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. Author manuscript; available in PMC: 2025 Aug 8.
Published in final edited form as: J Health Psychol. 2025 Aug 7;31(3):1192–1207. doi: 10.1177/13591053251354859

Food Access Relates to Loss of Control Eating in Adolescents with Cardiometabolic Conditions: Exploration of Week-to-Week Variation

Carolina M Bejarano 1,2, Lauren Shomaker 3,4, Amy S Shah 2,5, Stavra Xanthakos 2,6, Sanita Ley 1,2, James Peugh 1,2, Rachel Louis-Gonzalez 1, Meg H Zeller 1,2
PMCID: PMC12333545  NIHMSID: NIHMS2092118  PMID: 40772342

Abstract

The present study examined the relationship between FI and LOC in adolescents treated for cardiometabolic conditions, and inter-connections among week-to-week variations in receipt of SNAP, FI, and LOC. Forty-one adolescents presenting to cardiometabolic clinics completed validated surveys of FI and LOC. A subset of 20 adolescents experiencing FI completed 4 weekly e-surveys, over one month. 56% reported public assistance, 39% reported FI, and 37% reported LOC. FI related to greater LOC (β=.17, p<.01). Among those with FI, receipt of SNAP related to greater within-person FI within the same week (β=.59, p<.05) but not the following week (β=−.99, p=.06). FI related to greater LOC in the same week (β=.20, p<.001) and in the following week (β=.20, p<.001). In adolescents treated for cardiometabolic conditions, FI is prevalent and related to greater LOC. SNAP benefits might dampen FI week-to-week, but the effect of FI on LOC and cardiometabolic health should be explored causally.

Keywords: food insecurity, loss of control eating, adolescents, type 2 diabetes, MASLD, cardiometabolic

Introduction

Food insecurity refers to inconsistent access, availability, and affordability of foods and beverages that are critical to well-being and the prevention of chronic disease (Anderson, 1990; Economic Research Service, United States Department of Agriculture, 2023). Systemic inequities create a disproportionate risk of experiencing food insecurity among minoritized youth (Kunin-Batson et al., 2024). Approximately 17% of households with children in the United States (U.S.) experience food insecurity overall, as compared to 22% of households identifying as non-Hispanic/Latino Black and 21% of homes identifying as Hispanic/Latino (ERS, USDA, 2023). Food insecurity has been related to greater risk for cardiometabolic health problems including risk for type 2 diabetes (T2D) and metabolic dysfunction-associated steatotic liver disease (MASLD; Paik et al., 2024; Thomas et al., 2021). Recent work has found that almost one third of youth and young adults with type 2 diabetes reported experiences of food insecurity in the past year (Malik et al., 2023; Reid et al., 2022) and that food insecurity is prevalent and associated with earlier presentation amongst youth treated for MASLD (Orkin et al., 2024). Though there is emerging work in adolescents, most of the existing work on food insecurity and cardiometabolic health is in adults, creating a need to understand and address the rising rates of cardiometabolic diseases at early ages that especially affect minoritized adolescents. In particular, prevalence rates of T2D in adolescents have increased approximately 5% per year over the past decade, disproportionately affecting non-Hispanic/Latino Black and Hispanic/Latino youth as compared to non-Hispanic/Latino White youth, a reflection of health disparities driven by systems of inequity (Bacha et al., 2021). Likewise, MASLD, the most common chronic liver disease in children with an estimated overall prevalence of 24% in adolescents and young adults, affects males of Hispanic/Latino ethnicity at higher rates than non-Hispanic/Latino White youth (Perumpail et al., 2024).

Food insecurity has been posited to relate to poor cardiometabolic health outcomes through its impact on poor diet quality, which can often be sustained through cycles of poverty and systemic inequities (Paik et al., 2024; Bowen et al., 2021). Yet, we have limited knowledge of food insecurity and eating behavior in adolescents, let alone adolescents with cardiometabolic diseases like T2D and MASLD. In theory, periods of inconsistent food access not only directly contribute to the quality and nutrition of foods available to youth, but can also create beliefs about true or perceived food scarcity that relate to a “feast or famine” cycle in which more is eaten when food resources are available and less is eaten when it is not available (Hazzard et al., 2023). This phenomenon may put adolescents at heightened risk for loss of control eating (LOC). LOC is a salient component of binge-type disordered eating and refers to the subjective experience of being unable to control what or how much one is eating, regardless of the amount actually consumed (Tanofsky-Kraff et al., 2009a). It is relevant to cardiometabolic health, because it has been related to excess weight gain over time, worse cholesterol and blood pressure, and worsening components of metabolic syndrome in youth with elevated BMI (Radin et al., 2015, Stokjek et al., 2017, Tanofsky-Kraff et al., 2009b, 2011). LOC is experienced by approximately half of adolescents seeking weight management, and 1 in 5 adolescents with T2D (Wifley et al., 2011). LOC has been linked to to higher odds of having a MASLD comorbidity among adolescents undergoing metabolic and bariatric surgery for obesity (Ley et al., 2021). Disordered eating behaviors such as LOC can exacerbate risk factors relevant to T2D and MASLD given their effect on dietary intake. For example, LOC has been associated with youths’ consumption of more high-energy, lower nutrient snack and dessert foods in laboratory test meal studies (Tanofsky-Kraff et al., 2009a). Disordered eating cycles driven by LOC can contribute to risk for eating disorders, weight cycling, and downstream, cardiometabolic health concerns, all of which pose risk to overall health and quality of life (Hazzard et al., 2020; Nip et al., 2019).

A review of the evidence in adult samples has found food insecurity to be related to binge eating and other eating disorder pathology (Hazzard et al., 2020). In youth, a recent review found 20 total studies examining food insecurity and disordered eating, finding relationships amongst these constructs but calling for more hypothesis-driven work (Bidopia et al., 2023). Despite the prevalence of both food insecurity and LOC in adolescents with cardiometabolic conditions, to what extent food insecurity and LOC are related in this population is poorly understood. An understanding of the food insecurity-LOC association in adolescents receiving treatment for T2D and MASLD is critical to informing interventions to address eating behavior change, recognizing the role of food access. Moreover, the relationship of food insecurity and LOC is likely to be complex. Dietary behavior and the psychosocial factors that influence it (e.g., affect, environment) vary day-to-day and even throughout a day, for a given adolescent (i.e., “within-person fluctuation”; Bejarano et al., 2018; Bejarano et al., 2023). However, eating behavior is typically measured statically at one time point. Likewise, food insecurity is not a constant but is often measured at one time point (e.g., once per year in clinic). Food access often has considerable variability for adolescents in households affected by food insecurity, due to timing of resource distribution, family sources of income and public assistance (e.g., SNAP benefits: Supplemental Nutrition Assistance Program; i.e., food stamps). Indeed, food insecurity has been shown to fluctuate, especially for economically disadvantaged youth from minoritized racial/ethnic backgrounds (Gassman-Pines et al., 2023). How this week-to-week variation relates to LOC in adolescents has not been studied with methodology designed to capture these patterns.

Present study

The existing evidence on the relationships of food insecurity with cardiometabolic health mainly comes from studies of adults (Abdurahman et al., 2021; Thomas et al., 2021) with little research in adolescents. This is a critical gap, as adolescence is a key developmental period where both positive and negative health behaviors and habits are developed that can be carried into adulthood (Rohde et al., 2015). These behaviors are known to impact dysregulated eating habits as well as cardiometabolic health (Bauman et al., 2023; Radin et al., 2015). Therefore, adolescents are especially at risk, as the effects of food insecurity affect their relationships with food during this key developmental period. Moreover, few studies of food insecurity have focused on adolescents or examined variation in food access and its effects over an extended period of time. To address these gaps, the present study aims were (1) To examine associations between food insecurity and LOC in adolescents seeking treatment for T2D and/or MASLD; we hypothesized that food insecurity would be positively related to LOC; and (2) To examine week-to-week variations amongst perceived food insecurity, receipt of public assistance (e.g., SNAP), and LOC over one month in adolescents who endorsed food insecurity; we hypothesized that fluctuations in food insecurity and SNAP would be related to within-person variation in LOC.

Methods

Transparency and Openness

In this article, we report how we determined our sample size, all data exclusions, all manipulations, and all measures that were included in the study. All data, analysis code, and research materials may be made available upon request to the corresponding author. Data were analyzed using SPSS (version 29.0.1) and Mplus (version 8.11).

Participants

A multi-pronged approach was used to recruit adolescents from the T2D or MASLD clinics from October 2022-May 2023 at Cincinnati Children’s Hospital Medical Center. Recruitment methods included approaching adolescents and their caregiver(s) during an in-person clinic visit or as a follow-up to a clinic visit via email or phone; study flyers posted in the clinics; and institutional email advertising, all describing a study to learn more about adolescent eating behavior. In addition to being treated in the T2D or MASLD clinics, inclusion criteria required that participants were 1) ages 13–17 years, 2) presenting with a biological parent, adoptive parent, or legal guardian, and 3) were able to complete study procedures in English (i.e., parent/caregiver may speak Spanish as Spanish translations of consent materials were provided, but main adolescent measures were only available in English). Exclusion criteria included presence of a diagnosed sensory or cognitive disorder that would inhibit adolescents from completing study measures. Informed consent and assent were obtained for all participants.

A subset of adolescents was provided the option to continue in the study for week-to-week assessments; adolescents were eligible for this portion if they endorsed experiences of food insecurity either by a) a positive food insecurity screener or b) reporting use of public assistance for food resources: SNAP/food stamps and/or Free/Reduced cost Breakfast/Lunch. This was conducted to address potential underreporting on the food insecurity screener and to oversample those experiencing any need for food resources.

Procedures

All procedures were approved through the institutional review board (IRB). For phase 1, following consent/assent, adolescents completed measures in REDCap, either at the clinic visit or remotely through a REDCap link sent to their email. For the week-to-week phase 2 subset, adolescents completed reports of food insecurity and eating behavior on 4 weekly occasions, administered via smartphone or e-mail links to REDCap, over a one-month period. Surveys were administered at 8pm on Mondays, which was designed to capture each adolescent’s experience on a typical weekday, but unlikely to interfere with school, mealtimes, or sleep schedules. Adolescent participants were offered compensation for their time of up to $40 ($20 for Phase 1; $20 for Phase 2).

Constructs and Measures

Demographics.

Adolescents self-reported their age, sex, race/ethnicity, estimated household income, household information, number of adults and number of children residing in the home, (number of adults, number of children), parent marital status and parent education levels.

Food Insecurity.

Adolescents completed the Child Food Security Measure (CFS; Connell et al., 2004) a 9-item survey of perceived food insecurity in the past month, validated for youth ≥12-years-old with dichotomously scored response options: Never (0), Sometimes (1), A lot (1). The affirmative responses (i.e., Sometimes or A lot) are summed to create a score with the following classifications: High Food security (0), Marginal Food security (1), Low Food security (2–5), Very Low Food security (6–9; Connell et al., 2004; ERS, USDA, 2023). For the present study, a score of ≥1 was considered a positive screen in phase 1. For the week-to-week phase 2 period, the CFS was adapted to ask about experiences “in the past week.” These procedures are consistent with previous studies that utilized repeated administrations of questions from the CFS, such as those asking about the current day in ecological momentary assessment studies, and included a dichotomous indicator based on endorsement of at least one item (e.g., Gassman-Pines et al., 2023).

Public Assistance for Food Access.

Adolescents self-reported whether their family receives public assistance: SNAP benefits/food stamps (yes/no) and whether they receive Free/Reduced cost Breakfast/Lunch at school (yes/no) in phase 1. The report of SNAP benefits/food stamps in addition to the food insecurity screener was used due to a documented tendency for families to underreport food insecurity (Livings et al., 2023). This was reported generally (no specific time frame) in phase 1 and adapted in phase 2 to ask specifically about receipt of SNAP/food stamps “the past week.” Phase 2 did not ask about Free/Reduced cost Breakfast/Lunch as this would generally not vary week-to-week over a month.

Loss of Control Eating.

In phase 1, the 24-item Loss of Control over Eating Scale (LOCES; Latner et al., 2014) was completed to assess LOC over the past 4 weeks. Responses are provided on a 5-point Likert scale from Never (1) to Very Often/Always (5) and this measure has established internal consistency, test-retest reliability, and convergent/discriminant validity (Latner et al., 2014). In phase 2, adolescents completed the 7-item version of the Loss of Control over Eating Scale (LOCES-Brief; Latner et al., 2014; Vannucci et al., 2018), modified to ask about the past week. The weekly survey was scored with responses on a 5-point Likert scale from Never (0) to Very often (4). The 7-item version also has established excellent internal consistency, test-retest reliability, and convergent validity (Vannucci et al., 2018). Though briefer LOC questionnaires are typically used in ecological momentary assessment studies that assess LOC multiple times a day (e.g., Goldschmidt et al., 2022), the 7-item LOCES-B was used in the present study due to the weekly (vs. daily) administration and its concordance with the 24-item LOCES used in phase 1.

Cardiometabolic Health Markers.

BMI, hemoglobin A1c, ALT, AST, and systolic and diastolic blood pressure levels were obtained retrospectively from medical chart review and recorded by research staff and used for descriptive purposes in the present study.

Data Analytic Plan

The hypotheses and data analytic plan were pre-specified prior to data collection. Data collected via survey response were stored on a secure, password-protected REDCap database converted to Statistical Package for Social Sciences (SPSS) software program for scoring and analyses. SPSS was used for descriptive and preliminary analyses and all models were performed using Mplus (version 8.11). Robust (MLR) maximum likelihood estimation was used because it is the appropriate method for missing data handling (i.e., no interaction effects are estimates; see Enders et al., 2020) and it corrects standard error estimates in the presence of non-normally distributed data to protect against Type-1 inferential errors (no random slopes are estimated; see Enders et al., 2018).

Phase 1 Analysis.

Descriptive statistics (means, standard deviations, frequency/percentages) were calculated in SPSS. Preliminary analyses were conducted to examine correlations of potential covariates (clinic, age, race/ethnicity, income, parent education, sex) with LOC to determine which covariates to include in subsequent analyses. Participants endorsing food insecurity through the Child Food Insecurity Measure and/or endorsing use of public assistance in phase 1 were coded as “1.” This strategy was utilized to best capture experiences of food insecurity, even when considered “Marginal” due to the known underreporting of food insecurity (Livings et al., 2023). Frequency statistics were run in SPSS to determine the number and total percentage of participants endorsing food insecurity in the sample. Participants endorsing LOC through scored responses on the LOCES in phase 1 that were higher than the mean of LOC were coded as “1” (LOC M: 1.8, SD: .82; range 1.0–4.6). Regression analyses were conducted to examine respective associations of food insecurity (scores on the CFS) with LOC (LOCES), controlling for covariates identified in the preliminary analyses: Income, Parent Education.

Phase 2 Analysis.

Weekly measures of food insecurity were used to calculate a within-person food insecurity variable, or each participant’s deviation from their typical experience of food insecurity. Within-person assessment was conducted with the same procedures as past studies (Bejarano et al., 2018; Bejarano et al., 2023). Multilevel modeling was used to examine associations between three comparisons: (1) receipt of SNAP and food insecurity, (2) food insecurity and LOC, (3) and SNAP and LOC, respectively, within the same week, and from one week to the next, using all 4 weekly surveys. We examined the variability week-to-week in SNAP, perceived food insecurity, and LOC through the intra-class correlation coefficient (ICC). Separate data sets were created to examine “offset variables” (e.g., week 1 SNAP in association with week 2 food insecurity) for each relationship of interest: (1) whether receiving SNAP is related to perceived food insecurity in the same week, and in the following week, with food insecurity examined as both a dichotomous and continuous dependent variable; (2) whether perceived food insecurity is related to LOC in the same week, and in the following week; (3) whether receiving SNAP is associated with LOC in the same week, and in the following week.

Results

Descriptive Statistics

Overall, 85 adolescent patients were approached for study participation in T2D clinic and 100 were approached in the MASLD clinic. Forty-one participants completed phase 1 of the study. Twenty-three were eligible for phase 2 and 20 completed phase 2. Two participants did not engage in the weekly surveys, and 1 opted out. For adolescents in phase 2, 81.5% of weekly surveys were completed. The other patients who met eligibility criteria for phase 1 and were approached but did not participate declined due to lack of time or interest (see Figure 1 in Supplementary Material). Demographics and descriptive statistics of study variables of each group (T2D Clinic, MASLD Clinic) and the combined sample are presented in Table 1. Adolescent participants were on average, 15.2 years old (SD: 1.4; T2D Clinic M=14.7; MASLD Clinic M=15.7), with approximately half (51.2%) identifying as a female and representing diverse self-reported race/ethnicities (36.6% identifying as non-Hispanic/Latino Black, 41.5% identifying as non-Hispanic/Latino White, 34.1% identifying as Hispanic/Latino). Seven (17%; 6 in MASLD, 1 in T2D) caregivers completed a Spanish version of the consent form, with use of interpretation services. The participants recruited from the T2D clinic were majority non-Hispanic/Latino Black identifying (68.2%) and the majority from the MASLD clinic identified as Hispanic/Latino (57.9%), which reflects the respective clinic patient populations at the institution. The majority had a household income <$49,000 per year (66.3%) and were receiving public health insurance (63.4%). As far as key clinical characteristics, the average BMI percentile for the sample was 98.2 (SD=5.8). Average systolic blood pressure was 123.6mm Hg (SD=12.2) and average diastolic blood pressure for the sample was 71.4mm Hg (SD=10.7). In the T2D Clinic, average HbA1c was 6.9% (SD= 2.4). In the MASLD Clinic, average ALT was 77.6 unit/L (SD=46.6) and average AST was 44.5 unit/L (SD=26.1). Information on clinically-elevated cut-points is provided in Table 1.

Table 1.

Demographic Information and Descriptive Statistics

Variable T2D
Phase 1
n=22 (%)		 MASLD
Phase 1
n=19 (%)		 Total
Phase 1
n=41 (%)		 Phase 2
n=20 (%)
Biological Sex
 Female 13 (59.1%) 8 (42.1%) 21 (51.2%) 10 (50%)
 Male 9 (40.9%) 11 (57.9%) 20 (48.8%) 10 (50%)
Gender Identity
 Girl 12 (54.5%) 9 (47.4%) 21 (51.2%) 9 (45%)
 Boy 9 (40.9%) 10 (52.6%) 19 (46.3%) 10 (50%)
 Non-binary 1 (4.5%) 0 (0%) 1 (2.4%) 1 (5%)
Race/Ethnicity*
 Non-Hispanic Black 15 (68.2%) 0 (0) 15 (36.6%) 8 (40%)
 Non-Hispanic White 8 (36.4%) 9 (47.4%) 17 (41.5%) 6 (30%)
 Hispanic/Latino 3 (13.6%) 11 (57.9%) 14 (34.1%) 7 (35%)
Approximate Family Income
 < $10,000 3 (13.6%) 6 (31.6%) 9 (22.0%) 6 (30%)
 $10,000-$49,000 10 (45.5%) 9 (47.4%) 19 (46.3%) 12 (60%)
 $50,000-$99,000 6 (27.3%) 2 (10.5%) 8 (19.5%) 1 (5%)
 > $100,000 3 (13.6%) 2 (10.5%) 5 (12.2%) 1 (5%)
Parent Education
 >7th grade 0 (0%) 2 (10.5%) 2 (4.9%) 2 (10%)
 Some high school 0 (0%) 3 (15.8%) 3 (7.3%) 2 (10%)
 Completed high school 10 (45.5%) 4 (21.1%) 14 (34.1%) 9 (45%)
 Some college or vocational school 4 (18.2%) 3 (15.8%) 7 (17.1%) 6 (30%)
 Completed college or university 6 (27.3%) 5 (10.5%) 8 (19.5%) 1 (5%)
 Completed graduate or professional degree 2 (9.1%) 5 (26.3%) 7 (17.1%) 0 (0%)
Insurance Type
 Public 14 (63.6%) 12 (63.2%) 26 (63.4%) 19 (95%)
 Private 8 (36.4%) 7 (36.8%) 15 (36.6%) 1 (5%)
Reported use of SNAP 10 (45.5%) 8 (42.1%) 18 (43.9%) 15 (75%)
Reported Free/Reduced Cost Breakfast/Lunch 14 (63.6%) 9 (47.4%) 23 (56.1%) 15 (75%)
Completed Part 2 11 (50.0%) 9 (47.4%) 20 (48.8%) --
T2D
(M (SD))		 MASLD
(M (SD))		 Total
(M (SD))
Cardiometabolic Markers
 BMI 37.8 (5.4) 35.3 (6.1) 36.6 (5.8)
 BMI percentile (%) 98.8 (1.3) 97.5 (3.4) 98.2 (2.5)
 HbA1c (%) 6.9 (2.4) 5.4 (.29) 6.3 (1.9)
 ALT (unit/L) 92.1 (120.5) 77.6 (46.6) 81.8 (77.9)
 AST (unit/L) 77.4 (92.2) 44.5 (26.1) 54.1 (57.9)
 Systolic Blood Pressure (mm Hg) 119.3 (5.7) 128.5 (15.6) 123.6 (12.2)
 Diastolic Blood Pressure (mm Hg) 72.6 (9.8) 69.9 (11.7) 71.4 (10.7)
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Note. T2D = type 2 diabetes; MASLD = metabolic dysfunction-associated steatotic liver disease; SNAP = Supplemental Nutrition Assistance Program. M = mean; SD = standard deviation; BMI = body mass index. Clinical cutoffs for health markers: BMI percentile: >85% (overweight), >95% (obesity) elevated; Hemoglobin A1C >6.5% elevated; <6.4 normal; ALT >25 unit/L elevated; AST >25 unit/L elevated; Blood Pressure: Systolic >120mmHg, Diastolic <80mmHg elevated; Systolic <120mmHg, Diastolic 80mmHg normal.

*

Participants could check multiple racial/ethnic identities.

Rates of food security in the overall sample in phase 1 were: 25 (61%) High food security, 6 (15%) Marginal, 7 (17%) Low, 3 (7%) Very Low. Overall, food insecurity was present in 39% of participants as determined by categories of marginal, low, or very low food security. As another metric, 43.9% reported use of SNAP and 56.1% reported use of Free/Reduced-price school Breakfast/Lunch. The average level of food insecurity as measured by the CFS (possible scores: 0–9) was 1.1 (SD: 2.1; range:0–8), and was similar in those treated in T2D (M=1.2; SD=2.3) and MASLD clinics (M=1.1; SD=1.9). Both endorsement of food insecurity and use of public assistance for food resources were used to determine overall rates of food insecurity. Therefore, food insecurity was present in 56% when examined dichotomously (i.e., screening positively for food insecurity on any of the metrics vs. not). The average level of LOC (possible scores: 1–5) in the overall sample in phase 1 was 1.8 (SD: .82; range 1.0–4.6), and was similar in T2D (M=1.8; SD=.86) and MASLD (M=1.8; SD=.81). Overall, 15 participants endorsed LOC that was higher than the average (36.6%; i.e., 7 participants in T2D and 8 participants in MASLD clinics respectively).

Aim 1 Results: Association between Food Insecurity and LOC (phase 1)

Regression models indicated that food insecurity was significantly and positively related to LOC in the overall sample (β: .17; SE: .07; p < .01).

Aim 2 Results: Weekly Variation of SNAP, Food Insecurity, and LOC (phase 2)

Within the same week, the ICC of food insecurity was .11, indicating that 11% of the variability in perceived food insecurity was between participants and 94% of the variability was within participants. In the lagged data examining one week to the next, the ICC of food insecurity was .46, indicating that 46% of the variability was between participants and 54% was intraindividual variability within each adolescent. Within the same week, the ICC of LOC was .95, indicating that 95% of the variability was between adolescents and only 5% was within adolescents. One week to the next, the ICC of LOC was .72, indicating that 72% of the variability was between adolescents and 28% was within adolescents. The ICC for receiving SNAP was .48, indicating that 48% of the variability was between adolescents and 52% was within adolescents.

Multilevel Models

SNAP in association with food insecurity and LOC.

Receiving SNAP was significantly associated with within-person food insecurity reported in the same week when examined continuously (β=.60, p < .05) but not the following week (β= −.48, p = .20) when examining data from the four weekly surveys. When food insecurity was examined as a binary outcome, receiving SNAP was not significantly associated with within-person food insecurity in the same week (β= .06, p = .47) or the following week (β= −.99, p = .06). There were no significant associations between between-person SNAP and within-person food insecurity (See Table 2). There were also no significant relationships between receiving SNAP and LOC eating on between- or within-person levels, within the same week or in association with the following week.

Table 2.

Results of Multilevel Models: SNAP/Food stamps predicting Food Insecurity

WP Food Insecurity (same week) WP Food insecurity (Following week)
β (SE) p β (SE) p
SNAP/Food stamps
Within-person
 Binary .06 (.82) .47 −.99 (.69) .06
 Continuous .60 (.29) .03* −.48 (.59) .20
 Variances .14 (.03) .00* .15 (.03) .00*
 Residual Variances .13 (.09) .00* .36 (.28) .00*
Between-person
  Binary −.52 (1.8) .39 −.65 (1.3) .31
  Continuous −.29 (1.3) .39 .49 (1.2) .32
  Means .46 (.09) .00* .44 (.10) .00*
  Intercepts (Continuous) .98 (.70) .07 .62 (.72) .20
  Thresholds (Binary) 1.2 (1.2) .14 .68 (.71) .14
  Variances .14 (.08) .00* 1.6 (3.8) .00*
  Residual Variance (Binary) 15.6 (19.7) .00* 1.6 (3.8) (.00)
  Residual Variance (Continuous) 1.0 (1.3) .00* .43 (.29) .00*
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Note. WP=within-person; Significant relationships denoted by an asterisk. SNAP= Supplemental Nutrition Assistance Program.

Food insecurity in association with loss of control eating.

Food insecurity was significantly associated with within-person LOC in the same week (β=.17, p < .001) and the following week (β=.20, p < .001). There were no significant associations between between-person food insecurity and within-person LOC in the same week or the following week (See Table 3).

Table 3.

Results of Multilevel Models: Food insecurity predicting Loss of Control Eating

WP LOC
(same week)		 WP LOC
(Following week)
b (SE) p b (SE) p
Food insecurity
Within-person
 Food Insecurity .17 (.06) .00* .20 (.05) .00*
 Variances .83 (.17) .00* 3.16 (.68) .00*
 Residual Variances .14 (.03) .00* .45 (.10) .00*
Between-person
  Food Insecurity .09 (.09) .11 -.06 (.10) .27
  Means 1.53 (.61) .01* 1.5 (.51) .00*
  Intercepts .86 (.24) .00* 1.1 (.21) .00*
  Variances 7.0 (3.4) .00* 4.1 (2.4) .00*
  Residual Variance .68 (.32) .00* .18 (.15) .00*
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Note. WP=within-person; Significant relationships denoted by an asterisk.

Discussion

This study found that food insecurity and LOC were prevalent among adolescents treated in cardiometabolic clinics (T2D and/or MASLD). Additionally, while preliminary given the limited sample size, food insecurity was related to LOC as assessed by both static assessment at one timepoint and week-to-week assessment in those with heightened food insecurity. This study builds on the limited research examining relationships between food insecurity and LOC in adolescents with cardiometabolic health conditions by focusing on this key developmental age group with attention to fluctuating mechanisms. Moreover, our recruitment of racially and ethnically diverse adolescents treated in cardiometabolic clinics targeted a population that may be most at risk for experiencing environmental and behavioral disruptions that impact health behavior (Rodgers et al., 2018).

Contextualizing and Interpreting Rates of Food Insecurity

Rates of food insecurity were slightly higher than expected based on rates of national data (ERS, USDA, 2023), but aligned with published rates in adolescent and young adult patients treated for cardiometabolic conditions (Malik et al., 2023; Nip et al., 2019; Orkin et al., 2024), further highlighting the need to address disproportionate risk in the context of cardiometabolic health. In our sample that was approximately 1/3 non-Hispanic/Latino Black, Hispanic/Latino, and non-Hispanic/Latino White identifying, respectively, some level of food insecurity was present in 39% of the sample, compared to 20–22% as expected based on current rates from the USDA that capture disparities experienced by non-Hispanic/Latino Black and Hispanic/Latino families (Rabbitt et al., 2024), 30.7% in youth with T2D (Malik et al., 2023) and 42% in a pediatric MASLD clinic (Orkin et al., 2024). Additionally, 43.9% of participants reported use of SNAP and 56.1% reported use of free/reduced costs breakfasts or lunches at school. These rates are also slightly higher than rates of SNAP usage (38%) and rates of enrollment in School Breakfast Program (30.6%) and National School Lunch Program (54.1%) for families with children in the state where the study occurred (Food and Nutrition Service, 2024; Ohio Department of Education & Workforce, 2023). One potential contribution to these higher than expected rates could be the impact of the COVID-19 pandemic (Kakaei et al., 2022) which exacerbated issues of food access and also disproportionately affected health of minoritized groups, including non-Hispanic/Latino Black and Hispanic/Latino individuals (Tai et al., 2021).

Contextualizing and Interpreting Rates of LOC

Rates of LOC in this sample were also slightly higher than expected. LOC eating was present in 36.6% of the sample, as compared to 20–26% as expected based on rates of binge eating among adolescent girls with elevated weight in the TODAY study (Wilfley et al., 2011) and 25–33% as found in a previous study from our team reporting on LOC in the context of unplanned eating and binge eating in MASLD (Ley et al., 2021). Past studies have also found a range of approximate rates of 30–35% presence of LOC (either clinical or sub-clinical) in adolescents with overweight or obesity (Goldschmidt et al., 2008; He et al., 2017), 20–25% in non-treatment seeking youth (Stojek et al., 2017) and up to 46% in a sample of treatment and non-treatment seeking youth (Radin et al., 2015). While adolescents in our study recruited from the cardiometabolic clinics were not specifically seen for treatment of eating behavior, all had BMI meeting criteria for overweight or obesity (i.e., >85 percentile) for which health behavior interventions were a component of their cardiometabolic care. Therefore, rates of LOC in our sample appear to align with the literature to date, but further research is needed examining LOC in adolescents seen for cardiometabolic comorbidities to obesity. Future work must also attend to differences in measurement of LOC (sensation of feeling unable to control eating, regardless of how much is consumed; a component of binge eating) as compared to binge eating (LOC with consumption of an objective large amount of food) across studies.

Discussion of Aim 1 Findings: Food Insecurity Associated with LOC

Results pertaining to Aim 1 indicated that food insecurity was significantly and positively related to LOC in adolescents treated for cardiometabolic conditions in T2D and/or MASLD clinics. Our study is one of the few studies examining relationships between food insecurity and disordered eating in adolescents, and may be one of the only studies examining these relationships in adolescents in cardiometabolic health clinics. While the significant association between food insecurity and LOC in the present study fits with findings from studies of adults (Hazzard et al., 2020) at the time of a recent review, there were only 20 published studies examining food insecurity and disordered eating behaviors in youth (Bidopia et al., 2023), which did not focus specifically on health risk pertinent to T2D or MASLD. While food insecurity has been related to increased health risk (Abdurahman et al., 2019, 2021) and BMI for adults, it has been less studied in adolescents, with particularly mixed associations of food insecurity with BMI (Franklin et al., 2012). One study of adolescents found that food insecurity was significantly related to more binge eating episodes and higher BMI percentile for Hispanic/Latino adolescents but not for adolescents with other racial/ethnic identities (Kim et al., 2021).

In this study we examined food insecurity and LOC in adolescents whose cardiometabolic health risk was already elevated to the level of being treated in T2D and MASLD clinics. Continued work that examines food insecurity and LOC from a preventive lens in larger samples, before health risks become clinically significant, may allow further clarity in understanding these relationships and how interventions can best target LOC for improved health outcomes. For example, one study found that remission of LOC symptoms had a beneficial impact on metabolic markers in youth (Shank et al., 2018). Additionally, longitudinal research is needed to understand the impacts of food insecurity on cardiometabolic health outcomes in youth, as food insecurity may impact health risk on a more distal timescale (Hazzard et al., 2022). For example, a recent study found significant relationships of LOC with diastolic blood pressure in adolescents with both elevated anxiety and BMI, providing a framework for continued research in this area (Bauman et al., 2023). Moreover, findings on the impact of SNAP benefits for children and adolescents experiencing food insecurity are mixed, with some research suggesting beneficial impacts on health (Alfaro-Hudak et al., 2022) and some suggesting associations with higher cardiometabolic risk (Leung et al., 2017). Future work can inform how SNAP can be best utilized to support health longitudinally for youth, and how to enhance treatment to potentially include attention to food access on environmental levels, individual adolescent eating behavior, and family eating habits.

Discussion of Aim 2 Findings: Week-to-week Associations of Food Insecurity, SNAP, and LOC

Results of Aim 2 examining week-to-week variation and relationships amongst perceived food insecurity, receipt of SNAP, and LOC over one month in a sub-sample of adolescents experiencing food insecurity partially supported the hypothesized relationships. When examining the variability of these variables of interest, food insecurity generally had higher variability within-person and LOC had more variability between person, while SNAP was generally even in between- and within-person variability. This suggests that food insecurity is likely to fluctuate over a month for an individual adolescent, aligned with findings from Gassman-Pines et al. (2023) confirming variation in food insecurity for adolescents. In comparison, LOC was likely to vary more from adolescent to adolescent than for an individual adolescent in this sample. While preliminary given the modest size of the current sample, one potential explanation is that there is a lagged association of receiving SNAP and having access to food in a manner that increases perceived food security. Specifically, receiving SNAP was significantly related to higher food insecurity in the same week, but this relationship was not significant in relationship to the following week. Though speculative, it is possible that families acquire food resources during the same week they receive SNAP, which then has a slightly delayed influence on perceived food insecurity the following week. This possibility makes sense considering processes that occur over 2 weeks (week-to-week) as we measured in the present study. While our study did not account for perceived food insecurity at the beginning versus at the end of the month, there is evidence on the larger context of SNAP timing over the month as a whole. For example, studies by Gassman-Pines et al., show that parents of young children reported higher levels of food insecurity at the end of the month (2019) and that adolescents reported higher daily food insecurity in the second half of the month (2023), after receiving SNAP near the beginning of the month.

Experiencing food insecurity was also significantly related to LOC eating with the same magnitude in the same week and the following week. This suggests that once food insecurity is experienced generally, adolescents may be at heightened risk for LOC eating any time of the month, regardless of the food resources that are present, aligned with a learned “feast or famine” response (Hazzard et al., 2023). Our findings fit with those of a recent study by Hazzard et al. (2023), observing a significant association of food insecurity and subsequent binge eating symptoms in young adults 22–29 years old. One prior study also found relationships between food insecurity and LOC eating episodes in a racially/ethnically diverse sample of adolescents (Kim et al., 2021). However, while ecological momentary assessment methodology was used to measure eating behavior, a static measurement of food insecurity was used in this study. While a psychometrically sound measure of LOC was used in this study, it is possible that other measures may be more sensitive to capturing within-person variability, a notion that merits investigation in further work.

Study Limitations

Though the limited sample size did not allow us to make meaningful comparisons across the two clinics or across racial/ethnic groups, rates of both food insecurity and LOC were similar across the sample. It is important to recognize the limitations of the modest sample, especially in phase 2 of the study. Findings should be considered preliminary and not yet generalizable until a study with a larger sample size can replicate these findings. We were unable to examine differences between the T2D and MASLD clinic or by race/ethnicity, nor appropriately examine potential mechanistic processes in the association between food insecurity and LOC. Future studies will be strengthened by recruiting larger samples that are powered to examine mediation and moderation effects by type of cardiometabolic disease. Moreover, scores on the food insecurity measure had limited variability and led us to dichotomize the variable based on any endorsement of food insecurity and/or receipt of SNAP. While we did examine food insecurity both dichotomously and continuously, future work with larger samples may solely use a continuous measure of food insecurity or categorize it further (i.e., high, marginal, low, very low; ERS USDA, 2023). Additionally, we were not able to look at differential diagnoses. It is possible that some patients in the study had diagnoses of both T2D and MASLD, but we did not examine this. Further, patients were at varying levels of diagnosis (i.e., recruitment included new visits and follow-ups in both clinics). Considering that both food insecurity and LOC eating are related to psychological factors, future work can be strengthened by more comprehensively capturing symptoms such as depression and anxiety in relation to these experiences. Overall, future studies can improve rigor by enrolling larger samples of adolescents with targeted health markers that are collected for a specified study timeline (e.g., at the time of data collection and one year follow up).

Conclusions and Future Direction

This study builds on the limited research examining relationships between food insecurity and LOC in adolescents with cardiometabolic conditions. This study provides strong preliminary data for continued work understanding adolescent experiences of food access and risk for developing disordered eating and cardiometabolic health risk. This continued research can then be harnessed to inform or adapt preventive interventions (Herbozo et al., 2023). While existing eating behavior interventions have a strong evidence base, few are grounded in approaches that address the impact of food insecurity or are adapted to the needs of youth at risk for cardiometabolic health concerns (Burnette et al., 2023). Piloting methodology to capture fluctuation in food insecurity and eating behavior is an essential step to inform continued study of these pathways and long-term health outcomes. Future work can also utilize daily surveys or ecological momentary assessment methodology (e.g., multiple times per day) to better understand the fluctuating impact of public assistance for food resources, food insecurity, and eating behavior, with attention to known related factors such as experiences of racism, discrimination and weight stigma. Together the goal of this work is to move toward interventions that are tailored for specific and unique risk factors and needs of adolescents who have experienced food insecurity, while also connecting families to resources that align with their identified needs. Such interventions may offer high impact in primary care, for example, with the potential to act as preventive of the onset of or worsening of cardiometabolic health risk.

Interventions to address and mitigate the impact of food insecurity must occur on environmental, interpersonal, and individual levels. In addition to more nuanced data to capture the fluctuating nature of food access and its effect on eating behavior (e.g., on a daily level), it will be important to capture narratives of lived experience from the perspectives of adolescents and their families to understand their food systems and family dynamics around eating behavior. For example, qualitative data from adolescents and caregivers regarding their experience of the “feast or famine” phenomenon (Hazzard et al., 2023) would help contextualize quantitative findings to best inform intervention efforts. This will complement and support continued systems-level work to address food access as a social determinant of health.

Supplementary Material

Figure 1 Supplemental

Acknowledgments:

We appreciate the adolescents and their caregivers for their time spent participating in the study. Additionally, we appreciate the research coordinators and members of the medical teams that supported data collection for this study.

Funding statement:

This project, as well as the effort of Dr. Bejarano, was funded through a National Institutes of Health training grant: National Institute of Diabetes and Kidney Diseases Research Training in Child Behavior and Nutrition (T32DK063929–21).

Footnotes

Declaration of conflicting interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

CRediT Statement of Authorship Contributions: CRediT Statement of Authorship Contributions: Dr. Bejarano: conceptualziation [lead], data curation [lead], formal analysis [equal], investigation [lead], methodology [lead], project administration [lead], visualization [lead], writing – original draft [lead], writing – review and editing [equal]. Dr. Shomaker: conceptualization [supporting], supervision [supporting], writing – review and editing [equal]. Dr. Shah: conceptualization [supporting], supervision [supporting], writing – review and editing [equal]. Dr. Ley: conceptualization [supporting], writing – review and editing [supporting]. Dr. Xanthakos: conceptualization [supporting], writing – review and editing [supporting]. Dr. Peugh: formal analysis [lead], writing – review and editing [supporting]. Dr. Louis-Gonzalez: review and editing [equal]. Dr. Zeller: conceptualization [equal], supervision [lead], writing – review and editing [equal].

Statements and Declarations:

Ethical considerations: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The REACH study was reviewed and approved by the Institutional Review Board at Cincinnati Children’s Hospital Medical Center (ID: 2022–0196).

Consent to participate: Caregivers provided written informed consent and adolescents provided assent prior to data collection. Participants and caregivers were informed that participation was voluntary and they could discontinue at any time.

Consent for publication: Not applicable.

Study Registration: This study was not formally registered.

Analytic plan pre-registration: The analysis plan was not formally pre-registered.

Analytic code availability: Analytic code used to conduct the analyses presented in this study are not available in a public archive. They may be available by emailing the corresponding author.

Data availability:

De-identified data from this study are not available in a public archive. De-identified data from this study will be made available (as allowable according to institutional IRB standards) by emailing the corresponding author.

Materials availability:

Materials used to conduct the study are not publicly available. Figure 1 is included as supplemental material.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure 1 Supplemental
NIHMS2092118-supplement-Figure_1_Supplemental.docx (37.7KB, docx)

Data Availability Statement

De-identified data from this study are not available in a public archive. De-identified data from this study will be made available (as allowable according to institutional IRB standards) by emailing the corresponding author.

Materials used to conduct the study are not publicly available. Figure 1 is included as supplemental material.

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