Ultra

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

2National Institutes of Health Clinical Center, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

4Singapore Institute for Clinical Sciences, Singapore.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

3National Institute of Nursing Research, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

2National Institutes of Health Clinical Center, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

2National Institutes of Health Clinical Center, Bethesda, MD.

1National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.

Author Contributions

We investigated whether ultra-processed foods affect energy intake in 20 weight-stable adults, aged (mean±SE) 31.2±1.6 y and BMI=27±1.5 kg/m2. Subjects were admitted to the NIH Clinical Center and randomized to receive either ultra-processed or unprocessed diets for 2 weeks immediately followed by the alternate diet for 2 weeks. Meals were designed to be matched for presented calories, energy density, macronutrients, sugar, sodium, and fiber. Subjects were instructed to consume as much or as little as desired. Energy intake was greater during the ultra-processed diet (508±106 kcal/d; p=0.0001), with increased consumption of carbohydrate (280±54 kcal/d; p

Hall et al. investigated 20 inpatient adults who were exposed to ultra-processed versus unprocessed diets for 14 days each, in random order. The ultra-processed diet caused increased ad libitum energy intake and weight gain despite being matched to the unprocessed diet for presented calories, sugar, fat, sodium, fiber, and macronutrients.

The perpetual diet wars between factions promoting low-carbohydrate, keto, paleo, high-protein, low-fat, plant-based, vegan, and a seemingly endless list of other diets has led to substantial public confusion and mistrust in nutrition science. While debate rages about the relative merits and demerits of various so-called “healthy” diets, less attention is paid to the fact that otherwise diverse diet recommendations often share a common piece of advice: avoid ultra-processed foods (Katz and Meller, 2014).

Ultra-processed foods have been described as “formulations mostly of cheap industrial sources of dietary energy and nutrients plus additives, using a series of processes” and containing minimal whole foods (Monteiro et al., 2018). As an alternative to traditional approaches that focus on nutrient composition of the diet, the NOVA (not an acronym) diet classification system considers the nature, extent, and purpose of processing when categorizing foods and beverages into four groups: 1) unprocessed or minimally processed foods; 2) processed culinary ingredients; 3) processed foods; and 4) ultra-processed foods (Monteiro et al., 2018).

While the NOVA system has been criticized as being too imprecise and incomplete to form an adequate basis for making diet recommendations (Gibney, 2019; Gibney et al., 2017; Jones, 2018), Brazil’s national dietary guidelines use the NOVA system and recommend that ultra-processed foods should be avoided (Melo et al., 2015; Moubarac, 2015). However, several attributes of ultra-processed foods make them difficult to replace: they are inexpensive, have long shelf-life, are relatively safe from the microbiological perspective, provide important nutrients, and are highly convenient – often being either ready-to-eat or ready-to heat (Shewfelt, 2017; Weaver et al., 2014).

The rise in obesity and type 2 diabetes prevalence occurred in parallel with an increasingly industrialized food system (Stuckler et al., 2012) characterized by large-scale production of high-yield, inexpensive, agricultural “inputs” (primarily corn, soy, and wheat) that are refined and processed to generate an abundance of “added value” foods (Blatt, 2008; Roberts, 2008). Ultra-processed foods have become more common worldwide (Monteiro et al., 2013; Moubarac, 2015), now constitute the majority of calories consumed in America (Martinez Steele et al., 2016), and have been associated with a variety of poor health outcomes (Fiolet et al., 2018; Mendonca et al., 2017; Mendonca et al., 2016), including death (Schnabel et al., 2019).

Ultra-processed foods may facilitate overeating and the development of obesity (Poti et al., 2017) because they are typically high in calories, salt, sugar, and fat (Poti et al., 2015) and have been suggested to be engineered to have supernormal appetitive properties (Kessler, 2009; Moss, 2013; Moubarac, 2015; Schatzker, 2015) that may result in pathological eating behavior (Schulte et al., 2015; Schulte et al., 2017). Furthermore, ultra-processed foods are theorized to disrupt gut-brain signaling and may influence food reinforcement and overall intake via mechanisms distinct from the palatability or energy density of the food (Small and DiFeliceantonio, 2019).

As compelling as such theories may be, it is important to emphasize that no causal relationship between ultra-processed food consumption and human obesity has yet been established. In fact, there has never been a randomized controlled trial demonstrating any beneficial effects of reducing ultra-processed foods or deleterious effects of increasing ultra-processed foods in the diet. Therefore, to address the causal role of ultra-processed foods on energy intake and body weight change, we conducted a randomized controlled trial examining the effects of ultra-processed versus unprocessed diets on ad libitum energy intake.

We admitted 10 male and 10 female weight-stable adults aged (mean±SE) 31.2±1.6 y with BMI=27±1.5 kg/m2 (see Table S1 in the Supplementary Information for more detailed demographics and anthropometrics) as inpatients to the Metabolic Clinical Research Unit (MCRU) at the NIH Clinical Center where they resided for a continuous 28-day period. Subjects were randomly assigned to either the ultra-processed or unprocessed diet for 2 weeks followed immediately by the alternate diet for the final 2 weeks (Figure 1).

Twenty adults were confined to the metabolic ward at the NIH Clinical Center. Every week, subjects spent one day residing in a respiratory chamber to measure energy expenditure, respiratory quotient, and sleeping energy expenditure. Average energy expenditure during each diet period was measured by the doubly labeled water (DLW) method. Body composition was measured by dual-energy X-ray absorptiometry (DXA) and liver fat was measured by magnetic resonance imaging/spectroscopy (MRI/MRS).

During each diet phase, the subjects were presented with three daily meals and were instructed to consume as much or as little as desired. Up to 60 minutes was allotted to consume each meal. Menus rotated on a 7-day schedule and the meals were designed to be well-matched across diets for total calories, energy density, macronutrients, fiber, sugars, and sodium, but widely differing in the percentage of calories derived from ultra-processed versus unprocessed foods (Table 1) as defined according to the NOVA classification scheme (Monteiro et al., 2018). While we attempted to match several nutritional parameters between the diets, the ultra-processed versus unprocessed meals differed substantially in the proportion of added to total sugar (~54% vs 1%, respectively), insoluble to total fiber (~16% versus 77%, respectively), saturated to total fat (~34% vs 19%), and the ratio of omega-6 to omega-3 fatty acids (~11:1 vs. 5:1).

Diet composition of the average 7-day rotating menu presented to the subjects during the Ultra-processed and Unprocessed diet periods.

The weekly cost for ingredients to prepare 2000 kcal/d of ultra-processed meals was estimated to be $106 versus $151 for the unprocessed meals as calculated using the cost of ingredients obtained from a local branch of a large supermarket chain. Snacks appropriate to the prevailing diet and bottled water were available throughout each day. The meals plus snacks were provided at an amount equivalent to twice each subject’s estimated energy requirements for weight maintenance as calculated by 1.6 × resting energy expenditure measured at screening. Details of the diet menus are provided as Supplemental Information.

Figures 2A and ​and2B2B show that metabolizable energy intake was 508±106 kcal/d greater during the ultra-processed diet (p=0.0001). Neither the order of the diet assignment (p=0.64) nor sex (p=0.28) had significant effects on the energy intake differences between the diets. Baseline BMI was not significantly correlated with the energy intake differences between the diets (r=0.11; p=0.66).

A) Energy intake was consistently higher during the ultra-processed diet. B) Average energy intake was increased during the ultra-processed diet because of increased intake of carbohydrate and fat, but not protein. C) Energy consumed at breakfast and lunch was significantly greater during the ultra-processed diet, but energy consumed at dinner and snacks was not significantly different between the diets. D) Both diets were rated similarly on visual analogue scales (VAS) with respect to pleasantness and familiarity. E) Appetitive measures were not significantly different between the diets. F) Meal eating rate was significantly greater during the ultra-processed diet.

During the unprocessed diet, energy intake did not significantly change over time (−7.7±6.4 kcal/d2; p=0.23), whereas there was a significant linear decrease in energy intake during the ultra-processed diet (−25.5±6.4 kcal/d2; pBody weight and composition changes.

A) The ultra-processed diet led to increased body weight over time whereas the unprocessed diet led to progressive weight loss. B) Differences in body weight change between the ultra-processed and unprocessed diets were highly correlated with the corresponding energy intake differences. C) Body fat mass increased over time with the ultra-processed diet and decreased with the unprocessed diet. D) Body weight, body fat, and fat-free mass changes between the beginning and end of each diet period..

Body fat mass increased by 0.4±0.1 kg (p=0.0015) during the ultra-processed diet and decreased by 0.3±0.1 kg during the unprocessed diet (p=0.05) (Figure 3C) whereas fat-free mass tended to increase during the ultra-processed diet (0.5±0.3 kg; p=0.09) and decrease during the unprocessed diet (0.6±0.3 kg; p=0.08) (Figure 3D). While the dual energy X-ray absorptiometry (DXA) methodology used to measure body composition in our study tends to underestimate body fat changes (Pourhassan et al., 2013), the relatively large fat-free mass changes may be due to extracellular fluid shifts associated with differences in sodium intake between the diets. Indeed, individual differences in sodium intake between the diets were significantly correlated with changes in fat-free mass (r=0.63; p=0.004) and body weight (r=0.64; p=0.002). Such fluid shifts may also affect the accuracy and precision of the measured body fat changes (Lohman et al., 2000; Muller et al., 2012).

Thirteen subjects completed measurements of liver fat content by magnetic resonance spectroscopy at baseline and the end of each diet period (Ouwerkerk et al., 2012). Baseline liver fat was 1.2±0.1% and liver fat was not significantly changed after the unprocessed diet (0.95±0.1%; p=0.24) or the ultra-processed diet (1.1±0.2%; p=0.74).

Subjects spent one day each week residing in respiratory chambers to measure the components of 24hr energy expenditure. On the chamber days subjects were presented with identical meals within each diet period and those meals were not offered on non-chamber days. Table 2 shows that there was no significant difference in energy intake between the diets on the chamber days, but the food quotient differences indicated that subjects consumed relatively more carbohydrate versus fat during the chamber days on the ultra-processed diet. While subjects tended to have greater 24hr energy expenditure during the ultra-processed diet (51±27 kcal/d; p=0.06), there were no significant differences in sleeping energy expenditure, sedentary energy expenditure, or physical activity. These results contrast with a previous study suggesting that energy expenditure was ~60 kcal lower for 6 hours following consumption of processed versus unprocessed sandwiches (Barr and Wright, 2010).

Energy expenditure and food intake during the respiratory chamber and doubly labeled water periods.

Fasting blood measurements at baseline and at the end of the ultra-processed and unprocessed diet periods.

A) Glucose concentrations following a 75g oral glucose tolerance test (OGTT) was not significantly different between the diets. B) Insulin concentrations following the OGTT were not significantly different between the diets. C) Continuous glucose monitoring throughout the study did not detect significant differences in average glucose concentrations or glycemic variability as measured by the coefficient of variation (CV) of glucose.

It is possible that differences in glucose tolerance and insulin sensitivity would have emerged after longer periods on each diet. However, shorter durations of overfeeding have previously been demonstrated to result in rapid impairments in glucose tolerance and insulin sensitivity (Lagerpusch et al., 2012; Walhin et al., 2013), albeit with greater differences in energy intake than the present study.

Another possible explanation is that exercise can prevent changes in insulin sensitivity and glucose tolerance during overfeeding (Walhin et al., 2013). Our subjects performed daily cycle ergometry exercise in three 20-minute bouts at a constant intensity corresponding to 30–40% of each subjects’ estimated heart rate reserve. This relatively low intensity exercise was mandated to avoid the sedentary behavior and de-training that often occurs during inpatient metabolic ward studies. Indeed, the average physical activity level (defined by total energy expenditure by DLW divided by resting energy expenditure) during the inpatient stay was 1.59±0.06 which is representative of free-living adults (SACN, 2011). It is intriguing to speculate that perhaps even this modest dose of exercise prevented any differences in glucose tolerance or insulin sensitivity between the ultra-processed and unprocessed diets.

Ultra-processed foods are less expensive and more convenient than preparing meals using unprocessed whole foods and culinary ingredients. Because the meals were prepared and presented at no cost to our subjects, and they could not choose their meals or their mode of presentation, our study did not address how consumer choices between ultra-processed versus unprocessed meals may be influenced by cost and convenience.

Our study was not designed to identify the cause of the observed differences in energy intake. Many of the potential negative effects of ultra-processed foods have been hypothesized to relate to their elevated sugar, fat, and sodium content while being low in protein, and fiber (Poti et al., 2017). However, we attempted to match these nutritional variables in the presented meals to investigate whether other aspects of ultra-processed diets contribute to excess energy intake. Had the experimental diets used in our study allowed for greater differences in sugar, fat, sodium content more typical of differences between ultra-processed versus unprocessed diets, we may have observed larger differences in energy intake.

Our study did not include a weight-maintenance run-in period or a washout period between test diets. These design choices were made to lessen the burden to the subjects and reduce the likelihood of dropouts, which was successful because all 20 subjects who successfully screened for the study also completed. To partially address the lack of run-in or washout periods, we compared ad libitum energy intake during the final week of each test diet period and the substantial diet differences persisted. The lack of a run-in period complicates the interpretation of the baseline blood measures in comparison to those obtained at the end of each test diet and all such diet comparisons were potentially confounded by the substantial differences in energy intake and corresponding weight changes.

Finally, the inpatient environment of the metabolic ward makes it difficult to generalize our results to free-living conditions. However, current dietary assessment methods are insufficient to accurately or precisely measure energy intake outside the laboratory (Schoeller, 1990; Schoeller et al., 2013) and adherence to study diets cannot be guaranteed in free-living subjects. While the 28-day duration of our study was relatively modest, most laboratory-based studies of food intake are typically much shorter in duration, often occurring within a single day of testing with one or two meals (Gibbons et al., 2014).

In conclusion, our data suggest that eliminating ultra-processed foods from the diet decreases energy intake and results in weight loss whereas a diet with a large proportion of ultra-processed food increases energy intake and leads to weight gain. Whether reformulation of ultra-processed foods could eliminate their deleterious effects while retaining their palatability and convenience is unclear. Until such reformulated products are widespread, limiting consumption of ultra-processed foods may be an effective strategy for obesity prevention and treatment. Such a recommendation could potentially be embraced across a wide variety of healthy dietary approaches including low-carb, low-fat, plant-based, or animal-based diets. However, policies that discourage consumption of ultra-processed foods should be sensitive to the time, skill, expense, and effort required to prepare meals from minimally processed foods – resources that are often in short supply for those who are not members of the upper socioeconomic classes.

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Kevin Hall (vog.hin@hnivek).

The study protocol was approved by the Institutional Review Board of the National Institute of Diabetes & Digestive & Kidney Diseases (ClinicalTrials.gov Identifier {"type":"clinical-trial","attrs":{"text":"NCT03407053","term_id":"NCT03407053"}}NCT03407053). Eligible subjects were between 18–50 years old with a body mass index (BMI) > 18.5 kg/m2 and were weight-stable (< ± 5% over the past 6 months). Volunteers were excluded if they had anemia, diabetes, cancer, thyroid disease, eating disorders or other psychiatric conditions such as clinical depression or bipolar disorder. Volunteers with strict dietary concerns, including food allergies or adherence to particular diets (e.g., vegetarian, vegan, kosher, etc.) were also excluded.

Subjects were told that the purpose of the study was to learn about how a processed versus unprocessed diet affects the amount of food they eat, glucose tolerance, hormone levels, markers of inflammation, body weight and composition, energy expenditure, and liver fat. The subjects were told that this was not a weight loss study. They wore loose fitting clothing throughout the study and were blinded to daily weight and continuous glucose measurements.

The diets were designed and analyzed using ProNutra software (version 3.4, Viocare, Inc., Princeton, NJ) with nutrient values derived from the USDA National Nutrient Database for Standard Reference, Release 26 and the USDA Food and Nutrient Database for Dietary Studies, 4.0. The ultra-processed and unprocessed meals were provided on 7-day rotating menus (see the Supplemental Information for detailed menu information). Foods and beverages were categorized according to the NOVA system (Monteiro et al., 2018).

Bottled water and snacks representative of the prevailing diet were provided ad libitum throughout the day in snack boxes located in the subjects’ inpatient rooms. Meals were presented to the subjects plated approximately as shown in the photographs included in the Supplemental Information with instructions to eat as much or as little as desired. Subjects were given up to 60 minutes to eat and when they finished each meal a nurse removed the meal and documented the meal duration. Remaining food and beverages were identified and weighed by nutrition staff to calculate the amount of each food consumed and the nutrient and metabolizable energy intake were calculated using the nutrition software described above. Meal eating rate was calculated by dividing the measured food intake by the meal duration.

During each diet period, subjects were asked to complete appetitive surveys over the course of three separate days implemented using REDCap (Research Electronic Data Capture) electronic data capture tools (Harris et al., 2009). The surveys comprised visual analog scales (VAS) in response to four questions: 1) “How hungry do you feel right now?” 2) “How full do you feel right now?” 3) “How much do you want to eat right now?” and 4) “How much do you think you can eat right now?”. Subjects answered the questions using 100-point VAS line scale anchored at 0 and 100 by descriptors such as “not at all” and “extremely”. The questions were answered immediately prior to each meal and at least every 30 to 60 minutes over the 2–3 hours following the consumption of each meal. We calculated the mean values of the responses adjusted for the energy consumed using multiple linear regression.

On the last two days of the first diet period and the first two days of the second diet period, subjects were asked to complete another survey to assess the palatability and familiarity of the meals provided. The questions were embedded amongst distracter “mood” ratings (e.g., alert, happy, and clear-headed). Survey items were completed after the first bite of the meal.

Daily body weight measurements were performed at 6am each morning after the first void (Welch Allyn Scale-Tronix 5702; Skaneateles Falls, NY, USA). Subjects wore hospital-issued top and bottom pajamas which were pre-weighed and deducted from scale weight. To minimize the influence of fluctuations in body fluids, weight changes during each 14-day diet period were calculated by linear regression. Body composition measurements were performed at baseline and weekly using dual-energy X-ray absorptiometry (General Electric Lunar iDXA; Milwaukee, WI, USA). Changes in body energy stores were calculated using the measured changes in body fat and fat-free mass along with the corresponding energy densities of 9300 kcal/kg and 1100 kcal/kg, respectively. Liver fat measurements were performed using T1 and T2 corrected proton magnetic resonance spectroscopy with a breath-holding technique in a 3T scanner (MAGNETOM Verio; Siemens, Tarrytown, NY) (Ouwerkerk et al., 2012).

Overall physical activity was quantified by calculating average daily metabolic equivalents (MET) using small, portable, pager-type accelerometers (Actigraph, Pensacola, FL) sampled at 80 Hz and worn on the hip (Freedson et al., 1998).

All chamber measurement periods were >23 hours and we extrapolated the data to represent 24hr periods by assuming that the mean of the measured periods was representative of the 24hr period. Energy expenditure was calculated as follows:

where VO2 and VCO2 were the volumes of oxygen consumed and carbon dioxide produced, respectively, and N was the 24hr urinary nitrogen excretion measured by chemiluminescence (Antek MultiTek Analyzer, PAC, Houston, TX).

Sleeping energy expenditure was determined by the lowest energy expenditure over a continuous 180 minute period between the hours of 00:00–06:00 (Schoffelen and Westerterp, 2008). Sedentary energy expenditure and physical activity expenditure were defined as previously described (Hall et al., 2016).

Subjects drank from a stock solution of 2H2O and H218O water where 1 g of 2H2O (99.99% enrichment) was mixed with 19 g of H218O (10% enrichment). An aliquot of the stock solution was saved for dilution to be analyzed along with each set of urine samples. The water was weighed to the nearest 0.1 g into the dosing container. The prescribed dose was 1.0 g per kg body weight and the actual dose amounts were entered in a dose log. Spot urine samples were collected daily. Isotopic enrichments of urine samples were measured by isotope ratio mass spectrometry. The average CO2 production rate (rCO2) were estimated from the rate constants describing the exponential disappearance of the labeled 18O and D water isotopes (kO and kD) in repeated spot urine samples collected over several days and were corrected for previous isotope doses (Bhutani et al., 2015). We used the parameters of Racette et al. (Racette et al., 1994) with the weighted dilution space calculation, Rdil, proposed by Speakman (Speakman, 1997):

where (ND / NO)ave is the mean of the ratio of the body water pool sizes ND / NO from the n subjects. In cases where the individual values for the total body water, N, differed by > 5% from that calculated as 73% of the fat-free mass determined by DXA within a few days of the dose, N was adjusted to agree with the DXA data.

The average total energy expenditure (EEDLW) from the DLW measurement of rCO2 was calculated as:

where RQ was calculated by adjusting the respiratory chamber RQ measurements for the overall degree of energy imbalance of each subject as determined by body composition changes during the DLW period as previously described (Hall et al., 2019).

Subjects wore the Dexcom G4 Platinum (Dexcom Inc, San Diego, CA, USA) continuous glucose monitor (CGM) daily during the inpatient stay. The device consisted of a small sensor, a transmitter, and a hand-held receiver. The sensor was inserted subcutaneously in the lower abdomen to measure interstitial glucose concentrations every 5 minutes which were transmitted to the receiver. Finger stick calibrations were required at insertion as well as each morning and night. The sensor was changed every 7 days. Subjects were blinded to their glucose readings. The CGM was removed during MRI/MRS procedures and DXA scans. All the data was downloaded at the end of the inpatient stay.

This study was powered to detect a difference in mean ad libitum energy intake over each 14-day test diet period (the primary endpoint) of 125–150 kcal/d in 20 subjects with probability (power) of 0.8 with a Type I error probability of 0.05. This sample size calculation was informed by previous studies measuring day to day variability of ad libitum energy intake having a standard deviation of about 500–600 kcal/d (Bray et al., 2008; Edholm et al., 1970; Tarasuk and Beaton, 1992). Using the conservative assumption that within-subject energy intake correlations were zero, over the 14-day diet period each subject was expected to have a mean energy intake with a standard error of about 130–160 kcal/d and the mean energy intake difference between the study diets was therefore estimated to have a standard error of about 190–230 kcal/d.

Statistical analyses were performed using SAS (version 9.4; SAS Institute Inc, Cary, NC, USA). The baseline data are presented as mean ± SE. Data were analyzed by analysis of variance (PROC GLM, SAS). The data tables and figures present least squares mean ± SE and two-sided t-tests were used to compare the diet groups. Significance was declared at p < 0.05.

Increased availability, marketing, and consumption of ultra-processed foods has been associated with rising obesity prevalence, but scientists have not yet demonstrated that ultra-processed food causes obesity or adverse health outcomes. Researchers at the NIH investigated whether people ate more calories when exposed to a diet composed of ultra-processed foods compared with a diet composed of unprocessed foods. Despite the ultra-processed and unprocessed diets being matched for daily presented calories, sugar, fat, fiber, and macronutrients, people consumed more calories when exposed to the ultra-processed diet as compared to the unprocessed diet. Furthermore, people gained weight on the ultra-processed diet and lost weight on the unprocessed diet. Limiting consumption of ultra-processed food may be an effective strategy for obesity prevention and treatment.

This work was supported by the Intramural Research Program of the National Institutes of Health, National Institute of Diabetes & Digestive & Kidney Diseases. We thank the nursing and nutrition staff at the NIH MCRU for their invaluable assistance with this study. We also thank Ms. Shavonne Pocock for taking the photos of the study diets. We are most thankful to the study subjects who volunteered to participate in this demanding protocol.

Declaration of Interests

CG Forde has received reimbursement for speaking at conferences sponsored by companies selling nutritional products, serves on the scientific advisory council for Kerry Taste and Nutrition, and is part of an academic consortium that has received research funding from Abbott Nutrition, Nestec, and Danone. The other authors have no conflicts of interest.

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