiological responses to negative effects. Additionally, differences across profiles in food fussiness were minor, implying that picky eating is less central to profile distinctions in later life. Overall, these results emphasize that while the overall latent structure remains consistent across ages, older adults display age-specific patterns, especially more pronounced emotional under-eating and greater stability in food fussiness. To explore and clarify the factors behind these age-specific appetitive profiles, multinomial logistic regression was conducted using sociodemographic and oral health variables. Demographic factors significantly correlated with latent appetitive trait profiles. Specifically, older adults from rural areas were more likely to belong to the Moderate Appetite with Emotional Under-Eating profile, potentially reflecting disparities in dietary environments and access to nutritional resources. Participants without a spouse were more likely to fall into the High Conflicted Appetitive Traits profile, which may indicate the role of social support in regulating eating behaviors and reducing psychological distress [ 10 ]. Moreover, better oral health was associated with a higher likelihood of being in the Low Appetitive Traits profile and a lower likelihood of being classified in the High Conflicted Appetitive Traits profile. This suggests that better oral health helps maintain consistent eating habits and a stable relationship with food, aligning with the “anorexia of aging” phenomenon in older adults [ 29 , 30 ]. Conversely, poorer oral health, such as chewing difficulties, pain, and discomfort, may contribute to more conflicted or dysregulated appetitive patterns. Notably, tooth count did not significantly predict profile membership, suggesting that self-perceived oral health may reflect functional and psychosocial aspects of oral conditions beyond tooth count alone. These findings highlight the impact of demographic vulnerabilities and oral health on older adults’ eating behaviors and nutritional status [ 36 , 37 ]. Interventions aimed at socially disadvantaged individuals and improving oral health could play a key role in reducing appetite-related risks and supporting healthy aging. Finally, the four appetitive trait profiles displayed distinct health-related patterns that mirror the physiological and psychological changes of aging [ 2 , 3 ]. The High Conflicted Appetitive Traits profile showed the highest levels of psychological distress and the most severe disordered eating and ARFID symptoms, similar to prior studies on the group with conflicted emotional eating patterns [ 44 , 67 , 68 ]. This pattern may reflect competing motivations: cue- and emotion-driven approach tendencies co-occurring with avoidance reinforced by negative effects and the perceived costs of eating (e.g., discomfort or fear of consequences). In later life, sensory decline and oral problems may further increase avoidance while leaving approach motivations relatively intact, thereby strengthening ambivalence and psychological burden. In contrast, the Low Appetitive Traits profile showed comparatively favorable well-being and quality-of-life indicators, consistent with a less emotionally reactive appetite pattern. Notably, BMI did not differ significantly across four profiles, likely reflecting age-related changes in body composition and muscle mass, which make weight a less sensitive indicator of nutritional risk in older adults [ 4 ]. Clinically, these findings suggest that screening for a conflicted appetitive pattern may help identify older adults who could benefit from integrated interventions that target emotion regulation and maladaptive eating cycles (e.g., CBT-informed coping skills, addressing avoidance/fear and cue-driven eating) alongside practical supports such as oral-health management and tailored dietary guidance to reduce eating-related discomfort and improve well-being. 4.1. Implications The significant differences in psychological distress and HRQoL across profiles highlight the potential need for pattern-specific interventions. Recognizing this heterogeneity in appetitive traits may enable clinicians and researchers to design tailored dietary and behavioral strategies that promote healthier eating and improve overall nutritional and health outcomes in older adults. In particular, the High Conflict Appetitive Traits group demonstrated the most severe psychological distress and disordered eating, indicating an urgent need for comprehensive support of older adults with complex and contradictory appetitive patterns. Programs that integrate nutritional counseling with oral health improvement, social support enhancement, and mental health care may be particularly effective for this high-risk subgroup. Furthermore, proactive strategies that mitigate food avoidance and foster enjoyment of food could help reduce appetite-related risks and support healthy aging. 4.2. Limitations and Future Research Directions Several limitations should be noted that guide future research directions. First, the present study is a cross-sectional design, which restricts the ability to infer temporality and causality between appetitive traits, oral health, and health-related outcomes. Longitudinal and experimental (e.g., intervention) studies are needed to clarify the temporal relationships and causal pathways among these variables. Relatedly, building on prior work using negative emotional eating profiles in adolescents [ 69 ], latent transition analysis should be conducted to determine whether older adults move between profiles across time and whether such transitions predict subsequent changes in psychological well-being, HRQoL, and eating-related psychopathology. Second, the study sample was drawn from a specific geographic and cultural context, which may limit the generalizability of the findings, as appetitive traits and dietary behaviors are influenced by cultural and environmental factors. Future research should conduct multisite and cross-cultural investigations to determine whether the latent profiles of appetitive traits observed in this study are consistent across different populations. Third, most variables were assessed using self-reports, which may have introduced inaccuracies and social desirability that could have biased our findings. Incorporating objective measures, such as clinical evaluations of oral health and nutritional biomarkers, would strengthen the robustness of future findings. Finally, although this study considered key demographic factors and oral health as correlates of profile membership, other relevant factors, such as medical conditions, especially in older adults, may play a crucial role in shaping appetite, but this study did not include these variables. Appetitive traits are multi-factorial, and as such, future studies should adopt a more comprehensive approach by integrating highly correlated variables to provide a holistic understanding of appetitive traits in older adults. 4.3. Conclusion In conclusion, this study identified four distinct appetitive trait profiles among older Chinese adults: Low Appetitive Traits , Moderate Appetitive with Emotional Under-eating , Moderate Appetitive Traits , and High Conflicted Appetitive Traits . Among these profiles, the High Conflict Appetitive Traits group exhibited the highest psychological distress and disordered eating, whereas the Low Appetitive Traits group demonstrated the most favorable health profile. Oral health and demographic characteristics, including age, marital status, residence, and educational level, were significantly associated with profile membership, highlighting the potential influence of social and functional factors on appetitive behaviors. These findings emphasize the importance of identifying high-risk appetitive patterns and developing targeted interventions that integrate nutritional guidance, oral health promotion, and psychosocial support to foster healthy aging. Abbreviations AEBQ Adult Eating Behavior Questionnaire AIC Akaike Information Criterion ARFID Avoidant/Restrictive Food Intake Disorder BCH Bolck–Croon–Hagenaars BIC Bayesian Information Criterion BLRT Bootstrap Likelihood Ratio Test BMI Body Mass Index BP Bodily Pain ED Eating Disorder EDE-QS Eating Disorder Examination Questionnaire Short Form GH General Health GOHAI Geriatric Oral Health Assessment Index HRQoL Health-Related Quality of Life K6 6-item Kessler Psychological Distress Scale LL Log-Likelihood LMRT Lo–Mendell–Rubin Adjusted Likelihood Ratio Test LPA Latent Profile Analysis M Mean MH Mental Health MLR Robust Maximum Likelihood Estimator NIAS Nine-Item ARFID Screen OR Odds Ratio PF Physical Function RE Role Emotional RP Role Physical SABIC Sample-Size Adjusted Bayesian Information Criterion SD Standard Deviation SE Standard Error SF Social Function SF-8 8-item Short Form Health Survey VT Vitality Declarations Conflicts of Interest The authors have no conflicts of interest to declare. Funding This study was supported by the Hunan Provincial Social Science Achievement Evaluation Committee General Project in 2023 (No. XSP24ZDI020). Author Contributions Gui Chen: Conceptualization, Funding acquisition, Project administration, Investigation, Supervision, Formal analysis, Writing – original draft, Writing – review & editing. Qiling Peng: Formal analysis, Writing – original draft, Writing – review & editing. Xingwei Luo: Writing – original draft; Writing – review & editing; Shaowu Li: Investigation, Writing – review & editing. Yunyi Cheng: Writing – original draft; Writing – review & editing. Urvashi Dixit: Writing – review & editing. Wesley R. Barnhart: Writing – original draft, Writing – review & editing. Jinbo He: Conceptualization, Supervision, Writing – original draft, Writing – review & editing. Ethical Approval Ethical approval was obtained from the Research Ethics Office at Hengyang Normal University (2023LL1230). 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Assessing Appetitive Traits Among Chinese Young Adults Using the Adult Eating Behavior Questionnaire: Factor Structure, Gender Invariance and Latent Mean Differences, and Associations With BMI. Assessment. 2021;28(3):877–89. 10.1177/1073191119864642 . Gideon N, Hawkes N, Mond J, Saunders R, Tchanturia K, Serpell L. Development and Psychometric Validation of the EDE-QS, a 12 Item Short Form of the Eating Disorder Examination Questionnaire (EDE-Q). PLoS ONE. 2016;11(5):e0152744. 10.1371/journal.pone.0152744 . He J, Sun S, Fan X. Validation of the 12-item Short Form of the Eating Disorder Examination Questionnaire in the Chinese context: confirmatory factor analysis and Rasch analysis. Eat Weight Disord. 2021;26(1):201–9. 10.1007/s40519-019-00840-3 . Barnhart WR, Cui T, Zhang H, Cui S, Zhao Y, Lu Y, He J. Examining an integrated sociocultural and objectification model of thinness- and muscularity-oriented disordered eating in Chinese older men and women. Int J Eat Disord. 2023;56(10):1875–86. 10.1002/eat.24017 . Zickgraf HF, Ellis JM. Initial validation of the Nine Item Avoidant/Restrictive Food Intake disorder screen (NIAS): A measure of three restrictive eating patterns. Appetite. 2018;123:32–42. 10.1016/j.appet.2017.11.111 . He J, Zickgraf HF, Ellis JM, Lin Z, Fan X. Chinese Version of the Nine Item ARFID Screen: Psychometric Properties and Cross-Cultural Measurement Invariance. Assessment. 2021;28(2):537–50. 10.1177/1073191120936359 . Turner-Bowker DM, Bayliss MS, Ware JE Jr., Kosinski M. Usefulness of the SF-8 Health Survey for comparing the impact of migraine and other conditions. Qual Life Res. 2003;12(8):1003–12. 10.1023/a:1026179517081 . Wang L, Palmer AJ, Cocker F, Sanderson K. Multimorbidity and health-related quality of life (HRQoL) in a nationally representative population sample: implications of count versus cluster method for defining multimorbidity on HRQoL. Health Qual Life Outcomes. 2017;15(1):7. 10.1186/s12955-016-0580-x . Lang L, Zhang L, Zhang P, Li Q, Bian J, Guo Y. Evaluating the reliability and validity of SF-8 with a large representative sample of urban Chinese. Health Qual Life Outcomes. 2018;16(1):55. 10.1186/s12955-018-0880-4 . Xing W, Chen X, Zhu J. The reliability of the SF-8 health survey in assessing health-related quality of life of the patient with coronary heart disease. Foreign Med Sci(Sect Cardiovasc Dis). 2004;31:181–4. Kessler RC, Green JG, Gruber MJ, Sampson NA, Bromet E, Cuitan M, et al. Screening for serious mental illness in the general population with the K6 screening scale: results from the WHO World Mental Health (WMH) survey initiative. Int J Methods Psychiatr Res. 2010;19(Suppl 1):4–22. 10.1002/mpr.310 . Zhang L, Li Z. A Mokken scale analysis of the Kessler-6 screening measure among Chinese older population: findings from a national survey. BMC Geriatr. 2020;20(1):361. 10.1186/s12877-020-01771-w . Wong MCM, Liu JKS, Lo ECM. Translation and Validation of the Chinese Version of GOHAI. J Public Health Dent. 2002;62(2):78–83. https://doi.org/10.1111/j.1752-7325.2002.tb03426.x . IBM Corp N. IBM SPSS statistics for windows. IBM corp Armonk, NY; 2019. Muthén LK, Muthén B. Mplus user's guide: Statistical analysis with latent variables, user's guide. Muthén & Muthén; 2017. Nylund KL, Asparouhov T, Muthén BO. Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study. Struct Equation Modeling: Multidisciplinary J. 2007;14(4):535–69. 10.1080/10705510701575396 . Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Routledge; 1988. Asparouhov T, Muthén B. Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using Mplus. Struct Equation Modeling: Multidisciplinary J. 2014;21(3):329–41. 10.1080/10705511.2014.915181 . Bolck A, Croon M, Hagenaars J. Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators. Political Anal. 2004;12(1):3–27. 10.1093/pan/mph001 . Doets EL, Kremer S. The silver sensory experience – A review of senior consumers’ food perception, liking and intake. Food Qual Prefer. 2016;48:316–32. https://doi.org/10.1016/j.foodqual.2015.08.010 . Dixit U, Barnhart WR, Ahlich EM, Henderson RR, Love AA, He J, Zickgraf HF. Characterizing emotional eating by valence, over-vs under-eating, and contextual factors: A latent profile analysis. Appetite. 2025;215:108235. 10.1016/j.appet.2025.108235 . Dixit U, He J, Whited M, Ellis JM, Zickgraf HF. Negative emotional eating patterns among American university students: A replication study. Appetite. 2023;186:106554. 10.1016/j.appet.2023.106554 . Weng H, Barnhart WR, Zickgraf HF, Dixit U, Cheng Y, Chen G, He J. Negative emotional eating patterns in Chinese adolescents: A replication and longitudinal extension with latent profile and transition analyses. Appetite. 2025;204:107728. 10.1016/j.appet.2024.107728 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 01 Apr, 2026 Reviewers invited by journal 31 Mar, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 19 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9173767","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616631205,"identity":"3e63a8c6-14b4-489d-8f41-c25bb5baecc5","order_by":0,"name":"Gui Chen","email":"","orcid":"","institution":"Hengyang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Gui","middleName":"","lastName":"Chen","suffix":""},{"id":616631206,"identity":"74ef9a92-b6ca-4a61-93bb-d5faff1a0309","order_by":1,"name":"Qiling Peng","email":"","orcid":"","institution":"Hengyang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Qiling","middleName":"","lastName":"Peng","suffix":""},{"id":616631207,"identity":"8ecd95a3-232e-42f6-bda7-3a8b3c0b0ff4","order_by":2,"name":"Xingwei Luo","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xingwei","middleName":"","lastName":"Luo","suffix":""},{"id":616631208,"identity":"d160ee61-fb4d-4946-875f-d1224b1ba449","order_by":3,"name":"Shaowu Li","email":"","orcid":"","institution":"Hengyang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Shaowu","middleName":"","lastName":"Li","suffix":""},{"id":616631209,"identity":"ff8afffa-ee9b-475a-8568-34527833e8bf","order_by":4,"name":"Yunyi Cheng","email":"","orcid":"","institution":"Xi'an Jiaotong-Liverpool University","correspondingAuthor":false,"prefix":"","firstName":"Yunyi","middleName":"","lastName":"Cheng","suffix":""},{"id":616631211,"identity":"40c70168-60d6-40ee-8642-9c2071171aa5","order_by":5,"name":"Urvashi Dixit","email":"","orcid":"","institution":"University of South Alabama","correspondingAuthor":false,"prefix":"","firstName":"Urvashi","middleName":"","lastName":"Dixit","suffix":""},{"id":616631213,"identity":"cf93efc9-797f-4cee-8b5e-5236ee315c65","order_by":6,"name":"Wesley R. Barnhart","email":"","orcid":"","institution":"Suffolk University","correspondingAuthor":false,"prefix":"","firstName":"Wesley","middleName":"R.","lastName":"Barnhart","suffix":""},{"id":616631214,"identity":"400ff199-d1df-4408-ab50-5e5f38f73a1d","order_by":7,"name":"Jinbo He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYFAC5obPDAw2QAZj4wEitTA2zmZgSAMxGkjSchjMJE4L/+zGxuaCivN2a9sPA22psYkmqEXizsHG5hlnbidvO5MI1HIsLbeBoJ4bie2PedtuJ5sdAGphbDhMWIv8jcTGZt5/55LNzj8kUosBWEvDATuzG8TaYgjSwnMsOcHsBtCWBGL8Incj+WAzT42dvdn59IcPPtTYEOF9KEgEq0wgVjkI2JOieBSMglEwCkYYAADapUvwFawN/wAAAABJRU5ErkJggg==","orcid":"","institution":"Xi'an Jiaotong-Liverpool University","correspondingAuthor":true,"prefix":"","firstName":"Jinbo","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2026-03-20 01:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9173767/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9173767/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106251151,"identity":"747c4382-6f09-487e-9fba-3b46eb5ae2b7","added_by":"auto","created_at":"2026-04-06 17:22:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62664,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDescription of the selected four latent appetitive profiles.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9173767/v1/e3ad198e481fc3044d27fcf2.png"},{"id":106403715,"identity":"aea37b83-6ee2-4053-b3e3-c5f1e3b8607c","added_by":"auto","created_at":"2026-04-08 09:14:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1701549,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9173767/v1/50a15cb9-8563-40a8-91a9-7d59eea848b3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Appetitive Trait Profiles in Late Life: Links to Eating Disorder Psychopathology and Psychosocial Well-Being","fulltext":[{"header":"Plain English Summary","content":"\u003cp\u003eLittle is known about appetitive traits and their health connections among older adults. In this study, we surveyed 579 Chinese adults aged 60 to 93 years and identified four distinct appetitive trait profiles. These profiles differed in both eating-related symptoms and well-being. Older adults in the High Conflicted Appetitive Traits group showed the highest levels of eating disorder symptoms, ARFID symptoms, psychological distress, and lower quality of life. In contrast, those in the Low Appetitive Traits group generally experienced better health outcomes. Oral health was also associated with specific appetite profiles. These findings have implications for recognizing higher-risk profiles to design tailored interventions for appetitive traits to improve eating behaviors and well-being of older adults.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eAppetite plays a central role in regulating dietary intake by shaping food-related bodily cues (e.g., hunger and satiety cues) and food preferences [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In older adulthood, notable physiological changes occur, including reduced sensitivity to taste and smell, altered gastrointestinal function, and oral health problems such as tooth loss, which can significantly impact older adults\u0026rsquo; appetite and eating patterns [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These shifts may compromise nutritional intake and increase vulnerability to adverse health outcomes, including physical frailty, poor mental health, and diminished quality of life [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. China, like many countries globally, is facing a rapidly aging population [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Because nutrition is a modifiable determinant of health in later life, promoting healthy eating behaviors among older adults is central to supporting active aging and reducing age-related declines in health [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, it is crucial to understand the intricate relationships between appetitive traits and health outcomes in aging populations to develop targeted interventions that address older adults\u0026rsquo; health needs.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Appetitive Traits and Health Outcomes\u003c/h2\u003e \u003cp\u003eAppetitive traits are biologically based tendencies shaped and modified by environmental influences, and together these factors account for individual differences in eating behaviors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. To facilitate a comprehensive assessment of appetitive traits in adults, Hunot et al. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] developed the Adult Eating Behavior Questionnaire (AEBQ), which captures eight dimensions, including four food approach appetitive traits (hunger, food responsiveness, emotional over-eating, enjoyment of food) and four food avoidance appetitive traits (satiety responsiveness, emotional undereating, food fussiness, and slowness in eating). Related research on appetitive traits suggests that they are associated with both the quantity and quality of dietary intake. For instance, picky eating, external eating, and emotional overeating are more strongly linked to consumption of energy-dense foods such as high-sugar and high-fat foods [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], whereas enjoyment of food is associated with healthier dietary patterns rich in fruits, vegetables, and protein [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, two traits, food responsiveness and satiety responsiveness, are particularly relevant to food intake: higher food responsiveness is associated with more frequent eating, whereas lower satiety responsiveness is linked to reduced food intake [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch on children and adolescents consistently demonstrates significant associations between appetitive traits, eating disorders, and body mass index (BMI) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For example, a systematic review of 27 studies using child and baby eating behavior questionnaires found that food-approach appetitive traits, reflecting heightened responsiveness to external food cues, are associated with higher BMI z-scores, whereas food-avoidance traits, characterized by reduced or restrictive appetite, are associated with lower BMI z-scores [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Beyond weight outcomes, growing longitudinal evidence suggests that appetitive traits are also implicated in the development of eating disorder symptoms across the lifespan. For example, a large European longitudinal study found that greater food responsiveness in early childhood was associated with higher odds of disordered eating behaviors (e.g., binge eating) in adolescence [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Extending this evidence to later life, research in Chinese older adults has shown that higher emotional under-eating and greater satiety responsiveness at baseline were prospectively associated with higher eating disorder symptomatology at one-year follow-up [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Across age groups, appetitive traits also play a critical role in shaping individuals\u0026rsquo; emotional states and overall mental well-being. In the general population, traits such as emotional eating and external eating have been linked to mental health conditions, including depression and anxiety symptoms and greater loneliness and stress [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Emotional eating often serves as a maladaptive coping mechanism to manage negative emotions; however, rather than alleviating these emotions, it frequently exacerbates them, creating a cycle of emotional distress [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Appetite Traits Change with Age\u003c/h2\u003e \u003cp\u003eAppetitive traits develop early in childhood and often remain relatively stable over time [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, aging brings marked changes in certain traits, such as food fussiness, food responsiveness, and enjoyment of food, driven by biological, psychological, and environmental influences [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For example, children often exhibit higher levels of food responsiveness and food enjoyment, driven by their increased growth and energy needs. However, with age, these traits often decline due to metabolic changes, reduced sensory acuity, and shifts in taste preferences [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, older adults often experience loss of appetite associated with hormonal changes, reduced sensory perception, and altered taste functioning [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This age-related decline in appetite, often referred to as the \u0026ldquo;anorexia of aging,\u0026rdquo; is characterized by reduced motivation to eat and increased food avoidance, which can contribute to reduced energy intake, increased risk of malnutrition, and undesirable weight loss [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Appetite may also decrease transiently during acute illness, further compounding nutritional vulnerability. Beyond these biological changes, appetite and eating behaviors in later life are often compounded by psychological and social factors, such as depression symptoms, social isolation, and chronic health conditions, which further affect eating behaviors and food intake [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. As a result of these interrelated physiological, psychological, and social influences, older adults often exhibit reduced energy intake and altered dietary patterns, including a noticeable shift in food preferences, typically away from sweet or high-calorie food, reflecting age-related changes in appetitive traits [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Oral health plays a particularly important role: chewing difficulties, tooth loss, or denture problems can make eating less enjoyable and lead to food-avoidance behaviors [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These shifts may result in lower energy intake, poorer diet quality, and increased nutritional vulnerability. Despite this, little is known about how combinations of appetitive traits manifest in older adults.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3. A Latent Profile Perspective on Appetitive Traits\u003c/h2\u003e \u003cp\u003eAlthough much is known about individual appetitive traits, people often exhibit co-occurring tendencies (e.g., high food responsiveness combined with emotional undereating). Person-centered methods, such as latent profile analysis (LPA), enable researchers to capture these complex combinations by identifying subgroups that share similar trait patterns. Previous LPA studies, mostly in children, adolescents, and young adults, have revealed heterogeneous profiles that are differentially associated with outcomes such as dietary behaviors, weight status, and psychosocial well-being [\u003cspan additionalcitationids=\"CR39 CR40 CR41 CR42 CR43\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. For example, some studies focused on emotional eating traits, such as emotional overeating and undereating [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], while others concentrated on traits related to food avoidance and picky eating [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Although these studies provide valuable insights into specific aspects of eating behavior, they may not capture the full complexity of appetitive traits across a broader spectrum. In contrast, few studies have examined the full range of appetitive traits using the AEBQ [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Existing research has identified varying numbers of latent classes across samples, suggesting that latent profile patterns may differ by population. For instance, a study conducted by Coakley et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] identified four latent classes among university students, while a study by Chen et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] found only three latent classes in a sample of final-year students. These findings collectively underscore the population-specific variability and diversity of appetitive traits. To our knowledge, no studies have examined latent profiles of appetitive traits among older adults to date, despite the unique physiological and social contexts of this population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4. The Present Study\u003c/h2\u003e \u003cp\u003eThis study sought to address these gaps by applying LPA to a community sample of Chinese older adults. Specifically, our objectives were threefold. First, we identified latent subgroups based on eight appetitive traits assessed by the AEBQ [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Second, we examined demographic and oral health factors associated with profile membership. Third, we examined whether the identified profiles differed in disordered eating symptoms, avoidant/restrictive food intake disorder (ARFID) symptoms, psychological distress, health-related quality of life (HRQoL), and BMI. By integrating demographic, psychological, and oral health perspectives, this study provides an initial characterization of appetite patterns in later life and offers preliminary evidence to inform future longitudinal research and the development of targeted interventions to support healthy eating and psychosocial well-being in aging populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Procedures and Participants\u003c/h2\u003e \u003cp\u003eThe study was approved by the Research Ethics Committee of [\u003cem\u003eredacted for peer review\u003c/em\u003e] (No. \u003cem\u003eredacted for peer review\u003c/em\u003e), and all participants provided informed consent. Older adults were recruited through face-to-face household interviews conducted by trained psychology students following standardized administration procedures. During these visits, participants completed the questionnaires, and interviewers provided standardized instructions and assistance as needed (e.g., clarifying item wording) without influencing responses. Inclusion criteria were (a) age \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 60 years, consistent with commonly used demographic conventions that aligned with China\u0026rsquo;s legal definition of \u0026ldquo;older persons\u0026rdquo; as citizens aged 60 years and above (National People\u0026rsquo;s Congress Standing Committee, 2018), and (b) ability to complete the survey independently. Individuals with mobility limitations or communication difficulties were excluded to minimize participant burden. Each interview lasted approximately 60 minutes. A total of 612 individuals were approached for the study. Of these, 25 were excluded due to incomplete questionnaires (i.e., more than one scale was not completed), and eight were younger than 60 years, leaving 579 valid cases (320 women, 55.3%; 259 men, 44.7%), aged 60\u0026ndash;93 years (\u003cem\u003eM\u003c/em\u003e = 71.02, \u003cem\u003eSD\u003c/em\u003e = 7.96). Additional demographic characteristics included marital status (currently married or co-habiting: \u003cem\u003en\u003c/em\u003e = 429, 74.1%; without a spouse: \u003cem\u003en\u003c/em\u003e = 150, 25.9%), occupational status (retired: \u003cem\u003en\u003c/em\u003e = 423, 73.1%; employed [any current paid work, part-time or full-time]: \u003cem\u003en\u003c/em\u003e = 156, 26.9%), residence (rural: \u003cem\u003en\u003c/em\u003e = 501, 86.5%; urban: \u003cem\u003en\u003c/em\u003e = 78, 13.5%), and educational attainment (Low, illiterate, or primary-school education: \u003cem\u003en\u003c/em\u003e = 381, 65.8%; Medium, middle- or high-school education: \u003cem\u003en\u003c/em\u003e = 182, 31.4%; High, university degree or higher: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16, 2.8%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Measures\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Appetitive Traits\u003c/h2\u003e \u003cp\u003eThe Adult Eating Behavior Questionnaire (AEBQ) is a 35-item self-report questionnaire assessing appetitive traits in adults [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It includes eight subscales: four for food-approach traits (Hunger, Enjoyment of Food, Emotional Over-Eating, and Food Responsiveness) and four food-avoidance traits (Slowness in Eating, Emotional Under-Eating, Satiety Responsiveness, and Food Fussiness). The scale uses a five-point response scale, ranging from 1 (strongly disagree) to 5 (strongly agree). A mean score for each subscale was calculated, with higher scores indicating greater endorsement of appetitive traits. The AEBQ demonstrated good reliability and validity in both Western [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and Chinese contexts [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and has been used in Chinese older adults [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the present study, the Cronbach\u0026rsquo;s α values for the subscales of Enjoyment of Food, Hunger, Emotional Over-Eating, Food Responsiveness, Food Fussiness, Emotional Under-Eating, Slowness in Eating, and Satiety Responsiveness were 0.78, 0.85, 0.94, 0.68, 0.60, 0.95, 0.68, and 0.75, respectively. The eight AEBQ scales were used to identify latent profiles using LPA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Eating Disorder Symptoms\u003c/h2\u003e \u003cp\u003eIn this study, two validated tools were used to assess ED symptoms. The 12-item short form of the Eating Disorder Examination Questionnaire (EDE-QS) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] was used to assess traditional, thinness-oriented ED symptoms. The EDE-QS consists of 12 items; each rated on a 4-point response scale ranging from 0 (0 day/never) to 3 (6\u0026ndash;7 days/markedly). Scores of all items are summed to yield a total score, with a higher score indicating a higher level of traditional, thinness-oriented ED symptomatology. The Chinese version of the EDE-QS was validated in Chinese samples [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and has been used in older Chinese adults [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Cronbach\u0026rsquo;s α for the present older adult sample was 0.88.\u003c/p\u003e \u003cp\u003eThe Nine-Item ARFID Screen (NIAS; [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]) was used to assess symptoms of Avoidant/Restrictive Food Intake Disorder (ARFID). The NIAS comprises nine items to evaluate the core presentations of ARFID (avoidance of foods, poor appetite, and fear of negative consequences) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Each item is rated on a 5-point Likert scale ranging from 0 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater severity of ARFID symptoms. Scores for each subscale are calculated by summing the responses to the corresponding items, and a total NIAS score is derived by summing all item scores. The NIAS has been validated in Chinese samples [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and used with Chinese older adults [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the present study, the Chinese version of the NIAS, validated by He and colleagues [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], was used, with Cronbach\u0026rsquo;s alphas of 0.89, 0.79, 0.77, and 0.85 for the total scale, picky eating, appetite, and fear subscales, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Health-Related Quality of Life\u003c/h2\u003e \u003cp\u003eThe SF-8 Health Survey (SF-8) was used to assess participants\u0026rsquo; health-related quality of life (HRQoL). The eight items cover eight separate components of HRQoL named general health (GH; overall perceived health), physical function (PF; ability to perform physical activities), role physical (RP; limitations of usual daily activities due to physical health problems), bodily pain (BP; pain intensity), vitality (VT; energy level, e.g., feeling energetic versus tired), social function (SF; interference of health with social interactions with family and friends), mental health (MH; psychological distress and well-being), and role emotional (RE; limitations in usual daily activities or housework due to emotional problems) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The Chinese version of SF-8 also demonstrated good psychometric properties in Chinese general populations [\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], including older adults [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor scoring, the original score of all items can be converted to the percentage standard score, according to the following scoring formula [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]: (\u003cem\u003ex\u003c/em\u003e - min) / (max - min) \u0026times; 100, where \u003cem\u003ex\u003c/em\u003e is the observed score, and min and max are the lowest and highest possible values for that item. Higher scores indicate better health for that component. The average percentage standard score across eight items was used to compute the total score, with higher scores indicating higher HRQoL [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In this study, the Cronbach's α for the total scale was 0.87.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4. Psychological Distress\u003c/h2\u003e \u003cp\u003eThe Chinese version of the 6-item Kessler Psychological Distress Scale (K6) was used to assess psychological distress symptoms (e.g., nervousness, worthlessness, and hopelessness) during the past 30 days [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Each item is rated on a 5-point scale, with scores ranging from 0 (none of the time) to 4 (all the time). Summing the six items yields a total score range of 0 to 24, with higher scores indicating greater psychological distress. The Chinese version of the K6 showed good psychometric properties in multicultural environments [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], including application to Chinese older adults [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In this study, the K6 had a Cronbach's\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\text{a}\\text{l}\\text{p}\\text{h}\\text{a}\\:\\)\u003c/span\u003e\u003c/span\u003eof 0.87.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5. Geriatric Oral Health\u003c/h2\u003e \u003cp\u003eThe Chinese version of the Geriatric Oral Health Assessment Index Scale (GOHAI) was used to assess the oral health-related quality of life among older adults. The GOHAI contains 12 items that evaluate three dimensions: physical functioning (4 items), pain and discomfort (3 items), and psychosocial functioning (5 items). Participants respond on a 5-point response scale ranging from 1 (always) to 5 (never). The GOHAI score was calculated by summing the scores of 12 items, with higher scores indicating better oral health status. The Chinese version of GOHAI demonstrated acceptable reliability and validity [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In this study, the Chinese version yielded a Cronbach\u0026rsquo;s\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\alpha\\:\\:\\)\u003c/span\u003e\u003c/span\u003eof 0.92.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6. Demographic Variables\u003c/h2\u003e \u003cp\u003eA self-administered questionnaire was developed for this study to collect participants\u0026rsquo; demographic and oral health information, including sex, age, marital status, occupational status, residence, education, and the number of natural teeth. In the original questionnaire, marital status was categorized as \u0026ldquo;single,\u0026rdquo; \u0026ldquo;married,\u0026rdquo; \u0026ldquo;divorced,\u0026rdquo; and \u0026ldquo;widowed\u0026rdquo;; education had four levels: \u0026ldquo;illiterate or primary school,\u0026rdquo; \u0026ldquo;junior high school,\u0026rdquo; \u0026ldquo;senior high school or secondary school\u0026rdquo;, and \u0026ldquo;university and above\u0026rdquo;; occupational status included \u0026ldquo;retired,\u0026rdquo; \u0026ldquo;working,\u0026rdquo; and \u0026ldquo;employed\u0026rdquo;; and residence was classified as \u0026ldquo;urban\u0026rdquo; or \u0026ldquo;rural.\u0026rdquo; When analyzing these data, \u0026ldquo;single,\u0026rdquo; \u0026ldquo;divorced,\u0026rdquo; and \u0026ldquo;widowed\u0026rdquo; were combined into \u0026ldquo;no spouse,\u0026rdquo; the occupational statuses of \u0026ldquo;working\u0026rdquo; and \u0026ldquo;employed\u0026rdquo; were combined into \u0026ldquo;employed,\u0026rdquo; and educational levels were merged into three ordered categories: low (\u0026ldquo;illiterate or primary school\u0026rdquo; and \u0026ldquo;junior high school\u0026rdquo;), medium (\u0026ldquo;senior high school or secondary school\u0026rdquo;), and high (\u0026ldquo;university and above\u0026rdquo;).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data Analysis\u003c/h2\u003e \u003cp\u003eAnalyses were conducted using SPSS 26.0 [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] and Mplus 8.3 [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Descriptive statistics (means, standard deviations, and ranges) were computed for study variables. LPA was then conducted using the robust maximum likelihood estimator (MLR). To ensure the highest log-likelihood value was discovered during model estimation, we used 500 random starts and 20 iterations. Model selection was based on multiple criteria: lower AIC, BIC, and SABIC values, LMRT and BLRT tests, and entropy values [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Specifically, lower values of AIC, BIC, and SABIC indicate a better model fit. Entropy is an indicator of classification accuracy; a higher value indicates greater accuracy in this classification. The LMRT and BLRT are the significance tests that compare the k-class model to the k-1 class. If \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.5, it indicates the k-class model is a better fit. Solutions with classes representing\u0026thinsp;\u0026lt;\u0026thinsp;5% of the sample were rejected as spurious [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. After selecting the best model, latent appetitive trait patterns were named based on their score characteristics. Differences across the eight subscales of the Adult Eating Behavior Questionnaire (AEBQ) among latent appetitive trait profiles were examined using one-way analysis of variance (ANOVA), with effect sizes reported for significant findings. Partial eta-squares (η2) were used for the difference effect size, for which values of 0.01, 0.06, and 0.14 were reflective of small, medium, and large effect sizes, respectively [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Then, a robust three-step approach using multinomial logistic regression, proposed by Asparouhov and Muth\u0026eacute;n [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], was used to examine the demographic risk factors associated with potential appetitive trait patterns. The resulting odds ratios quantify the extent to which each covariate increases or decreases the likelihood of belonging to a particular profile relative to the reference profile, thereby identifying salient risk factors for each latent profile. Lastly, we employed the Bolck-Croon-Hagenaars method (BCH; [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]), a three-step weighting approach for testing distal outcome differences across latent profiles while accounting for classification uncertainty, to investigate whether the potential profiles differed in terms of health-related outcomes. Global Wald χ\u0026sup2; tests evaluated overall differences among potential appetitive traits patterns, followed by pair-wise contrasts to pinpoint specific between-class disparities in health-related outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Declaration of Generative AI in Scientific Writing\u003c/h2\u003e \u003cp\u003eDuring the preparation of this work, the authors used ChatGPT 5.2 to improve the language of the manuscript. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the content of the published article.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Descriptive Statistics\u003c/h2\u003e\n \u003cp\u003eDescriptive statistics for all study variables and demographic characteristics are presented in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eInsert Table 1 Here\u003c/em\u003e\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic characteristics and descriptive statistics by sex.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eWomen (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;259)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMen (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;320)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e/\u003cem\u003et\u003c/em\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eEffect size \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e(%)/\u003cem\u003eM\u003c/em\u003e(\u003cem\u003eSD\u003c/em\u003e) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e(%)/\u003cem\u003eM\u003c/em\u003e(\u003cem\u003eSD\u003c/em\u003e) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e(%)/\u003cem\u003eM\u003c/em\u003e(\u003cem\u003eSD\u003c/em\u003e) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e71.38 (7.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e71.74 (7.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e71.08 (7.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e21.65 (3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e21.840 (3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e21.49 (3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMaterial status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e26.73\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHave no spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e429 (74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e210 (65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e219 (84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHave a spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e150 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e110 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e40 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e501(13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e221 (85.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e280 (87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e78 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e38 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e40 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e424 (73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e186 (71.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e238 (74.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIn employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e155 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e73 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e82 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e63.45\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e381 (65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e126 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e255 (79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e125 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e79 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e46 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e73 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e54 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e19 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAEBQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e98.51 (16.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e98.96 (17.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e98.07 (14.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEnjoyment of food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e10.62 (2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e10.58 (2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e10.65 (2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEmotional over-eating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e12.63 (4.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e13.11 (5.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12.14 (3.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e2.57*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFood responsiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e11.26 (2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e11.17 (2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e11.35 (3.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHunger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e13.24 (5.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e13.41 (5.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12.60 (4.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFood fussiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e13.58 (2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e13.46 (2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e13.69 (2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEmotional under-eating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e15.51 (4.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e15.42 (4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e15.59 (5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSlowness in eating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e11.57 (3.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e11.49 (2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e11.65 (4.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSatiety responsiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e10.43 (3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e10.42 (3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e10.44 (2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOral health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e26.24 (8.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e26.09 (9.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e26.39 (8.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNumber of teeth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e19.81 (10.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e20.22 (10.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e19.40 (9.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eK6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e6.27 (4.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e6.46 (4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e6.09 (4.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSF-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e67.92 (15.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e69.22 (15.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e66.62 (15.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e2.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEDE-QS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e5.086 (7.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e6.05 (9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e4.15 (6.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e2.80\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNIAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e16.96 (7.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e16.97(7.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e16.94 (7.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePicky eating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e5.074 (2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e5.12 (3.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e5.02 (2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAppetite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e5.77 (2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e5.69 (2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e5.84 (2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e6.12 (3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e6.15 (3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e6.08 (3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: \u003csup\u003ea\u003c/sup\u003e % for categorical variables, means and standard deviations for continuous variables; \u003csup\u003eb\u003c/sup\u003e Chi-squared tests for categorical variables, independent t-tests for continuous variables. \u003csup\u003ec\u003c/sup\u003e NNT for categorical variables, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e for continuous variables. \u003csup\u003ed\u003c/sup\u003e Calculated based on self-reported height and weight. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .001. AEBQ, The Adult Eating Behavior Questionnaire; NIAS, Nine Item for ARFID Scale; EDE-QS, The 12-item short form of the Eating Disorder Examination Questionnaire; K6, The Chinese version of the 6-item Kessler Psychological Distress Scale; SF-8, The SF-8 Health Survey; M, Mean; SD, Standard Deviation; BMI, Body Mass Index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Latent Profile Identification\u003c/h2\u003e\n \u003cp\u003eLatent profile analysis models with one to six profiles were tested. Fit indices are shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Although the five- and six-profile models demonstrated lower AIC, BIC, and SABIC values and a significant BLRT, they included a class representing\u0026thinsp;\u0026lt;\u0026thinsp;5% of the sample, suggesting overextraction. The four-profile solution demonstrated strong model fit (significant LMRT and BLRT, high entropy), theoretical interpretability, and all class sizes\u0026thinsp;\u0026ge;\u0026thinsp;5%. Therefore, the four-profile model was selected as the optimal model.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eInsert Table 2 Here\u003c/em\u003e\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFit indices and class proportions for 1- to 6-profile models.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eClasses\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSABIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eLMRT\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eBLRT\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eProfile proportions\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-6568.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e13169.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e13238.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e13188.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-6335.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e12720.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12829.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e12750.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.61/0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-6226.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e12520.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12668.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e12560.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.08/0.64/0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-6136.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e12359.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12546.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e12410.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.16/0.29/0.45/0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-6072.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e12248.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12475.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e12310.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.04/0.27/0.26/0.38/0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-6019.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e12161.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e12427.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e12233.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.02/0.04/0.26/0.25/0.05/0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eNote: LL, Log-Likelihood; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; SABIC, Sample-Size Adjusted Bayesian Information Criterion; LMRT, Lo-Mendell-Rubin Adjusted Likelihood Ratio Test; BLRT, Bootstrap Likelihood Ratio Test.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Latent Profile Characteristics and Interpretation\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the standardized appetitive trait patterns across the four latent profiles. Most appetitive traits showed substantial variability between profiles, whereas enjoyment of food and food fussiness remained relatively similar across groups. Consistent with this visual pattern, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that all eight traits differed significantly across profiles (\u003cem\u003ep\u003c/em\u003es \u0026lt; .010), with the largest effect sizes observed for emotional under-eating (\u0026eta;\u0026sup2; = .72), emotional over-eating (\u0026eta;\u0026sup2; = .51), hunger (\u0026eta;\u0026sup2; = .50), food responsiveness (\u0026eta;\u0026sup2; = .44), and satiety responsiveness (\u0026eta;\u0026sup2; = .33). In contrast, differences in enjoyment of food and food fussiness, although statistically significant, had negligible effect sizes (\u0026eta;\u0026sup2; = .02), indicating they contributed minimally to distinguishing profiles. Based on the traits showing meaningful variation, the four profiles were labeled as follows: \u003cem\u003eLow Appetitive Traits\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;93; 16.1%), characterized by low scores on all food-approach and avoidance traits; \u003cem\u003eModerate Appetitive Traits with Emotional Under-Eating\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;168; 29.0%), with moderate levels across traits and elevated emotional under-eating; \u003cem\u003eModerate Appetitive Traits\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;260; 44.9%), reflecting moderate and balanced appetite characteristics; and \u003cem\u003eHigh Conflicted Appetitive Traits\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;58; 10.0%), marked by simultaneously high food-approach and food-avoidance tendencies, reflecting ambivalence toward eating.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eInsert Table 3 Here\u003c/em\u003e\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics of the four latent profiles.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLow Appetitive Traits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eModerate Appetitive Traits with Emotional Under-eating\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eModerate Appetitive Traits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eHigh Conflicted Appetitive Traits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e16.1% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e29.0% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;168)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e44.9% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;260)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e10.0% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cem\u003ePartial \u0026eta;2\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEnjoyment of food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e\n \u003cp\u003e-0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e\n \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c4\"\u003e\n \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e\n \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e6.32\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEmotional over-eating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e\n \u003cp\u003e-0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e\n \u003cp\u003e-0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c4\"\u003e\n \u003cp\u003e0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e\n \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e193.81\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFood responsiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e\n \u003cp\u003e-1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e\n \u003cp\u003e-0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c4\"\u003e\n \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e\n \u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e140.18\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHunger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e\n \u003cp\u003e-1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e\n \u003cp\u003e-0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c4\"\u003e\n \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e\n \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e208.60\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFood fussiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e\n \u003cp\u003e-0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e\n \u003cp\u003e-0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c4\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e\n \u003cp\u003e-0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e4.15\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEmotional under-eating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e\n \u003cp\u003e-1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e\n \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c4\"\u003e\n \u003cp\u003e-0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e\n \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e472.37\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSlowness in eating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e\n \u003cp\u003e-0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e\n \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c4\"\u003e\n \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e\n \u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e23.20\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSatiety responsiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e\n \u003cp\u003e-0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e\n \u003cp\u003e-0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c4\"\u003e\n \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e\n \u003cp\u003e1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e133.02\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Demographic Risk Factors for Latent Profiles\u003c/h2\u003e\n \u003cp\u003eMultinomial logistic regression (using a three-step method) examined whether demographic and oral health variables predicted membership in the four latent profiles (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Oral health scores differed significantly across the four profiles, with the highest scores in the \u003cem\u003eLow Appetitive Traits\u003c/em\u003e profile, followed by \u003cem\u003eModerate Appetite with Emotional Under-Eating\u003c/em\u003e, \u003cem\u003eModerate Appetitive Traits\u003c/em\u003e, and \u003cem\u003eHigh Conflicted Appetitive Traits\u003c/em\u003e; correspondingly, poorer oral health was associated with a greater likelihood of belonging to the latter three profiles rather than the \u003cem\u003eLow Appetitive Traits\u003c/em\u003e profile. Older age and rural residence were associated with higher odds of membership in the \u003cem\u003eModerate Appetite with Emotional Under-Eating\u003c/em\u003e profile relative to \u003cem\u003eLow Appetitive Traits\u003c/em\u003e. Individuals without a spouse were more likely to belong to the \u003cem\u003eHigh Conflicted Appetite Traits\u003c/em\u003e profile compared with both \u003cem\u003eLow Appetitive Traits\u003c/em\u003e and \u003cem\u003eModerate Appetitive Traits\u003c/em\u003e. Higher education also increased the likelihood of membership in \u003cem\u003eHigh Conflicted Appetite Traits\u003c/em\u003e relative to the two moderate appetite profiles. Sex and the number of natural teeth were not significantly associated with profile membership.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eInsert Table 4 here\u003c/em\u003e\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic variables associated with latent profile membership in the multinomial logistic regression model.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\n \u003cp\u003e② vs ①\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\n \u003cp\u003e③ vs ①\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\n \u003cp\u003e④ vs ①\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\n \u003cp\u003e② vs ③\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c20\" namest=\"c18\"\u003e\n \u003cp\u003e② vs ④\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c24\" namest=\"c22\"\u003e\n \u003cp\u003e④ vs ③\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c14\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c15\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c16\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c18\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c19\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c20\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c22\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c23\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c24\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTeeth Numbers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c24\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOral Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-0.05\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-0.13\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e-0.22\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\n \u003cp\u003e0.16\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\n \u003cp\u003e-0.09\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c24\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-0.05\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.06\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c24\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\n \u003cp\u003e0.11\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c24\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\n \u003cp\u003e-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c24\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.96\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c24\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eMaterial status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e1.33\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\n \u003cp\u003e-0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\n \u003cp\u003e0.95\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c24\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c24\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e2.00\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\n \u003cp\u003e-1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\n \u003cp\u003e2.84\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c24\"\u003e\n \u003cp\u003e17.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"24\"\u003eNote: Sex (0\u0026thinsp;=\u0026thinsp;male, 1\u0026thinsp;=\u0026thinsp;female); residence (0\u0026thinsp;=\u0026thinsp;urban, 1\u0026thinsp;=\u0026thinsp;rural); Material status (0\u0026thinsp;=\u0026thinsp;having a spouse, 1\u0026thinsp;=\u0026thinsp;no spouse); Education level (0\u0026thinsp;=\u0026thinsp;low; 1\u0026thinsp;=\u0026thinsp;medium; 2\u0026thinsp;=\u0026thinsp;high); The comparison between latent groups is made with the latent group after \u0026ldquo;vs\u0026rdquo; as the reference; The \u0026ldquo;0\u0026rdquo; level of each demographic variable as the reference group for comparison; Abbreviations: OR, odds ratio; SE, approximate standard error. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .05., \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .01. ① = Low Appetite Traits, ② = Moderate Appetitive Traits with Emotional Under-eating, ③ = Moderate Appetitive Traits, and ④ = High Conflicted Appetitive Traits.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Profile Differences in Health-Related Problems\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the comparisons of the four appetitive trait patterns on various health-related measures. Significant differences were observed for psychological distress (K6: \u003cem\u003e\u0026chi;\u003c/em\u003e\u0026sup2; = 66.47, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), health-related quality of life (SF-8: \u003cem\u003e\u0026chi;\u003c/em\u003e\u0026sup2; = 23.41, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), oral health-related quality of life (GOHAI: \u003cem\u003e\u0026chi;\u003c/em\u003e\u0026sup2; = 88.96, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) disordered eating symptoms (EDE-QS: \u003cem\u003e\u0026chi;\u003c/em\u003e\u0026sup2; = 115.80, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and Avoidant/Restrictive Food Intake Disorder symptoms (ARFID: \u003cem\u003e\u0026chi;\u003c/em\u003e\u0026sup2; = 82.36, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Significant group differences were also observed for three subscales of the NIAS: picky eating (\u0026chi;\u0026sup2; = 90.12, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), low appetite (\u0026chi;\u0026sup2; = 64.95, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and fear of aversive consequences (\u0026chi;\u0026sup2; = 52.66, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). In contrast, no significant differences were found for BMI (\u003cem\u003e\u0026chi;\u003c/em\u003e\u0026sup2; = 2.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.334).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eInsert Table 5 here\u003c/em\u003e\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eProfile differences in health-related problems.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLow Appetitive Traits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eModerate Appetitive Traits with Emotional Under-eating\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eModerate Appetitive Traits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eHigh Conflicted Appetitive Traits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e16.1% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e (\u003cem\u003eSE\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e29.0% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;168)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e (\u003cem\u003eSE\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e44.9% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;260)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e (\u003cem\u003eSE\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e10.0% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e (\u003cem\u003eSE\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u003cem\u003eApproximate Chi-Square\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eK6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.70(0.41)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5.46(0.34)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e6.82(0.33)\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e9.99(0.77)\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e66.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSF-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e75.06(1.70)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e66.14 (1.40)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e67.30(0.99)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e63.29(2.36)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e23.41\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEDE-QS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.16(0.37)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.36(0.32)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.07(0.59)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e12.22(1.70) \u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e115.80\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eARFID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e12.67(0.81)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e15.01(0.60)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e17.70(0.43)\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e25.81(1.36) \u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e82.36\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePicky eating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.66(0.30)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3.90(0.24)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.64(0.18)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e8.00(0.49) \u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e90.12\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAppetite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.29(0.32)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5.41(0.24)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.85(0.17)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e8.78(0.47) \u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e64.95\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.72(0.35)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5.70(0.29)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e6.20(0.20)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e9.03(0.50) \u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e52.66\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOral Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e50.91(0.95)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e47.54(0.71)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e43.68(0.61)\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e36.32(1.63)\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e88.96\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e21.87 (0.37)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e21.30 (0.28)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e21.65 (0.22)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e22.27 (0.51)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eNote: Different superscript letters (a, b, c, d) within a row indicate significant pairwise differences between profiles at \u003cem\u003ep\u003c/em\u003e \u0026lt; .05. SE, standard error, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .001. Abbreviations: ARFID, Avoidant/Restrictive Food Intake Disorder; EDE-QS, The 12-item short form of the Eating Disorder Examination Questionnaire; K6, The Chinese version of the 6-item Kessler Psychological Distress Scale; SF-8, The SF-8 Health Survey; BMI, Body Mass Index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003eSpecifically, for psychological distress, individuals in the \u003cem\u003eHigh Conflicted Appetitive Traits\u003c/em\u003e group exhibited the highest levels of distress, exceeding those in the \u003cem\u003eLow Appetitive Traits\u003c/em\u003e (\u0026chi;\u0026sup2; = 52.33, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), \u003cem\u003eModerate Appetitive with Emotional Under-Eating\u003c/em\u003e (\u0026chi;\u0026sup2; = 28.78, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and \u003cem\u003eModerate Appetitive Traits\u003c/em\u003e groups (\u0026chi;\u0026sup2; = 13.30, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Moreover, the \u003cem\u003eModerate Appetitive Traits\u003c/em\u003e group reported significantly higher distress than the \u003cem\u003eModerate Appetitive with Emotional Under-Eating\u003c/em\u003e group (\u0026chi;\u0026sup2; = 7.05, \u003cem\u003ep\u003c/em\u003e = .008), and the latter also exceeded the \u003cem\u003eLow Appetitive Traits\u003c/em\u003e group (\u0026chi;\u0026sup2; = 10.71, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Regarding health-related quality of life (SF-8), the \u003cem\u003eLow Appetitive Traits\u003c/em\u003e group reported the highest scores, significantly higher than those of the \u003cem\u003eHigh Conflicted Appetitive Traits\u003c/em\u003e (\u0026chi;\u0026sup2; = 16.42, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), \u003cem\u003eModerate Appetitive with Emotional Under-eating\u003c/em\u003e (\u0026chi;\u0026sup2; = 16.06, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and \u003cem\u003eModerate Appetitive Traits\u003c/em\u003e groups (\u0026chi;\u0026sup2; = 14.46, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). No significant differences were observed among the remaining three groups (all \u003cem\u003ep\u003c/em\u003es \u0026gt; .132).\u003c/p\u003e\n \u003cp\u003eFor disordered eating symptoms, both EDE-QS and ARFID scores revealed a clear gradient: the \u003cem\u003eHigh Conflicted Appetitive Traits\u003c/em\u003e group scored significantly higher than all other groups (all \u003cem\u003ep\u003c/em\u003es \u0026lt; .007) on both measures. The \u003cem\u003eModerate Appetitive Traits\u003c/em\u003e group also scored higher than both the \u003cem\u003eModerate Appetitive with Emotional Under-eating\u003c/em\u003e and the \u003cem\u003eLow Appetitive Traits\u003c/em\u003e groups (all \u003cem\u003ep\u003c/em\u003es \u0026lt; .001) on both measures. For EDE-QS scores, no significant difference emerged between the \u003cem\u003eModerate Appetitive with Emotional Under-eating\u003c/em\u003e and the \u003cem\u003eLow Appetitive Traits\u003c/em\u003e groups (\u0026chi;\u0026sup2; = 0.16, \u003cem\u003ep\u003c/em\u003e = .686). In contrast, for ARFID scores, the \u003cem\u003eModerate Appetitive with Emotional Under-eating\u003c/em\u003e group scored significantly higher than the \u003cem\u003eLow Appetitive Traits\u003c/em\u003e group (\u0026chi;\u0026sup2; = 5.29, \u003cem\u003ep\u003c/em\u003e = .022).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOlder adults experience unique psychological, physiological, and social influences on appetite that differ markedly from those of the general population, highlighting the importance of examining their specific appetitive trait patterns and related health outcomes. To address gaps in prior research, the present study employed a person-centered approach, using LPA to identify distinct patterns of appetitive traits and examined their associations with health-related factors among older Chinese adults. Across profiles, the most clinically concerning pattern was the \u003cem\u003eHigh Conflicted Appetitive Traits\u003c/em\u003e profile, which was characterized by elevated eating-related psychopathology and poorer psychosocial well-being, whereas the \u003cem\u003eLow Appetitive Traits\u003c/em\u003e profile generally showed the most favorable quality-of-life indicators. Oral health emerged as a robust correlate of profile membership, highlighting the relevance of age-related functional factors in shaping appetite patterns in later life. Below, we interpret these profiles in relation to prior work and discuss implications for assessment and intervention.\u003c/p\u003e \u003cp\u003eAmong the four latent profiles, the largest group, \u003cem\u003eModerate Appetitive Traits\u003c/em\u003e, showed balanced levels of food approach and food avoidance traits, indicating relatively stable appetitive characteristics in later life. In contrast, the \u003cem\u003eHigh Conflicted Appetitive Traits\u003c/em\u003e group displayed high scores in both food approach and food avoidance traits, except for food fussiness, reflecting ambivalent eating tendencies, and was linked to higher disordered eating, increased psychological distress, and poorer health-related and oral health-related quality of life. At the opposite end, the \u003cem\u003eLow Appetitive Traits\u003c/em\u003e group reported the lowest scores across all food-approach and food-avoidance traits. These features align with the \u0026ldquo;anorexia of aging\u0026rdquo; phenomenon, where older adults naturally experience a decline in appetite. As people age, age-related changes in bodily function and metabolism may contribute to reduced appetite and greater food avoidance [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], thereby decreasing food enjoyment. Lastly, the \u003cem\u003eModerate Appetitive with Emotional Under-Eating\u003c/em\u003e group exhibited moderate avoidance traits and significant emotional under-eating, suggesting a need for further investigation into its dietary patterns and potential health risks. These patterns may also reflect the cultural context of Chinese older adults, for whom eating is strongly embedded in family and social life, potentially sustaining approach motivations even when aging-related constraints (e.g., sensory decline and oral discomfort) increase avoidance. Cohort-related experiences, such as historical food scarcity and frugal norms, may further heighten the salience of eating, contributing to ambivalent appetitive tendencies in later life.\u003c/p\u003e \u003cp\u003eComparison studies in children and young adults [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] suggest that the four-profile solution in older adults aligns with the latent profile structure observed across the lifespan, indicating that co-occurring food approach and avoidance traits are a consistent phenomenon. Similar to younger populations, older adults showed both a low-appetite subgroup and a high-ambivalence subgroup, with the latter resembling \u0026ldquo;food seekers and avoiders\u0026rdquo; patterns previously linked to emotional reactivity and disordered eating risk [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Consistent with the findings on latent profiles of negative emotional eating reported by He \u0026amp; Chen et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], which focused solely on emotional under-eating and emotional over-eating, the present study also revealed similar emotional eating patterns, suggesting that negative emotional eating clusters into comparable configurations across age groups. Notably, emotional under-eating was more prominent among older adults, likely reflecting age-related changes in appetite regulation, psychosocial stressors, and physiological responses to negative effects. Additionally, differences across profiles in food fussiness were minor, implying that picky eating is less central to profile distinctions in later life. Overall, these results emphasize that while the overall latent structure remains consistent across ages, older adults display age-specific patterns, especially more pronounced emotional under-eating and greater stability in food fussiness.\u003c/p\u003e \u003cp\u003eTo explore and clarify the factors behind these age-specific appetitive profiles, multinomial logistic regression was conducted using sociodemographic and oral health variables. Demographic factors significantly correlated with latent appetitive trait profiles. Specifically, older adults from rural areas were more likely to belong to the \u003cem\u003eModerate Appetite with Emotional Under-Eating\u003c/em\u003e profile, potentially reflecting disparities in dietary environments and access to nutritional resources. Participants without a spouse were more likely to fall into the \u003cem\u003eHigh Conflicted Appetitive Traits\u003c/em\u003e profile, which may indicate the role of social support in regulating eating behaviors and reducing psychological distress [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, better oral health was associated with a higher likelihood of being in the \u003cem\u003eLow Appetitive Traits\u003c/em\u003e profile and a lower likelihood of being classified in the \u003cem\u003eHigh Conflicted Appetitive Traits\u003c/em\u003e profile. This suggests that better oral health helps maintain consistent eating habits and a stable relationship with food, aligning with the \u0026ldquo;anorexia of aging\u0026rdquo; phenomenon in older adults [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Conversely, poorer oral health, such as chewing difficulties, pain, and discomfort, may contribute to more conflicted or dysregulated appetitive patterns. Notably, tooth count did not significantly predict profile membership, suggesting that self-perceived oral health may reflect functional and psychosocial aspects of oral conditions beyond tooth count alone. These findings highlight the impact of demographic vulnerabilities and oral health on older adults\u0026rsquo; eating behaviors and nutritional status [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Interventions aimed at socially disadvantaged individuals and improving oral health could play a key role in reducing appetite-related risks and supporting healthy aging.\u003c/p\u003e \u003cp\u003eFinally, the four appetitive trait profiles displayed distinct health-related patterns that mirror the physiological and psychological changes of aging [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The \u003cem\u003eHigh Conflicted Appetitive Traits\u003c/em\u003e profile showed the highest levels of psychological distress and the most severe disordered eating and ARFID symptoms, similar to prior studies on the group with conflicted emotional eating patterns [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. This pattern may reflect competing motivations: cue- and emotion-driven approach tendencies co-occurring with avoidance reinforced by negative effects and the perceived costs of eating (e.g., discomfort or fear of consequences). In later life, sensory decline and oral problems may further increase avoidance while leaving approach motivations relatively intact, thereby strengthening ambivalence and psychological burden. In contrast, the \u003cem\u003eLow Appetitive Traits\u003c/em\u003e profile showed comparatively favorable well-being and quality-of-life indicators, consistent with a less emotionally reactive appetite pattern. Notably, BMI did not differ significantly across four profiles, likely reflecting age-related changes in body composition and muscle mass, which make weight a less sensitive indicator of nutritional risk in older adults [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Clinically, these findings suggest that screening for a conflicted appetitive pattern may help identify older adults who could benefit from integrated interventions that target emotion regulation and maladaptive eating cycles (e.g., CBT-informed coping skills, addressing avoidance/fear and cue-driven eating) alongside practical supports such as oral-health management and tailored dietary guidance to reduce eating-related discomfort and improve well-being.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Implications\u003c/h2\u003e \u003cp\u003eThe significant differences in psychological distress and HRQoL across profiles highlight the potential need for pattern-specific interventions. Recognizing this heterogeneity in appetitive traits may enable clinicians and researchers to design tailored dietary and behavioral strategies that promote healthier eating and improve overall nutritional and health outcomes in older adults. In particular, the \u003cem\u003eHigh Conflict Appetitive Traits\u003c/em\u003e group demonstrated the most severe psychological distress and disordered eating, indicating an urgent need for comprehensive support of older adults with complex and contradictory appetitive patterns. Programs that integrate nutritional counseling with oral health improvement, social support enhancement, and mental health care may be particularly effective for this high-risk subgroup. Furthermore, proactive strategies that mitigate food avoidance and foster enjoyment of food could help reduce appetite-related risks and support healthy aging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Limitations and Future Research Directions\u003c/h2\u003e \u003cp\u003eSeveral limitations should be noted that guide future research directions. First, the present study is a cross-sectional design, which restricts the ability to infer temporality and causality between appetitive traits, oral health, and health-related outcomes. Longitudinal and experimental (e.g., intervention) studies are needed to clarify the temporal relationships and causal pathways among these variables. Relatedly, building on prior work using negative emotional eating profiles in adolescents [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], latent transition analysis should be conducted to determine whether older adults move between profiles across time and whether such transitions predict subsequent changes in psychological well-being, HRQoL, and eating-related psychopathology. Second, the study sample was drawn from a specific geographic and cultural context, which may limit the generalizability of the findings, as appetitive traits and dietary behaviors are influenced by cultural and environmental factors. Future research should conduct multisite and cross-cultural investigations to determine whether the latent profiles of appetitive traits observed in this study are consistent across different populations. Third, most variables were assessed using self-reports, which may have introduced inaccuracies and social desirability that could have biased our findings. Incorporating objective measures, such as clinical evaluations of oral health and nutritional biomarkers, would strengthen the robustness of future findings. Finally, although this study considered key demographic factors and oral health as correlates of profile membership, other relevant factors, such as medical conditions, especially in older adults, may play a crucial role in shaping appetite, but this study did not include these variables. Appetitive traits are multi-factorial, and as such, future studies should adopt a more comprehensive approach by integrating highly correlated variables to provide a holistic understanding of appetitive traits in older adults.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Conclusion\u003c/h2\u003e \u003cp\u003eIn conclusion, this study identified four distinct appetitive trait profiles among older Chinese adults: \u003cem\u003eLow Appetitive Traits\u003c/em\u003e, \u003cem\u003eModerate Appetitive with Emotional Under-eating\u003c/em\u003e, \u003cem\u003eModerate Appetitive Traits\u003c/em\u003e, and \u003cem\u003eHigh Conflicted Appetitive Traits\u003c/em\u003e. Among these profiles, the \u003cem\u003eHigh Conflict Appetitive Traits\u003c/em\u003e group exhibited the highest psychological distress and disordered eating, whereas the \u003cem\u003eLow Appetitive Traits\u003c/em\u003e group demonstrated the most favorable health profile. Oral health and demographic characteristics, including age, marital status, residence, and educational level, were significantly associated with profile membership, highlighting the potential influence of social and functional factors on appetitive behaviors. These findings emphasize the importance of identifying high-risk appetitive patterns and developing targeted interventions that integrate nutritional guidance, oral health promotion, and psychosocial support to foster healthy aging.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAEBQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdult Eating Behavior Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARFID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAvoidant/Restrictive Food Intake Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBCH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBolck\u0026ndash;Croon\u0026ndash;Hagenaars\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBLRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBootstrap Likelihood Ratio Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBodily Pain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEating Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEDE-QS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEating Disorder Examination Questionnaire Short Form\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGOHAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeriatric Oral Health Assessment Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHRQoL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth-Related Quality of Life\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eK6\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e6-item Kessler Psychological Distress Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLog-Likelihood\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLo\u0026ndash;Mendell\u0026ndash;Rubin Adjusted Likelihood Ratio Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLatent Profile Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMental Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRobust Maximum Likelihood Estimator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNine-Item ARFID Screen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhysical Function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRole Emotional\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRole Physical\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSABIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSample-Size Adjusted Bayesian Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocial Function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSF-8\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e8-item Short Form Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVitality\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Hunan Provincial Social Science Achievement Evaluation Committee General Project in 2023 (No. XSP24ZDI020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGui Chen: Conceptualization, Funding acquisition, Project administration, Investigation, Supervision, Formal analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. Qiling Peng: Formal analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. Xingwei Luo: Writing \u0026ndash; original draft; Writing \u0026ndash; review \u0026amp; editing; Shaowu Li: Investigation, Writing \u0026ndash; review \u0026amp; editing. Yunyi Cheng: Writing \u0026ndash; original draft; Writing \u0026ndash; review \u0026amp; editing. Urvashi Dixit: Writing \u0026ndash; review \u0026amp; editing. Wesley R. Barnhart: Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. Jinbo He: Conceptualization, Supervision, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Research Ethics Office at Hengyang Normal University (2023LL1230).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese data are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI in Scientific Writing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the authors used ChatGPT 5.4 to improve the language of the manuscript. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHunot C, Fildes A, Croker H, Llewellyn CH, Wardle J, Beeken RJ. Appetitive traits and relationships with BMI in adults: Development of the Adult Eating Behaviour Questionnaire. Appetite. 2016;105:356\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.appet.2016.05.024\u003c/span\u003e\u003cspan address=\"10.1016/j.appet.2016.05.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoesveldt S, Bobowski N, McCrickerd K, Ma\u0026icirc;tre I, Sulmont-Ross\u0026eacute; C, Forde CG. The changing role of the senses in food choice and food intake across the lifespan. 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Appetite. 2025;204:107728. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.appet.2024.107728\u003c/span\u003e\u003cspan address=\"10.1016/j.appet.2024.107728\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-eating-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joed","sideBox":"Learn more about [Journal of Eating Disorders](http://jeatdisord.biomedcentral.com)","snPcode":"40337","submissionUrl":"https://submission.nature.com/new-submission/40337/3","title":"Journal of Eating Disorders","twitterHandle":"@JEatDisord","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"appetitive traits, latent profile analysis, older adults, eating disorder, health-related quality of life","lastPublishedDoi":"10.21203/rs.3.rs-9173767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9173767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eAppetitive traits shape eating behaviors across the lifespan, yet their patterns and health correlates in later life remain poorly understood. This study used latent profile analysis (LPA) to identify distinct profiles of appetitive traits among older adults and to examine their sociodemographic and oral health correlates, as well as their associations with eating disorder symptoms and psychosocial outcomes.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eA sample of 579 Chinese older adults (aged 60\u0026ndash;93 years) was included. LPA was conducted to derive appetitive trait profiles, followed by multinomial logistic regression and Bolck\u0026ndash;Croon\u0026ndash;Hagenaars (BCH) analyses to examine predictors and health outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFour profiles were identified: \u003cem\u003eLow Appetitive Traits\u003c/em\u003e (16.1%), \u003cem\u003eModerate Appetitive Traits with Emotional Under-Eating\u003c/em\u003e (29.0%), \u003cem\u003eModerate Appetitive Traits\u003c/em\u003e (44.9%), and \u003cem\u003eHigh Conflicted Appetitive Traits\u003c/em\u003e (10.0%). Oral health emerged as a consistent correlate of profile membership, alongside age, marital status, residence, and education, whereas sex and number of natural teeth were not associated with memberships. Profiles differed significantly in psychological distress, disordered eating, ARFID symptoms, and health-related quality of life. The High Conflicted Appetitive Traits profile was characterized by the greatest eating-related psychopathology and poorest psychosocial outcomes, whereas the Low Appetitive Traits profile showed the most favorable health profile.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings demonstrate substantial heterogeneity in appetitive traits in later life and highlight their close links with oral health, sociodemographic factors, and psychological well-being. Identifying high-risk appetitive profiles may inform targeted interventions integrating nutritional guidance, oral health promotion, and psychosocial support to promote healthy aging.\u003c/p\u003e","manuscriptTitle":"Appetitive Trait Profiles in Late Life: Links to Eating Disorder Psychopathology and Psychosocial Well-Being","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 17:22:01","doi":"10.21203/rs.3.rs-9173767/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"82795450677384661465937602877839550836","date":"2026-04-01T13:36:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-31T23:39:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T06:33:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T06:32:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Eating Disorders","date":"2026-03-20T01:39:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-eating-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joed","sideBox":"Learn more about [Journal of Eating Disorders](http://jeatdisord.biomedcentral.com)","snPcode":"40337","submissionUrl":"https://submission.nature.com/new-submission/40337/3","title":"Journal of Eating Disorders","twitterHandle":"@JEatDisord","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1daad484-1a86-4267-b62d-b5931d37ecc8","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T17:22:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 17:22:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9173767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9173767","identity":"rs-9173767","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}