Abstract
Introduction
Late night eating has been associated with higher odds of being overweight or obese. This study aims to evaluate the relationship between late night eating and body mass index ...in a nationally representative sample.
Methods
Actigraphy was used to estimate the average bedtime, waketime, duration and midpoint of sleep in the National Health and Nutrition Examination Survey 2003-04 and 2005-06 cohorts. Given the circular nature of clock time, the average was calculated to be the point that minimized the sum of squares of differences between time points. Dietary data was collected through two detailed interviews of the participants. Nighttime calories were defined as the average amount of calories consumed between the average bedtime and the average midpoint of time-in-bed, based on the data recorded during the dietary interviews.
Results
Higher average nighttime caloric consumption (in units of 100 kcal) was associated with higher BMI B(95% CI): 0.062 (0.003, 0.121); this remained significant after adjustment for age, gender, and race B(95% CI): 0.084 (0.026, 0.142). Higher nighttime caloric consumption (as a percentage of total average daily calories consumption) was associated with higher BMI B(95% CI): 1.522 (0.312, 2.733). This remained significant after adjustment for age, gender, and race B(95% CI): 1.718 (0.505, 2.931).
Conclusion
Higher nighttime caloric consumption, both in average amount (in units of 100 kcal) and as a percentage of average daily calories consumption, was associated with higher BMI. Additional study is needed to further elucidate the relationship between nighttime eating habits and body mass index.
Support
NHLBI T32HL110952
Background: Biocultural approaches to health research recognize that meaning and materiality mutually co-construct outcomes. However, both the "bio" and "cultural" are often unproblematized and ...analyzed through hegemonic, ethnobiocentric Western lenses. Drawing on theories of "biological normalcy," "emic validity," and "cultural consonance," this research analyzes how cultural meanings associated with men's body shape relate to men's vulnerability to obesity, body dissatisfaction, and disordered eating in South Korea. Methods: 82 young South Korean men (mean age=26.2±3.3 years) living in Seoul completed a survey assessing local understandings of what body types were considered "too thin" and "too fat," as well as their current and ideal shapes, on a figure rating scale. Cultural consensus analysis (CCA) was used to test for the presence of single cultural model of body shape. Based on CCA results for male Korean perceptions of "too thin" or "too fat," respondents were categorized into locally defined categories using their self-reported current shapes. ANOVA was used to examine variation in health outcomes across self-perceived, culturally specified body shapes. Results: CCA determined a single, strongly shared cultural model of body shape in this sample. Men whose bodies were locally characterized as "too thin," "balanced," and "too fat" varied in body mass index (BMI), body dissatisfaction (Male Body Attitudes Scale), disordered eating risk (EAT-26), and dieting (EAT-26). Men with "too fat" shapes reported the lowest average cultural consonance (an emically valid measure of male attractiveness) across body types, as well as the greatest engagement in dieting and risk for eating disorders. 41% of the men with "too fat" shapes exceeded the EAT-26 cutoff of 18 for Korean men compared to 11% of "too thin" and 14% of "balanced" men (p = 0.014). Further, "balanced" shapes reported the highest cultural consonance but were, on average, overweight by BMI (>23 by WHO standards for the Asian-Pacific Region). Conclusions: A biocultural approach challenges ethnobiocentrism in obesity research and offers insights into the importance of culture to understanding health beliefs, behaviors, outcomes.
Background: Positive psychological well-being (PPWB) is associated with improved health outcomes and positive health behaviors in general populations. However, it is not clear if this is so in women ...with obesity (BMI>30 kg/m2) who can experience poorer health and weight stigma. Thus, it is important to understand the relationships between BMI and multiple aspects of PPWB, health, and internalized weight bias (IWB). Methods: 1001 women > 18 years of age were surveyed using a web-based design. Five constructs of PPWB were measured including, positive emotion, engagement, relationships, meaning, and accomplishment (PERMA). Participants completed the PERMA-profiler (measures: PERMA, an overall well-being score, happiness, plus separate measures of health, loneliness, and negative emotion); and the modified Weight Bias Internalization Scale (WBIS-M). ANOVA was used to compare the five aspects of PERMA and happiness measures between BMI groups. MANCOVA was used to evaluate sources of variation on the significant individual PERMA measures with BMI groups, adjusting for health, IWB and demographic variables that were significantly different between BMI groups. Results: Women with obesity had significantly lower ratings of PERMA, happiness, general health, and higher IWB when compared to women in normal (BMI=18.5-24.99 kg/m2) and overweight (BMI=25-29.9 kg/m2) categories (p<.001). Health emerged as the greatest predictor of all aspects of PERMA accounting for up to 22% of the variance. IWB and BMI accounted for 1-6% of the variance in PERMA scores. Although women with class 3 obesity (BMI>40.0 kg/m2) had significantly lower PERMA scores than those with class 1 (BMI =30.0-34.9 kg/m2), obesity class was determined not to be a significant factor. Instead, health and IWB explained most of the differences. Conclusions: There is a complex relationship between health, IWB and PPWB in women. Further research is needed to understand the possible multidirectional relationship and to guide future interventions related to PPWB for women with obesity.
Background: Our group recently demonstrated no difference in weight loss at 6-months between personalized and one-size-fits-all low-fat diets. This study aimed to determine subgroup characteristics ...that would predict weight loss success. Methods: Data were analyzed on adults with abnormal glucose metabolism and obesity from a 6-month randomized clinical trial. Participants were randomized to a low-fat diet (<25% energy intake; Standardized) or a personalized diet that predicts post-prandial glycemic response to foods using a machine learning algorithm (Personalized). Both arms received identical behavioral counseling and were instructed to self-monitor dietary intake using a smartphone app. The gradient boosting machines method was used to determine which baseline variables (age, gender, race, ethnicity, income, education, BMI, metformin use, self-efficacy, glycemic variability) can predict successful weight loss (>5%) at 6-months in each study arm separately by repeated 5-fold cross validation. Results: A total of 204 adults were randomized 199 participants (Personalized: n=102 vs. Standardized: n=97) contributing data (mean standard deviation: age, 58 11 y; 67% female; body mass index, 33.9 4.8). Standardized and Personalized arms achieved 0.59 and 0.62 average AUCs, respectively. In the Standardized arm, participants who had a higher BMI (p=0.01), higher self-efficacy (p=0.006), were older (p=0.001) and were not on metformin (p=0.003) favored weight loss. In the Personalized arm, participants who had higher self-efficacy (p=0.0003) and were older (p=0.0001) favored weight loss. Conclusions: Baseline self-efficacy and age are significant contributors to weight loss success in a behavioral weight loss intervention.
Background: Ultra-processed food (UPF) intake has been increasingly recognized as an important dietary target for obesity prevention among youths, yet evidence on UPF intake and weight gain from ...longitudinal studies is limited. We aimed to evaluate the association between UPF intake and weight patterns in a large cohort of US children and adolescents. Methods: Study participants included children and adolescents (aged 7-17 years) enrolled in the Growing Up Today Studies 1 (GUTS1) and 2 (GUTS2) who completed baseline and had at least one additional assessment on diet and weight/height during follow-up (GUTS1 1996-2001: N=15,023; GUTS2 2004-2011: N=9,156). UPF was categorized based on the NOVA classification and intake was evaluated as percent energy (%E) from UPF over total daily calories. Change in self-reported weight status was assessed using change in body mass index (BMI), a more sensitive measure of adiposity change than BMI z-score in youth. Changes in BMI in association with UPF intake over 2, 4-5, and 7 years were evaluated using multivariate repeated-measure linear mixed models. Association between UPF intake and obesity risk was assessed in secondary analyses. Results: At baseline, the %E from UPF in children and adolescents was 49.9% and 49.5% in GUST1 and GUST2, respectively; the mean BMI was 18.7 kg/m2 and 19.8 kg/m2 in GUST1 and GUST2, respectively. A higher %E from UPF was associated with higher levels of total calorie intake, lower diet quality, longer sedentary time, and lower levels of family income and parental education. After multivariate adjustments for sociodemographic and lifestyle risk factors, each 10% increment in %E from UPF was associated with a 0.08 95% CI: 0.03-0.14 kg/m2 increase over 5 years in GUTS1 participants, and a 0.13 0.02-0.24 kg/m2 increase over 7 years in GUTS2 participants. No association was found between UPF intake and obesity risk. Conclusions: A higher UPF consumption was associated with greater increases in BMI in large prospective cohorts of children and adolescents in the US. Findings call for public health efforts to limit UPF consumption in youth to prevent excessive weight gain.
Background: We aimed to assess the utilization of anti-obesity medications (AOM) in patients with obesity utilizing a population level database. Methods: We performed a retrospective cohort study ...using an electronic health record-derived database (Explorys, IBM Watson Health) from 01/2017 to 9/2022. Adults with obesity body mass index (BMI) > 30 kg/m2 (Class I, II, III) were identified and the usage of AOM was analyzed (semaglutide, liraglutide, naltrexone/bupropion, orlistat, metformin, phentermine, topiramate, and phentermine/topiramate). Patients with a history of diabetes mellitus (DM) were subgrouped within the anti-diabetes medication for subgroup analysis. Patients with a history of migraines or seizure disorders were excluded from the topiramate group. Results: A total of 3,339,630 patients with obesity were included. Prevalence of AOM prescription was as followed: semaglutide (0.71%), liraglutide (2.58%), naltrexone/bupropion (0.82%), orlistat (0.24%), metformin with DM (17.82%), metformin without DM (2.26%), phentermine (3.98%), topiramate (2.24%), and phentermine/ topiramate (0.61%). Patients with obesity class III (BMI >40) had increased utilization of AOM compared to patients with BMI <40: semaglutide (OR 1.32; p<0.001), liraglutide (OR 1.58; p<0.001), naltrexone/bupropion (OR 1.46; p<0.001), orlistat (OR 1.60; p<0.001), metformin with DM (OR 1.25; p<0.001), metformin without DM (OR 1.75; p<0.001), phentermine (OR 1.38; p<0.001), topiramate (OR 1.60; p<0.001), phentermine/topiramate (OR 1.24; p<0.001). Conclusions: Amongst patients with obesity, prescription of AOM is very low and it appeared that AOM use is increased in late-stage obesity (severe/class III) rather than earlier in the disease process (class I, II). Further investigations into barriers of prescription of AOM are needed to increase utilization and prescription of AOM for patients with obesity.
Background: The main regulators of body weight are food intake and energy expenditure. Hunger and satiation are important factors of food intake regulation, whereas resting metabolic rate (RMR) is ...the main determinant of energy expenditure. The pathophysiology of obesity is associated with changes in food intake and energy expenditure; however, their evolution throughout the lifespan of adult with obesity has not been studied simultaneously. Methods: This is a cross-sectional study of patients with obesity aged 19 to 70 years grouped by decades. We assessed hunger using 100-mm visual analog scales (VAS) 4 hours after a 320kcal breakfast, satiation using calories to fullness (CTF) in an ad libitum meal, and RMR by indirect calorimetry. We studied the effect age on these three parameters using an ANCOVA model that included age category by decades, sex, and body mass index (BMI) as covariates corrected by Dunnett's Test. We chose patients aged 19 to 29 years as control group. Results: We included 470 patients (mean SD age 42.111 years, 78% females, BMI 39.17.2). Patients were distributed in the following age groups: 19-29 years (n=70; BMI 37.6 6.4); 30-39 years (n=140; BMI 39.57.4); 40-49 years (n=132; BMI 39.78.0); 50-59 years (n=94; BMI 38.76.3); 60-69 years (n=34; BMI 37.8 6.6). Hunger was significantly different among groups (p=0.003) with a decrease on hunger levels of 13.6 mm (SE 5.3; p=0.03) in older patients (60 to 69 years) vs. controls. CTF differed significantly across groups (p<0.001) with older patients eating 163.7 kcal less (standard error SE 60.2; p=0.02) vs. controls. RMR was significantly different among groups (p<0.001) with 241.9 kcal per day (SE 55.7; p<.001) less in older patients vs. controls. Conclusion: We noticed a downward trend in food intake and energy expenditure, especially among older adults. These shifts offer information on human obesity and aging and should be considered when developing treatments for people of all ages.
Objective: The development of these updated clinical practice guidelines (CPGs) was commissioned by the American Association of Clinical Endocrinologists (AACE), The Obesity Society (TOS), American ...Society for Metabolic and Bariatric Surgery (ASMBS), Obesity Medicine Association (OMA), and American Society of Anesthesiologists (ASA) Boards of Directors in adherence with the AACE 2017 protocol for standardized production of CPGs, algorithms, and checklists. Methods: Each recommendation was evaluated and updated based on new evidence from 2013 to the present and subjective factors provided by experts. Results: New or updated topics in this CPG include: contextualization in an adiposity-based chronic disease complications-centric model, nuance-based and algorithm/checklist-assisted clinical decision-making about procedure selection, novel bariatric procedures, enhanced recovery after bariatric surgery protocols, and logistical concerns (including cost factors) in the current health care arena. There are 85 numbered recommendations that have updated supporting evidence, of which 61 are revised and 12 are new. Noting that there can be multiple recommendation statements within a single numbered recommendation, there are 31 (13%) Grade A, 42 (17%) Grade B, 72 (29%) Grade C, and 101 (41%) Grade D recommendations. There are 858 citations, of which 81 (9.4%) are evidence level (EL) 1 (highest), 562 (65.5%) are EL 2, 72 (8.4%) are EL 3, and 143 (16.7%) are EL 4 (lowest). Conclusions: Bariatric procedures remain a safe and effective intervention for higher-risk patients with obesity. Clinical decision-making should be evidence based within the context of a chronic disease. A team approach to perioperative care is mandatory, with special attention to nutritional and metabolic issues.
The body mass index (BMI) is the metric currently in use for defining anthropometric height/weight characteristics in adults and for classifying (categorizing) them into groups. The common ...interpretation is that it represents an index of an individual’s fatness. It also is widely used as a risk factor for the development of or the prevalence of several health issues. In addition, it is widely used in determining public health policies.The BMI has been useful in population-based studies by virtue of its wide acceptance in defining specific categories of body mass as a health issue. However, it is increasingly clear that BMI is a rather poor indicator of percent of body fat. Importantly, the BMI also does not capture information on the mass of fat in different body sites. The latter is related not only to untoward health issues but to social issues as well. Lastly, current evidence indicates there is a wide range of BMIs over which mortality risk is modest, and this is age related. All of these issues are discussed in this brief review.
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