Subjective social status is associated with compensation for large meals – A prospective pilot study

Accepted Manuscript Subjective social status is associated with compensation for large meals – A prospective pilot study Nadeeja N. Wijayatunga, Bridget Ironuma, John A. Dawson, Bailey Rusinovich, Candice A. Myers, Michelle Cardel, Gregory Pavela, Corby K. Martin, David B. Allison, Emily J. Dhurandhar PII:

S0195-6663(18)30494-X

DOI:

10.1016/j.appet.2018.07.031

Reference:

APPET 3978

To appear in:

Appetite

Received Date: 13 April 2018 Revised Date:

26 July 2018

Accepted Date: 27 July 2018

Please cite this article as: Wijayatunga N.N., Ironuma B., Dawson J.A., Rusinovich B., Myers C.A., Cardel M., Pavela G., Martin C.K., Allison D.B. & Dhurandhar E.J., Subjective social status is associated with compensation for large meals – A prospective pilot study, Appetite (2018), doi: 10.1016/ j.appet.2018.07.031. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Title page Subjective Social Status is associated with compensation for large meals – a

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prospective pilot study

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Authors

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Affiliations

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TX, USA

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Birmingham, AL, USA

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Nadeeja N. Wijayatunga Bridget Ironuma

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John A. Dawson

Bailey Rusinovich

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Candice A. Myers Michelle Cardel Gregory Pavela Corby K. Martin

Emily J. Dhurandhar

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David B. Allison

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Department of Kinesiology and Sports Management, Texas Tech University, Lubbock,

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Department of Nutritional Sciences, Texas Tech University, Lubbock, TX, USA Center for Biotechnology & Genomics, Texas Tech University, Lubbock, TX, USA Pennington Biomedical Research Center, Baton Rouge, LA, USA Department of Health Outcomes & Policy, University of Florida, FL, USA Department of Health Behavior, School of Public Health, University of Alabama,

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Running Title:

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Subjective social status and energy balance

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Contact Info:

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Emily J. Dhurandhar

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[email protected]

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MS 3011, Department of Kinesiology and Sports Management, Texas Tech University,

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Lubbock, TX, USA

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School of Public Health-Bloomington, Indiana University, Bloomington, IN, USA

John Dawson – [email protected] Bailey Rusinovich – [email protected] Candice A. Myers – [email protected]

Gregory Pavela – [email protected]

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Michelle Cardel – [email protected]

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Bridget Ironuma – [email protected]

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Nadeeja Wijayatunga – [email protected]

Corby K. Martin – [email protected] David B. Allison – [email protected]

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Emily J. Dhurandhar – [email protected]

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Author Contributions:

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Study design: EJD; Data collection: EJD, CM, CAM; Data analysis: NNW, JAD, EJD, BI,

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BR, DBA; Data interpretation: EJD, JAD, NNW, MC, GP, DBA; Literature search: EJD,

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GP, MC, NNW; Generation of figures: NNW, JAD; Drafting of the manuscript: EJD,

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NNW; Editing the manuscript: All.

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Abstract

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Objectives: Subjective social status (SSS) is known to be inversely associated with

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obesity. Our objective was to determine if SSS is associated with eating behaviors that

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would predispose to weight gain, specifically, with inadequate compensation for excess

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energy consumed during a single large meal. Therefore, we conducted a pilot study to

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determine the association of SSS with 24-hour energy balance, 24-hour and post-lunch

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energy intake, changes in body composition and changes in adjusted resting energy

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expenditure on days when a high-energy lunch was consumed in free-living human

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subjects.

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Method: Female participants (7 normal weight and 10 overweight) consumed 60% of’

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estimated 24-hour energy requirements as a lunchtime meal in the laboratory for 14

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days. Subjective social status was measured at baseline using the MacArthur Scale.

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Remote Food Photography Method was used to record food intake outside of the lab on

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days 1-2, 7-8, and 12-13. Associations of 24-hour energy balance, 24-hour and post-

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lunch energy intake, changes in adjusted resting energy expenditure and changes in

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percent body fat (measured by dual x-ray absorptiometry) with SSS were studied.

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Results: Mean (standard deviation) age and BMI were 36.29(8.25) years and

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26.43(2.32) kg/m2, respectively. Lower SSS was significantly associated with positive

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energy balance (p for trend 0.002), and higher post-lunch energy intake (p=0.02) when

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controlled for age and initial body mass index.

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Conclusions: Our pilot data show that lower SSS is associated with higher post-lunch

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energy intake, which is indicative of poor energy compensation following a large meal.

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Over a longer time period, this could result in fat mass gain. Studies that are of longer

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duration and well-powered are warranted to confirm our findings.

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Keywords: Subjective social status, Socioeconomic status, Obesity, Energy balance

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BMI

Body Mass Index

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BMR

Basal Metabolic Rate

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DXA

Dual Energy X-Ray Absorptiometry scan

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NHANES

National Health and Nutrition Examination Survey

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PAL

Physical Activity Level

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REE

Resting Energy Expenditure

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RFPM

Remote Food Photography Method

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SES

Socioeconomic Status

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SSS

Subjective Social Status

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US

United States

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VAT

Visceral Adipose Tissue

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Acknowledgement: We would like to acknowledge Helen Kidane, Suzanne Choquette,

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Betty Darnelle, and Sandya Bhoyar for their help with data collection.

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Funding:

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This work was supported in part by the by University of Alabama Birmingham Nutrition

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Obesity Research Center (Award Number P30DK056336) from the National Institute 5

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of Diabetes and Digestive and Kidney Diseases and by NIH grants R25DK099080 and

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R25HL124208. In addition, this study was supported in part by NORC Center Grant #

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P30 DK072476 entitled “Nutrition and Metabolic Health Through the Lifespan”

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sponsored by NIDDK and U54 GM104940 from the National Institute of General

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Medical Sciences of the National Institutes of Health, which funds the Louisiana

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Clinical and Translational Science Center and National Institutes of Health National

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Heart, Lung, and Blood Institute (R01HL120960) and the National Center For

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Advancing Translational Sciences of the National Institutes of Health (UL1TR001427).

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The content is solely the responsibility of the authors and does not necessarily

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represent the official views of the National Institute of Diabetes and Digestive and

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Kidney Diseases or the National Institutes of Health or any other organization.

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Disclosure: The authors declared no conflicts of interest.

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Introduction

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Obesity is a major chronic disease in the United States (U.S.) and 38% of adults have

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obesity as of 2014 (1). In high-income countries, including the United States, obesity is

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more prevalent among those with lower socioeconomic status (SES), especially in

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women (2-5). In high-income countries, women who are food insecure have a 50%

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higher risk of having a high body weight than those who are food-secure, while no

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association is present for men (5). The association between low SES and obesity is

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complex and multiple theories have been suggested.

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Nettle et al. described the insurance hypothesis where humans increase energy intake

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over expenditure, resulting in fat storage, when they are uncertain about an adequate

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supply of food (5). The resource scarcity hypothesis is an extension of the insurance

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hypothesis and proposes that individuals who perceive food insecurity would be in

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positive energy balance, specifically when there is access to highly caloric food, and this

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effect would be specific to those with low social status only (6). The resource scarcity

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hypothesis may explain the difference seen in the relationship between social status

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and obesity in high- and low-income countries. Food insecure individuals consume high

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energy food and store excess body fat as insurance only when they have access to

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food, and there is more access to high energy density access food in high-income

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countries, whereas this is not the case in low income countries (5, 6). The life history

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theory proposes that exposure to an unpredictable environment during childhood

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impacts eating behavior, and explains in part the effects of low SES during childhood on

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obesity as an adult (7). Individuals with lower childhood SES tend to have marked

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impulsivity and a focus on short-term goals as adults, and this may result in problems

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with weight-management behaviors (7). Hence, studies that suggest social status may

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influence our decision-making, combined with theoretical frameworks from evolutionary

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biology, suggest that social status may play a causative role in weight gain.

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The association between low SES and obesity may not be a simplistic function of

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financial resources. Lower SES is associated with food, employment and housing

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insecurities, which affect the diet of the individual and the family (8). Affluence is

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associated with the consumption of nutrient-rich, high quality diets, whereas poverty is

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associated with the consumption of more affordable, low cost, energy-dense and

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nutrient-poor diets (9). However, the provision of extra resources in the form of cash

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transfers has resulted in an increased prevalence of overweight and obesity in adults

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and children (10, 11). Briefly, a large-scale, Mexican conditional cash transfer (CCT)

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program was conducted to alleviate poverty and the participants received cash

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transfers. Unexpectedly, higher BMI and increased prevalence of overweight and

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obesity were associated with receiving more money (10). Similarly, participation in the

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Supplemental Nutrition Assistance Program (SNAP, previously and still commonly

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referred to by ‘food stamps’) is associated with higher BMI in adults, independent of

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perceived food insecurity; this may be due to disordered eating patterns as a result of

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receiving monthly SNAP funds or could be due to increased, that increase consumption

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of energy-dense food (12).

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Subjective social status (SSS) is defined as “a person's belief about his / her location in

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a status order” (13). The MacArthur Scale of Subjective Social Status is a commonly

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used method to measure SSS at the society or community level. This scale is

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represented by a ladder; at the top of the ladder are people who are best off with the

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most amounts of money, education and best jobs, while the people who are the worst

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off are at the bottom of the ladder (14). Education, occupation, household income,

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feeling of future financial security, satisfaction with living standards, physiological

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functioning, strength and sickness contribute to SSS (15, 16). Interestingly, SSS

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appears to mediate the association between objective measures of SES (education and

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income) and BMI (17). According to a prospective cohort study, SSS is a strong

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predictor of ill health, irrespective of education, occupation and income (15). Lower SSS

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is also associated with increased risk of hypertension, dyslipidemia, coronary artery

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disease, diabetes and obesity (16, 18). Such associations between SSS and health

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outcomes may be due to the effects of perceived social status on the underlying

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physiology of feeding behavior, which may impact body size and body fatness (12, 19-

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22). It is often hypothesized that dietary factors associated with SES may drive obesity

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in low SES populations. However, this has recently been challenged by the idea that

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social status may be a fundamental driver, or cause, of these dietary behaviors and

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weight gain (5, 6, 23) Furthermore, the lack of effectiveness of interventions (10-12) that

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seeks to manipulate material resources on obesity also suggests that perceived social

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status, not simply SES, may cause weight gain. Considering these findings, SES may

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be a better indicator of access to valued resources while SSS may be a better indicator

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of security and sense of control.

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Recent studies have experimentally induced lower or higher SSS (24, 25). Cheon et al.

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conducted four studies where low (vs. high or neutral) socioeconomic status was

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experimentally induced in the participants. They observed that the subjective

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experience of low social class resulted in a preference for high energy foods and

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increased intake of energy from meals and snacks independent of financial resources

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(24). Cardel et al. also conducted a randomized crossover study in Hispanic young

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adults to experimentally manipulate social status conditions using a game of

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Monopoly™. Fasted participants consumed a standardized breakfast and were

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randomly assigned to either a high or low social status condition. Next, high vs. low

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status participants played a rigged game of Monopoly™ where the rules were different

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for each group (e.g., double the resources were given to those in the high status group).

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Following the game, the participants consumed lunch ad-libitum. Individuals reported

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decreased feelings of pride and powerfulness and consumed approximately 130 more

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kilocalories when placed in the low social status condition when compared to the high

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social status condition (25). Pavela et al. used a different strategy to experimentally

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induce higher or lower social status by assigning participants to be a leader or follower

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in a partner activity (26). However, they did not observe any difference in energy intake

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between the two groups as in the previous studies and this method to induce a relative

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difference in SSS may have not been effective. In a randomized social experiment, the

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intervention group moved from a high-poverty neighborhood to a low-poverty

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neighborhood. Interestingly, prevalence of obesity was reduced only in the intervention

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group (19), again highlighting the connection between SSS and obesity.

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In theory, energy balance can be achieved by controlling energy intake or energy

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expenditure and one is said to be in “energy balance” when energy intake is equal to

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energy expenditure, which results in a stable body weight (27). However, when energy

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intake is greater than expenditure, it is known as “positive energy balance” and this

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results in weight gain (27). Generally, compensatory mechanisms to defend against

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negative energy balance and weight loss may be stronger than for those against

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positive energy balance with weight gain. In American culture, individuals are frequently

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exposed to large meals in social or restaurant settings, and their ability to recognize

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these occasions and adjust their energy intake or expenditure to account for them may

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be an important indicator of obesity risk. Indeed, certain individuals are more

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predisposed to have inadequate compensation for excess energy and weight gain (28).

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We hypothesize that the experience of low SSS may be one determining factor that

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predisposes an individual to inadequate compensation for excess energy and weight

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gain. Even though experimentally induced lower SSS results in acute increases in

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energy intake, the association between actual SSS and energy-intake has not been

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observed in long-term studies, and the external validity of the various operationalization

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of social status (e.g., being randomly assigned as a “leader” or “follower” in a partner

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activity) have not been verified. Furthermore, there is a gap in the current literature

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regarding the effect of SSS on energy balance and eating behavior. Therefore, we

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conducted a novel observational, large meal challenge pilot study wherein female

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participants of varied SSS consumed a large lunchtime meal for 14 days, and we then

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monitored their post-lunch energy intake to determine how SSS is associated with their

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ability to adjust their energy intake to maintain energy balance despite the large meal

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challenge. Another purpose of this pilot study is to help to determine sample size for a

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larger scale study in the future.

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Our primary objective is to understand the association of SSS with 24-hour energy

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balance during the large meal challenge, as an indicator of weight gain propensity, and

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our secondary objectives are to identify the association of SSS with 24-hour energy

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intake, post-lunch energy intake, changes in body composition and adjusted resting

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energy expenditure (REE) following a high-energy lunch for 14 days in free-living

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human subjects. Our central hypothesis is that lower SSS will be associated with

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inadequate compensation for excess energy consumed during a single large meal (i.e.,

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the large meal provided to participants as part of the study). Further, we hypothesize

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that lower SSS will be associated with positive energy balance, to the extent that it

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leads to body fat gain in response to daily large meals over a 14-day period.

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Material and Methods

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We have used the CONSORT 2010 checklist of information to include when reporting a

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pilot or feasibility randomized trial (Appendix A- Table S1) (29). The trial was registered

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at clinicaltrials.gov, protocol number NCT03510364.

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Ethical concerns

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The protocol to protect our human subjects was approved by the Institutional Review

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Boards of University of Alabama, Birmingham (Protocol number F131010007) and

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Texas Tech University, Lubbock, TX (Protocol number IRB2016-571). We obtained

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informed consent from participants at the time of enrollment in the study.

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Study design

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This is a prospective feeding pilot study with convenience sampling and the study was

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conducted over 14 days. Figure 1 illustrates the study design. Participants were blinded

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regarding the aim of the study and were debriefed at the end of the study. Participants

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were told that the effect of diet macronutrient composition on resting metabolic rate was

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being measured.

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Figure 1. Study design Abbreviations: S1 (First phone screening), S2 (Second in-person screening), Q (Questionnaire based data collection), RFPM (Remote Food Photography Method), REE (Resting Energy Expenditure), DXA (Dual Energy X-Ray Absorptiometry scan)

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Fliers were posted in community locations around the University of Alabama at

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Birmingham campus, which is situated in the central part of the city. Twenty one women

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were recruited from the University of Alabama at Birmingham and surrounding area and

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data was collected between June 2014 and June 2015. First, a script-based telephone

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screening was used to identify eligible participants and was followed by in-person

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screening. Inclusion criteria included: between 20-50 years old, with BMI between 23-30

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kg/m2, no food allergies or food restrictions, not engaged in any weight reduction

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program within the past 3 months, not experienced any weight loss or gain of more than

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5% of body weight in the past 6 months other than due to post-partum weight loss, not

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on appetite suppressant or stimulant medication, not having undergone prior surgical

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procedures for weight control or liposuction, does not smoke or has not smoked in over

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6 months, does not have any major diseases such as cancer at present or had cancer

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that was treated in the past 2 years (except non-melanoma skin cancer), active or

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chronic infections (e.g., HIV or TB), cardiovascular disease, gastrointestinal disease,

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kidney disease, chronic obstructive airway disease that requires oxygen (e.g.,

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emphysema or chronic bronchitis), diabetes (type 1 or 2) and on anti-diabetic

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medications and/ or controlling with dietary modifications, uncontrolled psychiatric

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disease, not have a recent or ongoing problem with drug abuse or addiction, does not

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consume more than three alcoholic drinks per day and has not had 7 or more alcoholic

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beverages in a 24 hour period in the last 12 months, currently pregnant or within 3

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months post-partum, not currently nursing or completed nursing within the last 6 weeks,

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and not anticipating a possible pregnancy during the study.

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Dietary intervention

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For the feeding intervention, participants consumed a meal containing 60% of their

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estimated energy daily energy requirement as a lunchtime meal for 14 consecutive days

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under observation. On weekends, participants were allowed to bring the packed lunches

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home if necessary but were encouraged to consume them under observation in a

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tertiary location outside UAB’s Bionutrition Unit, which was closed on weekends. To

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ensure the participant receives 60% of the daily energy requirement as a lunch meal we

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added a supplemental shake to a standard 1200 kcal meal (Details of the shake and

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meals are in the Appendix B - Table S2 and S3). We determined the lunch calories to

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be provided to each participant based on their daily energy requirement calculated

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using basal metabolic rate (BMR) measured via indirect calorimetry at the baseline. We

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used a physical activity level value (PAL) of 1.4 (30), and we assumed that our

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participants were sedentary. Thus, target lunch calorie intake was calculated as

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BMR*1.4*0.6.

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Data collection

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A questionnaire was administered at baseline to collect sociodemographic data. We

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inquired about income, debt and food insecurity. These questions are provided in

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Appendix B. In the same questionnaire, we determined subjective social status of the

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participants, using the MacArthur scale of Subjective Social Status (SSS) (14) which

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uses an image of a ladder with ten rungs (Appendix B Figure.S1). The description

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provided regarding the ladder was, “At the top of the ladder are the people who are the

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best off – those who have the most money, the most education and the most respected

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jobs. At the bottom are the people who are the worst off – who have the least money,

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least education, and the least respected jobs or no job. The higher up are on this ladder,

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the closer you are to the people at the very top; the lower you are, the closer you are to

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the people at the very bottom”. Participants were asked to mark a “X” on the rung where

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they “thought” they stood at that particular time in their lives, relative to the other people

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in the United States (14).

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We measured height using a stadiometer with the participant standing straight, facing

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forward, looking straight ahead, heels touching the stadiometer, and the horizontal

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headpiece touching the crown of the head. Height was recorded in cm and rounded to

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the nearest 0.1 cm. Weight was recorded to the nearest 0.1 kg. BMI was calculated as

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height divided by height squared. If it did not fall between 23 and 30, the participant did

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not qualify and was excluded. Both pre- and post-intervention body fat percentage was

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measured using dual energy X-ray absorptiometry (DXA) (GE-Lunar Radiation Corp.

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Madison, WI) and resting metabolic rate was measured using indirect calorimetry (Vmax

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ENCORE 29N Systems, SensorMedics Corporation, Yorba Linda, CA).

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Each food item provided in the lunchtime meal in the laboratory was weighed before

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consumption, and any remaining food was weighed using electronic weighing scales.

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Participants were allowed to eat ad-libitum outside the laboratory. On days 1 and 2

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(early), 7 and 8 (middle), and 12 and 13 (late), food intake outside the lab was recorded

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using remote food photography (31, 32). For this, participants were first trained to use

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the SmartIntake application, an iPhone app to track their food intake, over a three-day

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period prior to the feeding intervention. In addition, participants used a food diary as a

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backup method if they forgot to record their food intake using the RFPM method. Thus,

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we assumed that the energy intake recorded during meals and snacks outside the

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laboratory are complete and that there are no missing data.

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Statistical analysis

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Data are presented as mean ± standard deviation (SD). We used IBM® SPSS version

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25 and R 3.5.0 statistical software for our analyses; please see below for further detail.

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Our primary outcome measure was 24-hour energy balance. Our secondary outcome

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measures included 24-hour energy intake, post-lunch energy intake, and difference in

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adjusted REE and changes in body composition.

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In our data, there were missing values for some lunchtime energy intakes;

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corresponding calculations of energy intake and energy balance were also missing for

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those subjects on those days. These missing values were handled through massive

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multiple imputation using the mice R package. Default settings were used (e.g.,

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predictive mean matching) except that we increased the number of imputations to 30

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and increased the maximum number of iterations to 20. These parameters were set to

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these higher values to make sure that we were compensating for the relatively small

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fractions of missing information exhibited by our data. Additionally, there was a single

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missing value in the results from each of the instruments measuring income and total

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debt; the tau-b extension to Kendall’s tau functionally ignores such an individual.

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We calculated the adjusted REE values by correction for lean mass (REE corrected =

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(REE / lean mass). The difference in adjusted REE over time was calculated as (Final

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REE / Final LM) – (Initial REE / Initial REE). Percent change in BMI, fat mass and

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visceral fat were calculated as [100*(final value – initial value) / initial value]. Post-lunch

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energy intake was calculated as the sum of energy intake consumed at dinner,

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afternoon snacks, and evening snacks recorded by the RFPM method. If a meal was

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not recorded via photos app, participants were immediately reminded to record their

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meal using a food diary and those data were used in place of photography data.

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Twenty-four hour energy intake was calculated as the sum of calories consumed during

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all meals and snacks during a 24-hour period. We calculated the energy expenditure as

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resting energy expenditure (REE) multiplied by 1.4 (a sedentary physical activity level)

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(30). Energy balance was calculated as follows: Energy balance = 24-hour Energy

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intake / Energy expenditure. For the early (Day (D)1 and 2) and middle (D7 and 8) time

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points, we used baseline REE value and for the late time point we used post-feeding

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REE (D12, D13) REE values for the above calculation.

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To identify association of race with SSS and adjusted REE, we used Mann Whitney U

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tests. For comparison of BMI, body fat, lean mass, REE and adjusted REE at basal and

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post feeding time points, we used paired t-tests after confirming normality using the

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Shapiro-Wilk test. Energy balance was modeled as a function of period (1-2, 7-8, and

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12-13 days) using a mixed linear effects model, while adjusting for subject as a random

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intercept using the lme4 and lmerTest packages in R to study if energy balance change

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with time while on a high calorie lunch; here and for all analyses, when missing values

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were present, a pooled analysis was obtained from the massive multiple imputation.

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Associations between SSS and energy balance, 24-hour total energy intake, and post

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lunch energy intake were assessed using linear models, controlling for age and baseline

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BMI using a mixed model (with subject as a random intercept). Because an energy

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balance percentage of 100% corresponds to perfect energy balance, and 50% and

392

200% are the same magnitude but in different directions, the natural log of energy

393

balance as a proportion was used in all analyses involving energy balance (e.g., 100%

394

becomes 0, 200% becomes 0.69315 and 50% becomes -0.69315). SSS could be

395

treated as a continuous variable or a factor whenever it was used in a linear or mixed

396

linear effects model; the p-values for trend that we will report treat it as a continuous

397

variable.

398

Furthermore, associations between SSS and difference in adjusted REE and percent

399

changes in body composition (BMI, fat mass and lean mass) were studied using linear

400

models while controlling for age. Kendall’s tau-b correlation analysis was performed

401

between SSS and income, education, debt, body composition and food insecurity

402

measures.

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Results

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We recruited 21 participants and 17 completed the study. Details of study participation,

407

handling of missing data, compliance and respective analysis are illustrated in Figure 2.

408

All participants were compliant, with each participant having an average lunch intake of

409

80% or more of their intended lunch intake (i.e. 60% of their daily energy requirement)

410

over the 14 days (for this determination, averages over imputed values were used when

411

lunch values were missing). All n=17 subjects were used in the analyses.

412

Baseline characteristics and associations with subjective social status

413

Our participants had SSS ranging from 3 to 8 out of a scale of 10. SSS was not

414

significantly correlated with indicators of socioeconomic status including income (τb=

415

0.346, p = 0.11, n=16), debt (τb = -0.206, p = 0.33, n=16) and education (τb = -0.102, p =

416

0.33, n=17). In addition, SSS was not significantly correlated with food insecurity

417

(Question 4: τb = -0.154, p = 0.49, n=17 and Question 5: τb = -0.211, p = 0.35, n=17).

418

We did not observe significant differences in SSS between non-Hispanic whites and

419

non-Hispanic blacks (two-sample Wilcoxon p=0.63, n=17 (12 and 5, respectively)).

420

Furthermore, we did not observe significant correlations of SSS with BMI (τb = 0.080, p

421

= 0.67, n=17) and body fat percentage (τb = 0.177, p = 0.35, n=17) at baseline.

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Figure.2 Flow diagram illustrating study participation, handling of missing data, compliance and respective analysis grouping. All participants were considered compliant given that their average energy intake at lunch was 80% or more of the intended amount.

428

Effects of high calorie meal on body composition and energy balance

429

The socio-demographics and pre- and post-feeding measurements are summarized in

430

Table 1. Even though a high calorie lunch was provided over 14 days, participants did

431

not show significant increases in BMI, lean mass or fat mass (all p > 0.05) (Table 1).

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Table 1. Socio-demographics and pre- and post- feeding measurements (n=17) P value

12:5

1 1 4 5 3 3 36.29 (8.25) 26.43 (2.32) 42.80 (4.47) 27.61 (6.92) 38.73 (4.64) 0.50 (0.46) 1247.53 (176.36) 29.15 (2.80)

0.194 0.277 0.452 0.940 0.515 0.104 0.119

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26.57 (2.30) 43.08. (4.54) 27.72 (6.78) 38.75 (4.18) 0.51 (0.43) 1320.53 (209.94) 30.62 (3.27)

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Race Non-Hispanic white: Non-Hispanic black Subjective social status score of: 3 4 5 6 7 8 Age (years) BMI (kg/m2) Lean mass (kg) Fat mass (kg) Fat mass % Visceral fat (kg) Resting energy expenditure (kCal/ 24hours) Adjusted resting energy expenditure (kCal/ 24hours/per kg of lean muscle) Data are shown as Mean (SD), proportions or as frequencies. P values based on paired t-test analyses.

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We observed that average resting energy expenditure increased by ~73 kCal/ 24 hours

437

by the end of the intervention but was not statistically significant (p>0.05) (Table 1).

438

Similarly, pre- and post-intervention adjusted REE were not significantly different

439

(p>0.05) (Table 1).

440

Positive energy balances of intake at 106.6%, 111.7% and 108.9% of energy needs

441

were observed at early (Day 1 and 2), middle (Day 7 and 8) and late (Day 12 and 13)

442

time periods, respectively (n=17); however, the latter two energy balance numbers are

443

not significantly different than the 106.6% of the early period (p=0.49 and p=0.76,

444

respectively).

445

Associations between subjective social status, energy balance, and body

446

composition following a high calorie meal

447

When controlling for age and baseline BMI, we observed that lower SSS was

448

associated with increased log energy balance (p for trend = 0.002). This association

449

between SSS and logged energy balance is shown in Figure 3, with a linear trend

450

superimposed over the best estimates obtained by treating SSS as a factor. We did not

451

control for race since there was no significant difference in baseline adjusted REE

452

between non-Hispanic whites vs non-Hispanic black (two-sample Wilcoxon p=0.23,

453

n=17).

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Figure 3. Association between subjective social status and log energy balance over 14 days (n=17). The loess smoothed fit line indicates the trend. We have reported log 24-hour energy balance. Thus, “0” indicates energy balance; positive values indicate positive energy balance, while negative values indicate negative energy balance.

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Average post lunch energy intake was significantly inversely associated with SSS

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(Figure 4) in the full sample (p for trend = 0.02) when controlled for age and baseline

463

BMI. Exhibiting a similar pattern, 24-hour energy intake was not significantly inversely

464

associated with SSS (p for trend = 0.20) when controlled for age and baseline BMI. In

465

general, these results are being driven by the subjects with the lowest and highest

466

observed SSS (scores of 3 and 8, respectively), as these subjects have the highest

467

leverage in the data set.

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When controlling for age, SSS was not associated with percent change in BMI, fat mass

469

or visceral adipose tissue (VAT) (p= 0.84, 0.68 and 0.52, respectively). Furthermore,

470

SSS was not associated with change in lean mass adjusted REE, when controlling for

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age (p=0.67).

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Figure 4. Association between subjective social status and post lunch energy intake over 6 days (n=17).

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DISCUSSION

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In this pilot study, we employed a novel approach to identify associations between SSS

478

and energy balance and energy intake in free-living females with normal and overweight

479

status. While controlling for age and baseline BMI, we assessed the association

480

between ad-libitum meal intake and energy balance following a high calorie meal

481

challenge, which was 60% of participant’s energy requirements, over 14 days. We

482

selected the duration as 14 days since it is relatively longer duration that may be

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needed for persistent effects on energy balance to result in measurable changes in

484

energy stores.

485

We report that SSS was inversely associated with 24-hour energy balance during 14

486

days of high calorie lunch in free-living females. 24-hour energy intake was not

487

significantly inversely associated with SSS, so any influence on energy balance through

488

energy intake is only apparent after adjusting for an individuals’ energy needs. Changes

489

in lean mass adjusted REE similarly did not show an association with SSS. Thus, the

490

inverse association between SSS and 24-hour energy balance is likely to be driven by

491

the 24-hour energy intake.

492

These findings are consistent with previous research findings that experimentally

493

induced lower social status resulted in higher energy intake in humans and primates

494

(33, 34). It has been previously shown that selection of high calorie food is predicted by

495

perception of scarcity, and not taste (35). Hence, participants with lower SSS may

496

continue to consume more calories because of their perception of low social status and

497

perception of scarcity. Sim et al., have shown that both acute and chronic subjective

498

deprivation of non-food resources are associated with increased consumption food and

499

stronger desire to consume large portion sizes (36). Our findings are also in line with

500

previous research that demonstrates individuals with food insecurity tend to consume

501

poor quality, energy dense food and tend to have disinhibited eating or are prone to

502

overeat (8).

503

The association of lower SSS with positive energy balance in our study may be driven

504

by differences in acute energy intake following the consumption of a much larger meal

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than what is normally consumed. We observed a statistically significant association

506

between SSS and post-lunch energy intake at the meals immediately following the large

507

lunch, specifically, such that individuals with lower SSS consumed more energy

508

following a large lunch. Thus, individuals with lower SSS may not experience the same

509

inhibition and sensation of satiety as higher SSS individuals for eating meals later the

510

same day. However, we did not measure satiety, fullness, gastric emptying, or any

511

satiety hormones in this study. Measuring differences in physiological regulation of

512

acute eating behaviors between social status categories would be a logical next step in

513

understanding the physiological roots of this difference in eating behavior.

514

One novel aspect of this study is that the experimental design allowed for observation of

515

differences in compensatory mechanisms to maintain energy balance, in spite of a large

516

meal challenge. A common belief is that an individual gains about 5 pounds or more

517

following a holiday, and this is due to the large energy-rich meals consumed with

518

holiday traditions. However, average holiday weight gain is minimal, and is about 0.5 kg

519

or less (37). Energy balance perturbations such as holiday meals, or our large meal

520

challenge, likely result in both metabolic and behavioral compensations. In our study,

521

we were able to measure aspects of both behavioral and metabolic compensation for

522

the large meal. We studied REE pre and post intervention, to detect metabolic

523

compensation, but did not observe any significant changes in REE over the 14-day

524

period. Instead, we measured individual differences in behavioral compensation, in the

525

form of continued high post-lunch energy intake and found that it was associated with

526

SSS. Based on this finding, one would expect to see an increase in body weight of

527

individuals with lower SSS, because they did not compensate for the high calorie meal

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challenge. However, we did not observe any association between SSS and percent

529

change in BMI or fat mass, and a study period longer than 2 weeks may be needed to

530

observe a change significant association of SSS with change in body composition.

531

There are several strengths of this pilot study. Unlike the previous studies that

532

investigated SSS and energy consumption by experimentally manipulating SSS, we

533

considered the actual SSS level in our participants. We blinded our participants

534

regarding the real purpose of the study in an attempt to prevent corresponding biases.

535

In order to ensure that the participants got a high calorie lunch we provided 60% of their

536

daily energy requirement as a lunch time meal in the laboratory setting. However, the

537

post-lunch meals were taken in a free-living setting. Hence, our study design allows our

538

findings to be generalized more broadly to free-living settings. We used the RFPM

539

method to track energy intake, which has high accuracy among adults (38), to capture

540

the post-lunch meal intake on selected days. Another important strength is that our

541

study was of longer duration. We conducted our study over 14 days to overcome any

542

day-to day variations that can occur with an individual’s food consumption.

543

There are some limitations in this pilot study. First, we did not measure physical activity

544

and account for its variation among participants in our calculations and assumed

545

everyone had a sedentary physical activity level. Because we did not study the basal

546

food intake in our study participants, we also cannot comment whether provision of high

547

calorie meal changed their feeding behavior. Another limitation is that our sample size is

548

small. Hence, our ability to be confident about the non-significant findings reported in

549

this study is limited. These negative findings may be true negative or false negative

550

results due to type II errors. However, even with a small sample size, we were able to

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observe significant association of SSS with energy balance and post-lunch energy

552

intake. Furthermore, our study was not a randomized controlled study. Hence, we

553

suggest a larger scale randomized trial in the future that involves strategies to

554

manipulate subjective social status. Our study participants included only females and

555

this was decided during study design because lower socioeconomic status (SES) is

556

associated with obesity in females in developed countries, but the association is not

557

observed in males (39). Also, female sex has a direct relationship with SSS (17). Thus,

558

our findings cannot be generalized to males.

559

participants who were either normal or overweight based on BMI despite the noted

560

limitations of using BMI to define obesity (40).There are several theories as to why SSS

561

may have a causal effect on obesity. Financial insecurity and desire for money may

562

increase consumption of palatable, energy dense food that may lead to weight gain over

563

time (16, 41). According to Insurance and Resource scarcity hypotheses, individuals

564

increase energy intake more than the expenditure in the presence of uncertainty about

565

adequate food (5, 6). Evolutionary biology suggests that organisms may respond to

566

perceived energetic insecurity by storing energy as fat as a survival mechanism to

567

ensure longevity and successful reproduction (22). The latter hypothesis suggests that

568

perception of the environment, rather than the environment itself, is critical for regulating

569

eating behavior and body fat stores. Thus, obesity may be an evolutionary adaptive

570

response to food insufficiency and energy storage is increased in response to food

571

insecurity (6, 16).

572

SES and perceived stress are associated (42), and this is an important consideration in

573

the interpretation of our findings. Adler et al. have reported that subjective social status

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574

and chronic stress are significantly associated after controlling for objective SES (43).

575

SSS has shown to be associated with depressive cognition (44). Thus, SES and

576

insecurities that accompany it are stressors that threaten one’s well-being, and may

577

cause

578

hypothalamic−pituitary−adrenal axis (8). This may lead to consumption of a highly

579

palatable, energy dense diet poor in quality to help reduce stress response, and stress

580

hormones may favor deposition of excess calories as fat in the central part of the body

581

(8). Even though SSS is related to stress, its effects are distinct from stress. For

582

example, in the MonopolyTM study by Cardel et al., participant’s perceived stress did not

583

change significantly when they were placed in high/low social status conditions, but their

584

perceived pride and powerfulness did change significantly and being in a low social

585

status position resulted in significantly higher percentage of daily calorie needs and

586

saturated fat consumed (25).

587

This pilot study is useful to plan a larger scale study. We identified that the sample size

588

that is required to detect the largest observed correlation observed in this study. The

589

effect size was 0.34 for the association of energy balance with SSS for this

590

experimental paradigm, which means a sample size of 50 subjects would yield 80%

591

power at an alpha of 0.05. Also, the predicted energy balance in individuals with the

592

lowest SSS (McArther scale score = 3) was 152.6% while in those with the highest SSS

593

had a predicted energy balance of 81.8% during this experimental feeding challenge

594

paradigm. This degree of positive energy balance in the low social status individuals

595

would conceivably produce up to about 3.2-3.6 kg weight gain over a 14-day period in a

596

subject representing the average subject in this study, according to the NIH-Body

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Weight Planner Calculator by Hall et al. (45). However, we did not observe this degree

598

of weight gain in our study, potentially because our projections did not account for any

599

changes in spontaneous physical activity or physical activity energy expenditure.

600

Therefore, we recommend 1 month or longer duration would be optimal for future

601

studies to detect weight gain associations with SSS, and we would also recommend

602

that future studies measure energy expenditure via accelerometer or a comparable

603

method.

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Conclusions

606

In our pilot study, we found some evidence to suggest that a lower score on the

607

MacArthur Scale of Subjective Social Status is associated with increased energy

608

balance and reduced ability to compensate for a large meal. Thus, compensation for

609

large meal perturbations in normal caloric consumption may be less accurate in

610

individuals with lower SSS. These findings merit confirmation in larger scale studies in

611

the future.

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status on depressive thinking: An experimental examination. Psychiatry Research.

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2016;241:22-5.

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45. Hall KD, Sacks G, Chandramohan D, Chow CC, Wang YC, Gortmaker SL, et al.

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Quantification of the effect of energy imbalance on bodyweight. Lancet.

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2011;378(9793):826-37.

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Appendix A

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Table S1. CONSORT 2010 checklist of information to include when reporting a pilot or feasibility randomized trial in a journal or conference abstract (29) Item Description Notes Line number Title Identification of study as randomized pilot or 2-3 feasibility trial Authors * Contact details for the corresponding author 4-14 Trial design Description of pilot trial design (eg, parallel, 2-3 cluster) Methods Eligibility criteria for participants and the settings Participants 276-298 where the pilot trial was conducted We have only one group. Not a Interventions Interventions intended for each group 299-311 randomized trial Objective Specific objectives of the pilot trial 245-250 24-hour energy balance, 24-hour energy Pre-specified assessment or measurement to 347-350 Outcome intake, Post-lunch energy intake, Percent address the pilot trial objectives**- given below changes in BMI, fat mass, lean mass, Change in adjusted resting energy expenditure Not a randomized trial How participants were allocated to interventions NA Randomization Whether or not participants, care givers, and Single blinded 266-267 Blinding those assessing the outcomes were blinded to (masking) group assignment Results Number of participants screened and randomized Not a randomized trial Numbers NA to each group for the pilot trial objectives** randomized Recruitment Trial status† Not applicable NA Numbers Number of participants analyzed in each group for 401 38

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the pilot objectives** Results for the pilot objectives, including any expressions of uncertainty** Harms Important adverse events or side effects General interpretation of the results of pilot trial Conclusions and their implications for the future definitive trial Trial Registration number for pilot trial and name of trial registration register Funding Source of funding for pilot trial *this item is specific to conference abstracts **Space permitting, list all pilot trial objectives and give the results for each. Otherwise, report those that are a priori agreed as the most important to the decision to proceed with the future definitive RCT. †For conference abstracts

407-470 NA 580-584 258 109-122

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analyzed Outcome

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Appendix B – Supplements to Methods Table S2. Details food provided for lunchtime meal containing a total of 1200 kCal Food

Amount

Day 1

Stouffer's Meatball Sub Campbell's Soup at Hand Tomato Broccoli, frozen Dannon Greek yogurt Strawberries, frozen Almonds, dry roasted Little Debbie Fancy cakes Skim milk

195.6 g (1 package) 297.7 g (1 can) 150 g 150.3 g (1 container) 55 g 20 g 1 package (2 cakes) 236.6 ml

Day 2

Stouffer's Chicken Parmigiana Garlic bread Lettuce Kraft Italian dressing Skim milk Chocolate cake

Day 3

Hamburger bun, white Hamburger patty, ground round Kraft 2% cheese Ketchup Mustard Baked potato Butter, regular Fat-free sour cream Mixed vegetables Dutch apple pie Skim milk

1 medium 90 g 1 slice 1 package 2 packages 360 g, before cooking 10 g 28 g 1 cup 160 g (1/8 pie slice) 236.6 ml

Day 4

Stouffer's Lasagna w/Meat Sauce Garlic bread Lettuce Bacon bits Croutons Kraft Ranch dressing Baby carrots, raw Hummus dip Apple Skim milk Vanilla ice cream

326 g (1 package) 1 slice 90 g 10 g 10 g 24.8 g (2 package) 100 g 60 g 1 medium sized 236.6 ml 170.1 g (2 single cups)

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340.2 g (1 package) 1 slice 1/4 head 24.8 g (2 package) 236.6 ml 60 g

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Hoagie roll Chicken salad Kraft American cheese Sun chips, cheddar Mandarin oranges, juice packed Skim milk Kellogg's Rice Krispie treat

Day 7

Croissant, plain Deli turkey Cheddar cheese Light mayonnaise Mustard Mini pretzels Banana Yoplait Original yogurt Orange juice Quaker chewy granola bar (choc chip)

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Day 6

1 medium sized 90 g 1 slice 1 package 2 packages 32 g (single bag) 170 g 226.8 g 80 g

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Hamburger bun, white Chicken breast, baked Kraft Deli Deluxe cheese Light mayonnaise Mustard Baked Lay's potato chips Yoplait Light yogurt 100 calorie peaches, canned Brownie, plain

1 small (5.5 inch) 75 g 1 slice 42.5 g (single bag) 42.52 g 236.6 ml 74 g (King size bar)

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1 medium 80 g 56.7 g (2 slices) 1 package 2 packages 25.5 g (1bag) 1 medium sized 170 g 236.6 ml 1 bar

Table S3. Composition of 100kCal Shake Contents Carnation Instant Breakfast powder Whole milk

Amount 15 g 75 g

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1. What is the highest degree you earned? I. High school diploma or equivalent II. Associate degree III. Bachelor’s degree IV. Master’s degree V. Doctorate VI. Other

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Questions related to income, debt and food security

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2. Which of the following describes your total combined household income for the past 12 months? This should include income (before taxes) from all sources (wages, gifts, rent, benefits, social security, welfare, etc.). I. Less than $5,000 II. $5,000 through $11,999 III. $12,000 through $15,999 IV. $16,000 through $24,999 V. $25,000 through $34,999 VI. $50,000 through $74,999 VII. $75,000 through $99,999 VIII. $100,000 and greater

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3. What is the total debt within your household from credit card charges, medical or legal bills, and loans other than mortgage, car, or student loans? (Do not include mortgage, car loans, or student loans.) Just give me your best estimate. I. Less than $500 II. $500 to $4,999 III. $5,000 to $9,9999 IV. $10,000 to $19,999 V. $20,000 to $49,999 VI. $50,000 to $99,999 VII. $100,000 to $199,999 VIII. $200,000 to $499,999 IX. $500,000 and greater 4. Which one of these statements best describes the food eaten in your household last year? I. We have enough food to eat and the kinds of foods we want. II. We have enough food to eat, but not always the kind we want to eat. III. Sometimes we don’t have enough to eat. IV. Often, we don’t have enough food to eat. 5. Within the past 12 months, how often have you worried that your food would run out before you received money to buy more? I. Never II. Often III. Always 42

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Figure S1. MacArthur scale of Subjective Social Status (SSS) (14)

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