Changes in ghrelin concentrations one year after resective and non-resective gastric bypass: associations with weight loss and energy and macronutrient intakes

ObjectiveGhrelin is a potent stimulator of appetite and synthesized in the stomach. Its role in weight loss after gastric bypass (GBP) is still controversial. The aim of this study was to evaluate the relation between weight loss and food intake and between weight loss and changes in serum ghrelin concentrations 1 y after GBP with resection of the bypassed stomach (R-GBP) and without resection (NR-GBP).MethodsOf 50 women (37.6 ± 10.2 y old, body mass index 43.8 ± 4.8 kg/m²) with GBP, 26 had R-GBP and 24 had NR-GBP. Body weight, body composition (dual energy x-ray absorptiometry), food intake, and serum ghrelin at baseline and 12 mo after GBP were measured.ResultsThe percentage of excess weight loss was 68.9 ± 12.8% at 12 mo after GBP. At 12 mo, the decrease of serum ghrelin was greater in the R-GBP group (?25.3 ± 22.5%) compared with the NR-GBP group (+11.2 ± 50.9%, P < 0.005). After adjustment by the baseline excess of body weight, there was a greater percentage of excess weight loss in the R-GBP group only at 6 mo (61.8% versus 54.9%, P = 0.011). After controlling for the baseline intake, a significant lower carbohydrate intake was observed in the R-GBP group 6 mo after surgery (P < 0.05).ConclusionA greater decrease in ghrelin levels was observed only in patients who underwent R-GBP at 12 mo after surgery. This difference was not associated with differences in dietary intakes or weight loss at the same time point. Therefore, the small gastric pouch is probably more important than decreased ghrelin levels in producing long-term weight loss after R-GBP.     

Reliability and Predictive Validity of Energy Intake Measures from the 24-Hour Dietary Recalls of Homebound Older Adults

Twenty-four-hour dietary recalls are used frequently to study homebound older adults' eating behaviors. However, the reliability and predictive validity of this method have not been established in this population. The purpose of this study was to examine whether homebound older adults provide reliable and valid measures of total energy intake in 24-hour dietary recalls. Two hundred thirty homebound older adults were interviewed in their homes using a questionnaire to assess eating behaviors and factors that could affect those behaviors. Participants completed three 24-hour dietary recalls at baseline and again at 6-month follow-up. Two subsamples were identified for analyses. For participants who were not hospitalized during the 6-month interval and had their weight measured at both assessments (n=52), sufficient test-retest reliability of energy intake was observed (r=0.59), but energy intake deficiencies relative to estimated energy requirements did not predict actual weight loss (r=0.08). When this sample was supplemented with 91 participants who experienced any adverse event (weight loss of 2.5% or more, hospitalization, institutionalization, or mortality) in the 6-month period (n=143), adverse events were more likely to occur for those with insufficient energy intake (odds ratio 3.49, P=0.009), and in white participants compared to African-American participants (odds ratio 3.13, P=0.016). Adequate test-retest reliability of the 24-hour dietary recall was demonstrated, but additional research with larger samples and longer follow-up intervals is needed to better evaluate the predictive validity of energy intake measures for this population.     

Slower Eating Speed Lowers Energy Intake in Normal-Weight but not Overweight/Obese Subjects

BackgroundThe effect of eating speed on energy intake by weight status is unclear.ObjectiveTo examine whether the effect of eating speed on energy intake is the same in normal-weight and overweight/obese subjects.DesignThe effect of slow and fast eating speed on meal energy intake was assessed in a randomized crossover design.Participants/settingThirty-five normal-weight (aged 33.3±12.5 years; 14 women and 21 men) subjects and 35 overweight/obese (44.1±13.0 years; 22 women and 13 men) subjects were studied on 2 days during lunch in a metabolic kitchen.InterventionThe subjects consumed the same meal, ad libitum, but at different speeds during the two eating conditions. The weight and energy content of the food consumed was assessed. Perceived hunger and fullness were assessed at specific times using visual analog scales.Statistical analysesEffect of eating speed on ad libitum energy intake, eating rate (energy intake/meal duration), energy density (energy intake per gram of food and water consumed), and satiety were assessed by mixed-model repeated measures analysis.ResultsMeal energy intake was significantly lower in the normal-weight (804.5±438.9 vs 892.6±330.2 kcal; P=0.04) but not the overweight/obese (667.3±304.1 vs 724.8±355.5 kcal; P=0.18) subjects during the slow vs the fast eating condition. Both groups had lower meal energy density (P=0.005 and P=0.001, respectively) and eating rate (P<0.0001 in both groups) during the slow vs the fast eating condition. Both groups reported less hunger (P=0.01 and P=0.03, respectively), and the normal-weight subjects reported more fullness (P=0.02) at 60 minutes after the meal began during the slow compared with the fast eating condition. There was no eating speed by weight status interaction for any of the variables.ConclusionsEating slowly significantly lowered meal energy intake in the normal-weight but not in the overweight/obese group. It lowered eating rate and energy density in both groups. Eating slowly led to lower hunger ratings in both groups and increased fullness ratings in the normal-weight group at 60 minutes from when the meal began.     

Serum prealbumin: An independent marker of short-term energy intake in the presence of multiple-organ disease involvement

ObjectiveSerum prealbumin has a prognostic value in several diseases, but its serum levels can be influenced by different factors. However, a multivariable analysis to test the independent effect of each has not yet, to our knowledge, been performed. The aim of this cross-sectional study was to investigate the association between prealbumin and several factors possibly affecting its serum levels to test the potential of using prealbumin as an indicator of nutritional status and short-term energy intake in patients newly diagnosed with immunoglobulin light-chain amyloidosis.MethodsMultivariable general linear regression models of non-collinear variables were fitted to assess the association of demographic (sex, age), nutritional (short-term energy intake, unintentional weight loss, body mass index), and clinical (cardiac and liver involvement, kidney function, C-reactive protein) parameters with serum prealbumin levels in 187 patients newly diagnosed with immunoglobulin light-chain amyloidosis.ResultsSerum prealbumin levels were associated with C-reactive protein and short-term energy intake (P < 0.001 for both). A significant association was also detected with age (P = 0.023), serum creatinine (P = 0.017), liver involvement (P = 0.002), and peripheral edema (P = 0.032). In a prespecified subgroup analysis (n = 140) in patients with normal C-reactive protein level (<0.5 mg/dL), all other associations were confirmed. A significant relation was also observed with sex (P = 0.022) and body mass index (P = 0.041).ConclusionsSerum prealbumin is associated with short-term energy intake independently of the presence of multiple-organ involvement and inflammation. Its serum levels should be always interpreted in light of its influencing factors, among which inflammation and liver and kidney functions appear predominant.     

Changes in Food and Nutrient Intake and Diet Quality on a Low-Fat Vegan Diet Are Associated with Changes in Body Weight,Body Composition,and Insulin Sensitivity in Overweight Adults: A Randomized Clinical Trial

BackgroundConsuming different food groups and nutrients can have differential effects on body weight, body composition, and insulin sensitivity.ObjectiveThe aim was to identify how food group, nutrient intake, and diet quality change relative to usual-diet controls after 16 weeks on a low-fat vegan diet and what associations those changes have with changes in body weight, body composition, and measures of metabolic health.DesignSecondary analysis of a randomized clinical trial conducted between October 2016 and December 2018 in four replications.Participants/settingParticipants included in this analysis were 219 healthy, community-based adults in the Washington, DC, area, with a body mass index (BMI) between 28 and 40, who were randomly assigned to either follow a low-fat vegan diet or make no diet changes.InterventionA low-fat, vegan diet deriving approximately 10% of energy from fat, with weekly classes including dietary instruction, group discussion, and education on the health effects of plant-based nutrition. Control group participants continued their usual diets.Main outcome measuresChanges in food group intake, macronutrient and micronutrient intake, and dietary quality as measured by Alternate Healthy Eating Index-2010 (AHEI-2010), analyzed from 3-day diet records, and associations with changes in body weight, body composition, and insulin sensitivity were assessed.Statistical analyses performedA repeated-measure analysis of variance model that included the factors group, subject, and time was used to test the between-group differences throughout the 16-week study. Interaction between group and time was calculated for each variable. Within each diet group, paired comparison t tests were calculated to identify significant changes from baseline to 16 weeks. Spearman correlations were calculated for the relationship between changes in food group intake, nutrient intake, AHEI-2010 score, and changes in body weight, body composition, and insulin sensitivity. The relative contribution of food groups and nutrients to weight loss was evaluated using linear regression.ResultsFruit, vegetable, legume, meat alternative, and whole grain intake significantly increased in the vegan group. Intake of meat, fish, and poultry; dairy products; eggs; nuts and seeds; and added fats decreased. Decreased weight was most associated with increased intake of legumes (r = ?0.38; P < 0.0001) and decreased intake of total meat, fish, and poultry (r = +0.43; P < 0.0001). Those consuming a low-fat vegan diet also increased their intake of carbohydrates, fiber, and several micronutrients and decreased fat intake. Reduced fat intake was associated with reduced body weight (r = +0.15; P = 0.02) and, after adjustment for changes in BMI and energy intake, with reduced fat mass (r = +0.14; P = 0.04). The intervention group’s AHEI-2010 increased by 6.0 points on average, in contrast to no significant change in the control group (treatment effect, +7.2 [95% CI +3.7 to +10.7]; P < 0.001). Increase in AHEI-2010 correlated with reduction in body weight (r = 0.14; P = 0.04), fat mass (r = ?0.14; P = 0.03), and insulin resistance as measured by the Homeostasis Model Assessment (HOMA-IR; r = ?0.17; P = 0.02), after adjustment for changes in energy intake.ConclusionsWhen compared with participants’ usual diets, intake of plant foods increased, and consumption of animal foods, nuts and seeds, and added fats decreased on a low-fat vegan diet. Increased legume intake was the best single food group predictor of weight loss. Diet quality as measured by AHEI-2010 improved on the low-fat vegan diet, which was associated with improvements in weight and metabolic outcomes. These data suggest that increasing low-fat plant foods and minimizing high-fat and animal foods is associated with decreased body weight and fat loss, and that a low-fat vegan diet can improve measures of diet quality and metabolic health.     

Fiber Intake and Plasminogen Activator Inhibitor-1 in Type 2 Diabetes: Look AHEAD (Action for Health in Diabetes) Trial Findings at Baseline and Year 1

Plasminogen activator inhibitor 1 (PAI-1) is elevated in obese individuals with type 2 diabetes and may contribute, independently of traditional factors, to increased cardiovascular disease risk. Fiber intake may decrease PAI-1 levels. We examined the associations of fiber intake and its changes with PAI-1 before and during an intensive lifestyle intervention (ILI) for weight loss in 1,701 Look AHEAD (Action for Health in Diabetes) participants with dietary, fitness, and PAI-1 data at baseline and 1 year. Look AHEAD was a randomized cardiovascular disease trial in 5,145 overweight/obese patients with type 2 diabetes, comparing ILI (goal of ≥7% reduction in baseline weight) with a control arm of diabetes support and education. ILI participants were encouraged to consume vegetables, fruits, and grain products low in sugar and fat. At baseline, median fiber intake was 17.9 g/day. Each 8.3 g/day higher fiber intake was associated with a 9.2% lower PAI-1 level (P=0.008); this association persisted after weight and fitness adjustments (P=0.03). Higher baseline intake of fruit (P=0.019) and high-fiber grain and cereal (P=0.029) were related to lower PAI-1 levels. Although successful in improving weight and physical fitness at 1 year, the ILI in Look AHEAD resulted in small increases in fiber intake (4.1 g/day, compared with –2.35 g/day with diabetes support and education) that were not related to PAI-1 change (P=0.34). Only 31.3% of ILI participants (39.8% of women, 19.1% of men) met daily fiber intake recommendations. Increasing fiber intake in overweight/obese individuals with diabetes interested in weight loss is challenging. Future studies evaluating changes in fiber consumption during weight loss interventions are warranted.     

Dietary Self-Monitoring,But Not Dietary Quality,Improves With Use of Smartphone App Technology in an 8-Week Weight Loss Trial

ObjectiveDietary self-monitoring is linked to improved weight loss success. Mobile technologies, such as smartphone applications (apps), might allow for improved dietary tracking adherence. The authors assessed the use of a popular smartphone app for dietary self-monitoring and weight loss by comparing it with traditional diet counseling and entry methods.MethodsDiet tracking and weight loss were compared across participants during an 8-week weight loss trial. Participants tracked intake using 1 of 3 methods: the mobile app “Lose It!”, the memo feature on a smartphone, or a traditional paper-and-pencil method.ResultsApp users (n = 19) recorded dietary data more consistently compared with the paper-and-pencil group (n = 15; P = .042) but not the memo group (n = 13). All groups lost weight over the course of the study (P = .001), and no difference in weight loss was noted between groups.Conclusions and ImplicationsSmartphone apps could represent a novel and feasible dietary self-monitoring method for individuals.     

Nutrition-Focused Wellness Coaching Promotes a Reduction in Body Weight in Overweight US Veterans

Diet plays a critical role in the pathogenesis of major chronic diseases common in populations of US veterans. The role of nutrition-focused wellness coaching in improving dietary behavior and/or reducing weight in overweight and obese US veterans is not known. At the San Francisco Veterans Affairs Medical Center, US veterans aged 25 to 80 years were randomized to receive nutrition coaching on eating behaviors at baseline only (control group, n=22) or an additional eight times over the course of 6 months (intervention group, n=28) in 2010-2011. Multiple coaching contacts decreased intake of energy, fat, and carbohydrate by 31% (P≤0.001) as evaluated by the 2005 Block food frequency questionnaire, which is composed of 111 food items. A weight loss of 5% from baseline (92.8 to 88.2 kg; P<0.01) was observed in the intervention group with mean body mass index decreasing from 30.4 to 28.9 (P<0.05). The control group showed a decrease in fat intake by 20% (P=0.01), but no statistically significant changes in intake of other nutrients or body weight (88.7 to 87.4 kg). Those in the intervention group reported diets at follow-up that were lower in cholesterol, saturated fat, sodium, sugar (P≤0.01), calcium (P< 0.05), and vitamin D (P<0.01), although when adjusted for energy (ie, nutrient density) calcium intake increased and vitamin D remained unchanged. Veterans' readiness to change eating behavior for weight loss improved with nutrition coaching. This study demonstrates that intermittent nutrition coaching can be an effective strategy to promote reductions in energy intake, body weight, and body mass index in overweight US veterans. Further research is needed to determine whether nutrition coaching improves other clinical outcomes and sustains weight loss.     

Influence of a nutritional intervention on dietary intake and quality of life in cancer patients: A randomized controlled trial

ObjectiveWeight loss is common in patients with malignant tumors and it can adversely affect quality of life and survival. The aim of the present study was to investigate the effects of a nutritional intervention in cancer patients in an outpatient setting.MethodsCancer outpatients (N = 58) who were classified as undernourished or at high risk for undernutrition by the Nutritional Risk Screening 2002 tool were randomized into two groups. One group (n = 30) received standardized individual nutritional therapy, including counseling by a dietitian, food fortification, and oral nutritional supplements if required. The second group (n = 28) received standard care. The nutritional intervention lasted 3 mo. Dietary intake (3-d dietary record), nutritional status (body weight), physical functioning (performance status, hand-grip strength) and quality of life (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire version 3.0) were assessed at baseline and after 6 wk and 3 mo. An additional follow-up assessment was carried out 3 mo post-intervention.ResultsNutritional intervention led to a significantly higher average energy and protein intake in the nutritional therapy group (+379 kcal; 95% confidence interval [CI], 117–642; P = 0.007, respectively; +10.4 g; 95% CI, 2.3–18.5; P = 0.016). However, the increased dietary intake was not associated with improvements in nutritional status, physical functioning, or quality of life.ConclusionsIndividual nutritional counseling significantly and positively influenced energy and protein intake, but did not improve nutritional or physical outcome or quality of life. These results indicate that nutritional therapy alone is of limited efficacy in cancer patients whose nutritional status has already deteriorated.     

Short- and long-term eating habit modification predicts weight change in overweight, postmenopausal women: results from the WOMAN study

BackgroundStandard behavioral obesity treatment produces poor long-term results. Focusing on healthy eating behaviors rather than energy intake may be an alternative strategy. In addition, important behaviors might differ for short- vs long-term weight control.ObjectiveOur aim was to describe and compare associations between changes in eating behaviors and weight after 6 and 48 months.DesignWe performed secondary analysis of data collected during a randomized weight-loss intervention trial with 48-month follow-up.ParticipantsWe studied 481 overweight and obese postmenopausal women enrolled in the Women on the Move through Activity and Nutrition (WOMAN) Study.Main outcome measuresWe measured changes in weight from baseline to 6 and 48 months.Statistical analyses performedLinear regression models were used to examine the associations between 6- and 48-month changes in eating habits assessed by the Conner Diet Habit Survey and changes in weight. Analyses were conducted in the combined study population and stratified by randomization group.ResultsAt 6 months in the combined population, weight loss was independently associated with decreased desserts (P<0.001), restaurant eating (P=0.042), sugar-sweetened beverages (P=0.009), and fried foods (P<0.001), and increased fish consumption (P=0.003). Results were similar in intervention participants; only reduced desserts and fried foods associated with weight loss in controls. At 48 months in the combined population, weight loss was again associated with decreased desserts (P=0.003) and sugar-sweetened beverages (P=0.011), but also decreased meats/cheeses (P=0.024) and increased fruits/vegetables (P<0.001). Decreased meats/cheeses predicted weight loss in intervention participants; desserts, sugar-sweetened beverages, and fruits/vegetables were independently associated in controls.ConclusionsChanges in eating behaviors were associated with weight change, although important behaviors differed for short- and long-term weight change and by randomization group. Future studies should determine whether interventions targeting these behaviors could improve long-term obesity treatment outcomes.     

Sleep deprivation is associated with lower diet quality indices and higher rate of general and central obesity among young female students in Iran

ObjectivesShort sleep duration and low diet quality are associated with weight gain. However, little is known about the relationship between sleep duration and the quality of diets. Therefore, we aimed to compare the diet quality indices and anthropometric measures between short and longer sleepers.MethodsThis cross-sectional study consisted of 410 female youths who were chosen among students of Isfahan University of Medical Sciences based on stratified random sampling method. Dietary intake assessment was done using a semiquantitative validated food frequency questionnaire. Sleep duration was estimated using self-reported nocturnal sleep duration by each person. Anthropometric measures were done using standard protocols. Diet quality indices (including dietary energy density, dietary diversity scores, healthy eating index, nutrient adequacy ratio, and mean adequacy ratio) were calculated using the standard definition.ResultsSubjects who slept less than 6 h/d were more likely to be overweight and obese (P = 0.0001) and also abdominally obese (P = 0.03). They also consumed more dietary energy (2406 ± 825 versus 2092 ± 700 kcal/d; P = 0.01, respectively) and carbohydrates (58.1 ± 16.2% versus 51.6 ± 10.3%; P = 0.03) but a lower amount of fiber (12 ± 7 versus 18 ± 7 g/d; P = 0.04), fruits (2.4 ± 0.6 versus 3.1 ± 0.7 servings/d; P = 0.04), whole grains (0.9 ± 0.1 versus 1.3 ± 0.1 servings/d; P = 0.04), and beans (0.3 ± 0.1 versus 0.8 ± 0.1 servings/d; P = 0.04). All diet quality indices were significantly lower among short sleepers (P < 0.05), apart from dietary energy density, which did not differ significantly (P = 0.8).ConclusionOur findings confirm the association of short sleep duration and obesity in young female youths. It might be derived from lower diet quality among short sleepers more than longer sleepers.     

Food diversity and nutrient intake during pregnancy in relation to maternal weight gain

BackgroundInadequate gestational weight gain has become a major problem for Algerian women and this is due to inadequate maternal nutrient intake and lack of food diversity. Our objective was to assess the caloric and nutrient intake, the nutritional adequacy of the diet of women during pregnancy and to appreciate their effects on gestational weight gain.MethodsA prospective cohort study of 300 pregnant women aged 19 to 43 years was conducted, from December 2013 to July 2016. Maternal age, parity, educational level, household income and pre-pregnancy body mass index were collected. Gestational weight gain was calculated and classified according to the IOM recommendations. Participants were asked to report the frequency of consumption of foods and beverages in the last trimester of gestation. Also, the estimation of energy and nutrient intakes was made by a three-day dietary record. Statistics were performed using StatviewTM and SPSS software.ResultsIntakes of energy, vitamines and minerals of women with insufficient weight gain were less than that of women with normal and excessive gain (P < 0.0001). Milk and dairy products (P = 0.0009), fruits and vegetables (P = 0.03), meats, fish and eggs (P = 0.0049) were significantly less consumed by women with insufficient gain. The lowest food diversification was more observed in women with insufficient gestational weight gain.ConclusionMost of the pregnant women had a poorly diversified diet and especially women with insufficient weight gain. Also, the intakes of energy, vitamins and minerals of women with insufficient gain were the lowest.     

Eating Timing: Associations with Dietary Intake and Metabolic Health

BackgroundEmerging research indicates that eating timing may influence dietary intake and metabolic health. However, studies to date have not examined the association of multiple measures of eating timing with both dietary intake and metabolic health in adults with overweight and obesity.ObjectiveTo examine the association of multiple measures of eating timing with dietary intake (ie, dietary composition, diet quality, and eating frequency) and metabolic health (ie, body composition and cardiometabolic risk).DesignThis is a cross-sectional analysis of baseline data from a weight loss and maintenance intervention collected from May 2015 to January 2018.Participants/settingParticipants were women with overweight or obesity who were dependents of active duty and retired military personnel (N = 229; mean ± standard error, BMI = 34.7 ± 0.4 kg/m², age = 40.9 ± 0.7 years). The study was conducted at military installations in Massachusetts, Connecticut, New York, Colorado, and Kentucky.Main outcome measuresEating timing variables examined included daily eating interval (time between first and last eating occasion), time-restricted eating (≤11 hours daily eating interval), early energy eaters (eating ≥60% of energy during the first half of time awake), and bedtime eaters (eating within 2 hours of bedtime).Statistical analysisThe main analysis was limited to those reporting plausible energy intake (64% of total sample [n = 146]). Linear, quantile, or logistic regression models were used to determine the association of eating timing with measures of dietary intake and metabolic health.ResultsIn individuals reporting plausible energy intake, each additional 1 hour in daily eating interval was associated with 53 kcal higher energy intake, higher glycemic load, eating frequency, and waist circumference (P < 0.05 for all). Significant associations were observed for: time-restricted eating and a lower energy intake, glycemic load, and eating frequency; early energy eating and higher carbohydrate intake; bedtime eating and a higher energy intake, glycemic load, and eating frequency.ConclusionsThese findings lend support for the mechanistic targeting of eating timing in behavioral interventions aimed at improving dietary intake and body composition.     

The Impact of a Long-Term Reduction in Dietary Energy Density on Body Weight Within a Randomized Diet Trial

We examined the effect of dietary energy density change on body weight in participants of a randomized trial. Intervention participants markedly increased fruit and vegetable intake while reducing energy intake from fat. Participants were 2,718 breast cancer survivors, aged 26–74 yr, with baseline mean body mass index of 27.3 kg/m^{m 2} (SD = 6.3). We assessed dietary intake by sets of four 24-h dietary recalls and validated with plasma carotenoid concentrations. Weight and height were measured at baseline, 1 yr, and 4 yr. Dietary energy density was calculated using food but excluding beverages. Intervention participants significantly reduced dietary energy density compared to controls and maintained it over 4 yr—both in cross-sectional ( P m < 0.0001) and longitudinal (Group m × Time interaction, P m < 0.0001) analyses. Total energy intake or physical activity did not vary between groups. The intervention group had a small but significant weight loss at 1 yr (Group m × Time interaction, P m < 0.0001), but no between-group weight difference was observed at 4 yr. Our study showed that reducing dietary energy density did not result in a reduction in total energy intake and suggests that this strategy alone is not sufficient to promote long-term weight loss in a free-living population.     

Weight loss and resting energy expenditure in male patients with newly diagnosed esophageal cancer

ObjectivesCancer cachexia is associated with impaired nutritional status and systemic inflammation. The goal of this study was to evaluate the nutritional status and resting energy expenditure (REE) changes in patients with newly detected esophageal cancer, and the influence of weight loss on REE.MethodsFifty-six patients and 30 healthy controls were prospectively enrolled, and patients were further divided into weight-stable (WS) and weight-loss (WL) subgroups. Body composition, measured REE (mREE), and the ratio of mREE to predicted REE (pREE) by Harris-Benedict formula were assessed. Blood levels of hemoglobin, albumin, prealbumin, high-sensitivity C-reactive protein (hs-CRP), tumor necrosis factor-alpha (TNF-α), and interleukin (IL)-6 were measured in patients.ResultsCancer patients had lower body mass index (BMI) and percentage of fat mass, but higher mREE and percentage of mREE/pREE compared with healthy controls (P < 0.05). WS (n = 32) and WL patients (n = 24) had similar BMI and body composition indices, but the latter had obviously higher mREE, mREE per kilogram body weight (mREE/BW), percentage of mREE/pREE, hs-CRP and IL-6 levels, and lower albumin and prealbumin levels. Percentage of weight loss was positively correlated with REE/BW, hs-CRP, and IL-6 level (r = 0.238, P = 0.044; r = 0.446, P = 0.01; r = 0.196, P = 0.047, respectively).ConclusionImpaired nutrition status, elevated energy expenditure, and higher inflammation status tend to be apparent in weight-losing patients with newly diagnosed esophageal cancer, which suggested that early recognition of body weight change and routine nutritional risk screening followed by adequate nutrition intervention should be applied in these patients.     

Food Insecurity and Dietary Intake among College Students with Unlimited Meal Plans at a Large,Midwestern University

BackgroundFood insecurity, a state of not being able to consistently access nutritious food due to financial constraints, has been associated with poor dietary intake among college students. The extent to which campus food resources contribute to this association is unknown.ObjectivesThis study examined the association between food insecurity and dietary intake in a sample of undergraduate students with unlimited meal plans and dining hall access at a large, public Midwestern university.DesignThe study design is cross-sectional. The data used are baseline data from a broader sugar-sweetened beverage intervention study that were collected using a Qualtrics survey prior to the intervention.Participants/settingThe sample consisted of 1033 undergraduate students recruited from 3 dining halls. The data were collected in November 2018.Main outcome measuresFood security was assessed using the 6-item Short Form Food Security Survey Module. Dietary intake was assessed using the National Cancer Institute 26-item Dietary Screener Questionnaire and the Beverage Intake Questionnaire-15.Statistical analyses performedGeneralized linear regression models were used to examine differences in dietary intake by students’ food security status, adjusting for students’ sociodemographic characteristics.ResultsIn the sample, 14% of students were food-insecure. After adjusting for sociodemographic variables, food-insecure students reported 9% lower intake of fruits (P = 0.02), 9% lower intake of vegetables (P < 0.001), 10% higher intake of dairy (P = 0.002), 6% higher intake of total added sugars (P = 0.01), 10% higher intake of added sugars from sugar-sweetened beverages (P = 0.01), 4% higher intake of calcium (P = 0.01), and 4% lower intake of fiber (P = 0.01) compared with food-secure students. With respect to beverage intake, food-insecure students had 56% higher intake of total sugar-sweetened beverages (P = 0.002), which was driven by 185% higher intake of energy and sports drinks (P = 0.001), and 121% higher intake of sweetened teas (P = 0.001).ConclusionsDespite having identical food resources within campus dining halls, there were significant differences in the diets of college students by food security status.     

Dietary Intakes of Patients with Alcohol Use Disorder During a 4-Week Protocol on an Inpatient Treatment Unit Found to Meet Dietary Reference Intakes for Macronutrients,but Have Variability in Energy Balance and Adequacy of Micronutrient Intake

BackgroundDespite literature supporting the importance of diet during rehabilitation, minimal research quantifies dietary intake during treatment for alcohol use disorder (AUD).ObjectiveThe aim was to quantify dietary intake and energy balance of patients with AUD during inpatient treatment.DesignThis was a secondary analysis of data from a 4-week observational protocol. Participants self-selected food from a room service menu. Dietary intake was recorded by patients and reviewed by nutrition staff. To quantify nutrient and food group intake, data were coded into Nutrition Data Systems for Research software, versions 2016 and 2017. Daily average intake was calculated for all dietary variables.Participants/settingParticipants (n = 22) were adults seeking treatment for AUD at the National Institutes of Health Clinical Center (Bethesda, MD) between September 2016 and September 2017 and who were enrolled in a study examining the microbiome during AUD rehabilitation. Four participants discontinued protocol participation before study week 4 and were not included in analyses examining change over time.Main outcome measuresWeight change, daily energy, and macronutrient and select micronutrient intakes were the main outcome measures included.Statistical analyses performedMean differences in intake and weight were assessed using nonparametric tests.ResultsSixty-four percent of participants were male; mean ± SD age was 46.3 ± 13.0 years, mean ± SD body mass index (calculated as kg/m²) was 23.9 ± 2.5, and mean intake was 2,665 kcal/d (consisting of 45.9% carbohydrate, 34.9% fat, and 19.1% protein). Eighty percent or more of this sample met the Estimated Average Requirement for 10 of 16 micronutrients assessed. Male participants consumed more energy than estimated needs (P = .003) and gained a mean ± SD of 2.67 ± 1.84 kg (P = .006) when an outlier with weight loss and acute pancreatitis was removed from analysis. Female participants did not gain weight or consume more than estimated energy needs.ConclusionsOverall macronutrient intake was within recommended ranges, but intake of other dietary components and weight gain were variable, supporting the need for individualized nutrition care during AUD treatment.     

Dietary patterns of obese high school girls: snack consumption and energy intake

In order to develop an obesity management program for teenagers, we compared obese and non-obese girls attending high schools in terms of their dietary practices related to snack consumption. Dietary records were collected for 7 days. No significant differences were found for the average daily energy intake between obese and non-obese girls. However, the highest energy intake was greater for obese girls while not much difference was found for the lowest amount of energy intake. Obese girls had significantly lower intakes in calcium (P < 0.01), vitamin A (P < 0.001) and folate (P < 0.01). Mean energy intake from snack (594.1 ± 312.1kcal) was significantly higher for obese girls than for non-obese girls (360.1 ± 173.1kcal) (P < 0.001). A significant, positive correlation was observed between energy intake from snack and total daily energy intake (r = 0.34 P < 0.01) only for obese girls. In case of dietary behaviors, obese adolescent girls consumed significantly greater number of items for snacks and fewer foods for regular meals compared to non-obese girls (P < 0.05). This study suggested that obesity management programs for adolescents should focus on providing strategies to reduce snack through enhancing balanced regular meals.     

Incorporating the Dietary Guidelines for Americans Vegetable Recommendations into the Diet Alters Dietary Intake Patterns of Other Foods and Improves Diet Quality in Adults with Overweight and Obesity

BackgroundUnderstanding how vegetables are incorporated into the diet, especially in the types and amounts recommended by the Dietary Guidelines for Americans, and how this alters dietary intake patterns is vital for developing targeted behavior change interventions.ObjectiveTo determine how a provision of vegetables was incorporated into the diet of adults with overweight and obesity; whether or not the provided vegetables displaced other foods; and what, if any, effect this had on diet quality and body weight and composition.DesignThis study investigated secondary outcomes from the Motivating Value of Vegetables Study, a community-based, randomized, parallel, nonblinded controlled trial. Participants were randomly assigned to a provided vegetable intervention or attention control group using a one to one allocation ratio.Participants/settingMen and women with self-reported low vegetable consumption, aged 18 to 65 years, with a body mass index ≥25 were recruited from Grand Forks, ND, between October 2015 and September 2017. Only participants randomized to the provided vegetable intervention group (n = 51; attrition = 8%) were included in this secondary analysis.InterventionDietary Guidelines for Americans recommended types and amounts of vegetables were provided weekly for 8 weeks.Main outcome measuresHow the provided vegetables were incorporated into the diet was measured using daily self-report and 24-hour dietary recalls. Diet quality was assessed via the Healthy Eating Index 2015. Body weight and composition were measured before and after the intervention.Statistical analyses performedData were assessed using generalized linear mixed models where phase (pre, post) was the within-subject factor and subject was the random effect.ResultsParticipants self-reported using 29% of the provided vegetables as substitutes for other foods. With the increase in vegetable consumption, there were decreases in total grains (mean difference ± standard deviation; –0.97 ± 3.23 oz-equivalents; P = 0.02), protein foods (–1.24 ± 3.86 oz-equivalents; P = 0.01), saturated fats (–6.44 ± 19.63 g; P = 0.02), and added sugars (–2.44 ± 6.78 tsp-equivalents; P = 0.02) consumed. Total Healthy Eating Index 2015 scores increased (+4.48 ± 9.63; P = 0.001) and dietary energy density decreased (–0.44 ± 0.52 kcal/g; P < 0.0001). There was no change in total energy intake or body weight and composition.ConclusionsIncreasing vegetable consumption to meet Dietary Guidelines for Americans recommendations alters dietary intake patterns, improving diet quality and energy density. These findings highlight the importance of characterizing how individuals incorporate Dietary Guidelines for Americans recommendations into their diet.