New insights in the mechanisms of weight-loss maintenance: Summary from a Pennington symposium

Obesity is a chronic disease that affects more than 650 million adults worldwide. Obesity not only is a significant health concern on its own, but predisposes to cardiometabolic comorbidities, including coronary heart disease, dyslipidemia, hypertension, type 2 diabetes, and some cancers. Lifestyle interventions effectively promote weight loss of 5% to 10%, and pharmacological and surgical interventions even more, with some novel approved drugs inducing up to an average of 25% weight loss. Yet, maintaining weight loss over the long-term remains extremely challenging, and subsequent weight gain is typical. The mechanisms underlying weight regain remain to be fully elucidated. The purpose of this Pennington Biomedical Scientific Symposium was to review and highlight the complex interplay between the physiological, behavioral, and environmental systems controlling energy intake and expenditure. Each of these contributions were further discussed in the context of weight-loss maintenance, and systems-level viewpoints were highlighted to interpret gaps in current approaches. The invited speakers built upon the science of obesity and weight loss to collectively propose future research directions that will aid in revealing the complicated mechanisms involved in the weight-reduced state.

The Microbiome, Epigenome, and Diet in Adults with Obesity during Behavioral Weight Loss

Obesity has been linked to the gut microbiome, epigenome, and diet, yet these factors have not been studied together during obesity treatment. Our objective was to evaluate associations among gut microbiota (MB), DNA methylation (DNAme), and diet prior to and during a behavioral weight loss intervention. Adults (n = 47, age 40.9 ± 9.7 years, body mass index (BMI) 33.5 ± 4.5 kg/m2, 77% female) with data collected at baseline (BL) and 3 months (3 m) were included. Fecal MB was assessed via 16S sequencing and whole blood DNAme via the Infinium EPIC array. Food group and nutrient intakes and Healthy Eating Index (HEI) scores were calculated from 7-day diet records. Linear models were used to test for the effect of taxa relative abundance on DNAme and diet cross-sectionally at each time point, adjusting for confounders and a false discovery rate of 5%. Mean weight loss was 6.2 ± 3.9% at 3 m. At BL, one MB taxon, Ruminiclostridium, was associated with DNAme of the genes COL20A1 (r = 0.651, p = 0.029), COL18A1 (r = 0.578, p = 0.044), and NT5E (r = 0.365, p = 0.043). At 3 m, there were 14 unique MB:DNAme associations, such as Akkermansia with DNAme of GUSB (r = -0.585, p = 0.003), CRYL1 (r = -0.419, p = 0.007), C9 (r = -0.439, p = 0.019), and GMDS (r = -0.559, p = 0.046). Among taxa associated with DNAme, no significant relationships were seen with dietary intakes of relevant nutrients, food groups, or HEI scores. Our findings indicate that microbes linked to mucin degradation, short-chain fatty acid production, and body weight are associated with DNAme of phenotypically relevant genes. These relationships offer an initial understanding of the possible routes by which alterations in gut MB may influence metabolism during weight loss.

Impact of the built, social, and food environment on long-term weight loss within a behavioral weight loss intervention

Background

Behavioral weight loss interventions can lead to an average weight loss of 5%-10% of initial body weight, however there is wide individual variability in treatment response. Although built, social, and community food environments can have potential direct and indirect influences on body weight (through their influence on physical activity and energy intake), these environmental factors are rarely considered as predictors of variation in weight loss.

Objective

Evaluate the association between built, social, and community food environments and changes in weight, moderate-to-vigorous physical activity (MVPA), and dietary intake among adults who completed an 18-month behavioral weight loss intervention.

Methods

Participants included 93 adults (mean ± SD; 41.5 ± 8.3 years, 34.4 ± 4.2 kg/m2, 82% female, 75% white). Environmental variables included urbanicity, walkability, crime, Neighborhood Deprivation Index (includes 13 social economic status factors), and density of convenience stores, grocery stores, and limited-service restaurants at the tract level. Linear regressions examined associations between environment and changes in body weight, waist circumference (WC), MVPA (SenseWear device), and dietary intake (3-day diet records) from baseline to 18 months.

Results

Grocery store density was inversely associated with change in weight (β = -0.95; p = 0.02; R 2 = 0.062) and WC (β = -1.23; p < 0.01; R 2 = 0.109). Participants living in tracts with lower walkability demonstrated lower baseline MVPA and greater increases in MVPA versus participants with higher walkability (interaction p = 0.03). Participants living in tracts with the most deprivation demonstrated greater increases in average daily steps (β = 2048.27; p = 0.02; R 2 = 0.039) versus participants with the least deprivation. Limited-service restaurant density was associated with change in % protein intake (β = 0.39; p = 0.046; R 2 = 0.051).

Conclusion

Environmental factors accounted for some of the variability (<11%) in response to a behavioral weight loss intervention. Grocery store density was positively associated with weight loss at 18 months. Additional studies and/or pooled analyses, encompassing greater environmental variation, are required to further evaluate whether environment contributes to weight loss variability.

Characterizing Patient Representations for Computational Phenotyping

Patient representation learning methods create rich representations of complex data and have potential to further advance the development of computational phenotypes (CP). Currently, these methods are either applied to small predefined concept sets or all available patient data, limiting the potential for novel discovery and reducing the explainability of the resulting representations. We report on an extensive, data-driven characterization of the utility of patient representation learning methods for the purpose of CP development or automatization. We conducted ablation studies to examine the impact of patient representations, built using data from different combinations of data types and sampling windows on rare disease classification. We demonstrated that the data type and sampling window directly impact classification and clustering performance, and these results differ by rare disease group. Our results, although preliminary, exemplify the importance of and need for data-driven characterization in patient representation-based CP development pipelines.

Predicting energy intake in adults who are dieting and exercising

BACKGROUND

When a lifestyle intervention combines caloric restriction and increased physical activity energy expenditure (PAEE), there are two components of energy balance, energy intake (EI) and physical activity energy expenditure (PAEE), that are routinely misreported and expensive to measure. Energy balance models have successfully predicted EI if PAEE is known. Estimating EI from an energy balance model when PAEE is not known remains an open question.

OBJECTIVE

The objective was to evaluate the performance of an energy balance differential equation model to predict EI in an intervention that includes both calorie restriction and increases in PAEE.

DESIGN

The Antonetti energy balance model that predicts body weight trajectories during weight loss was solved and inverted to estimate EI during weight loss. Using data from a calorie restriction study that included interventions with and without prescribed PAEE, we tested the validity of the Antonetti weight predictions against measured weight and the Antonetti EI model against measured EI using the intake-balance method at 168 days. We then evaluated the predicted EI from the model against measured EI in a study that prescribed both calorie restriction and increased PAEE.

RESULTS

Compared with measured body weight at 168 days, the mean (±SD) model error was 1.30 ± 3.58 kg. Compared with measured EI at 168 days, the mean EI (±SD) model error in the intervention that prescribed calorie restriction and did not prescribe increased PAEE, was -84.9 ± 227.4 kcal/d. In the intervention that prescribed calorie restriction combined with increased PAEE, the mean (±SD) EI model error was -155.70 ± 205.70 kcal/d.

CONCLUSION

The validity of the newly developed EI model was supported by experimental observations and can be used to determine EI during weight loss.

Proteomics, dietary intake, and changes in cardiometabolic health within a behavioral weight-loss intervention: A pilot study

OBJECTIVE

Identifying associations among circulating proteins, dietary intakes, and clinically relevant indicators of cardiometabolic health during weight loss may elucidate biologically relevant pathways affected by diet, allowing for an incorporation of precision nutrition approaches when designing future interventions. This study hypothesized that plasma proteins would be associated with diet and cardiometabolic health indicators within a behavioral weight-loss intervention.

METHODS

This secondary data analysis included participants (n = 20, mean [SD], age: 40.1 [9.5] years, BMI: 34.2 [4.0] kg/m2 ) who completed a 1-year behavioral weight-loss intervention. Cardiovascular disease-related plasma proteins, diet, and cardiometabolic health indicators were evaluated at baseline and 3 months. Associations were determined via linear regression and integrated networks created using Visualization Of LineAr Regression Elements (VOLARE).

RESULTS

A total of 16 plasma proteins were associated with ≥1 diet or health indicator at baseline (p < 0.001); changes in 42 proteins were associated with changes in diet or health indicators from baseline to 3 months (p < 0.005). Baseline tumor necrosis factor receptor superfamily member 10C (TNFRSF10C) was associated with intakes of dark green vegetables (r = -0.712), and fatty acid-binding protein 4 (FABP4) was associated with intakes of unsweetened coffee (r = -0.689). Changes in refined-grain intakes were associated with changes in scavenger receptor cysteine-rich type 1 protein M130 (CD163; r = 0.725), interleukin-1 receptor type 1 (IL1R-T1; r = 0.624), insulin (r = 0.656), and triglycerides (r = 0.648).

CONCLUSIONS

Circulating cardiovascular disease-related proteins were associated with diet and cardiometabolic health indicators prior to and in response to weight loss.

RF24 | PSUN110 A Cross-Sectional Study Evaluating the Impact of Combined Hormonal Contraceptives on Components of Energy Balance in Pre-Menopausal Women with Overweight or Obesity

Background

Combined estrogen and progestin hormonal contraceptives (CHCs, including combined oral contraceptives, hormonal patches, or vaginal rings) expose women to supraphysiologic levels of reproductive hormones, which in turn suppress endogenous estrogen and progesterone. Sustained elevations of synthetic estrogen/progestin or suppression of endogenous hormones in CHC users may lead to altered dietary intake and/or patterns of physical activity (PA). Previously, we reported that CHC use was associated with higher energy intake (EI) and weight regain over 1 year after weight loss in women with overweight and obesity enrolled in a behavioral weight loss program, suggesting a potential impact of CHCs on energy balance in the weight reduced state. The aim of this secondary data analysis was to compare dietary intake and PA in weight-stable women with overweight or obesity using CHCs to non-CHC users.

Methods

Pre-menopausal women with overweight or obesity (n=269, age 18-55 years, BMI 27-45kg/m2) enrolled in 3 different interventional weight loss trials were categorized as CHC users (CHC, n=48) or non-CHC controls (CON, n=221). Fat mass (FM) and lean mass (LM) were measured with DXA. Self-reported dietary energy and macronutrient intake was obtained from either 3-day (n=178) or 7-day (n=91) written diet diaries and analyzed using Nutrition Data System for Research (NDSR) software. Healthy eating index (HEI) scores for diet quality were calculated in a subset (CHC=17, CON=84) of participants using variables available in NDSR output files. Additionally, average daily step counts were measured over 1 week in a subset (CHC=27, CON=143) of participants using the activPAL device.

Results

Age was lower in CHC users (mean±SD; CHC 35.3±9.0 vs. CON 39.4±7.6 years, p<0.01), but race and ethnicity did not differ between the two groups. After controlling for age, there were no significant differences between groups in baseline BMI (mean±SEM; CHC 34.3±0.7 vs. CON 34.8±0.3 kg/m2), weight (93.6±2.4 vs. 94.2±1.1 kg), %FM (43.3±0.6 vs. 42.7±0.3%), or %LM (54.3±0.6 vs. 54.7±0.3%). There were no significant differences between groups in EI (mean±SEM; CHC 1763.7±78.1 vs. CON 1768.8±36.7 kcal); proportions of fat (36.7±0.9 vs. 36.9±0.4%), carbohydrate (43.9±1.1 vs. 44.3±0.5%), and protein intake (17.7±0.6 vs. 17.3±0.3%); or HEI scores (58.3±2.9 vs. 56.8±1.3). There was a trend for lower daily step count in CHC users (mean±SEM; CHC 5978±460 vs. CON 6913±197 steps/day, p=0.07).

Conclusion

Daily steps tended to be lower in CHC users compared to controls, while differences in self-reported dietary intake were not observed. Future studies that are done in larger samples over a greater range of BMI and with more detailed measures of PA timed to the menstrual cycle are needed to explore the extent to which CHC use may impact energy balance in women.

Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m., Sunday, June 12, 2022 1:06 p.m. - 1:11 p.m.

Cancer-related fatigue mediates the relationships between physical fitness and attendance and quality of life after participation in a clinical exercise program for survivors of cancer

PURPOSE

Cancer-related fatigue (CRF) is a common and limiting symptom reported by survivors of cancer, negatively impacting health-related quality of life (HRQoL). Exercise improves CRF, HRQoL, and physical fitness in survivors. Prospective research trials have shown that exercise-associated fitness improvements effects on HRQoL are mediated by CRF; however, this has not been investigated in a pragmatic real-world setting. This study utilizes data from a large heterogenous population of survivors participating in a clinical exercise program to investigate this mediation effect, as well as effects of program attendance.

METHODS

Data were collected from 194 survivors completing the BfitBwell Cancer Exercise Program (July 2016-February 2020). Changes in HRQoL, CRF, and fitness were calculated and program attendance collected. Basic correlation analyses were performed. Linear regression analyses were performed to assess mediation by CRF.

RESULTS

All measures of CRF, HRQoL, and physical fitness significantly improved following the exercise program. Improvements in physical fitness were significantly correlated with improvements in HRQoL (r = 0.15-0.18), as was program attendance (r = 0.26) and CRF (r = 0.59). The effects of physical fitness and program attendance on HRQoL were at least partially mediated by the effects of CRF.

CONCLUSION

This study extends research findings on how exercise programs improve HRQoL in survivors of cancer to a real-world setting. Results indicate that clinical exercise programs should target reductions in CRF in survivors (during or after treatment) through improvements in physical fitness to improve HRQoL and that high attendance should be encouraged regardless of fitness changes.

Comparison of weight loss induced by daily caloric restriction versus intermittent fasting (DRIFT) in individuals with obesity: study protocol for a 52-week randomized clinical trial

BACKGROUND

The standard of care for treating overweight and obesity is daily caloric restriction (DCR). While this approach produces modest weight loss, adherence to DCR declines over time and weight regain is common. Intermittent fasting (IMF) is an alternative dietary strategy for reducing energy intake (EI) that involves >60% energy restriction on 2-3 days per week, or on alternate days, with habitual intake on fed days. While numerous studies have evaluated IMF as a weight loss strategy, there are several limitations including lack of a standard-of-care DCR control, failure to provide guideline-based behavioral support, and failure to rigorously evaluate dietary and PA adherence using objective measures. To date, only three longer-term (52-week) trials have evaluated IMF as a weight loss strategy. None of these longer-duration studies reported significant differences between IMF and DCR in changes in weight. However, each of these studies has limitations that prohibit drawing generalizable conclusions about the relative long-term efficacy of IMF vs. DCR for obesity treatment.

METHODS

The Daily Caloric Restriction vs. Intermittent Fasting Trial (DRIFT) is a two-arm, 52-week block randomized (1:1) clinical weight loss trial. The two intervention arms (DCR and IMF) are designed to prescribe an equivalent average weekly energy deficit from baseline weight maintenance energy requirements. Both DCR and IMF will be provided guideline-based behavioral support and a PA prescription. The primary outcome is change in body weight at 52 weeks. Secondary outcomes include changes in body composition (dual-energy x-ray absorptiometry (DXA)), metabolic parameters, total daily energy expenditure (TDEE, doubly labeled water (DLW)), EI (DLW intake-balance method, 7-day diet diaries), and patterns of physical activity (PA, activPAL device).

DISCUSSION

Although DCR leads to modest weight loss success in the short-term, there is wide inter-individual variability in weight loss and poor long-term weight loss maintenance. Evidence-based dietary approaches to energy restriction that are effective long-term are needed to provide a range of evidence-based options to individuals seeking weight loss. The DRIFT study will evaluate the long-term effectiveness of IMF vs. DCR on changes in objectively measured weight, EI, and PA, when these approaches are delivered using guideline-based behavioral support and PA prescriptions.

Improving lifestyle obesity treatment during the COVID-19 pandemic and beyond: New challenges for weight management

Objective

The COVID-19 pandemic has resulted in significant changes to daily life and many health-related behaviors. The objective of this study was to examine how the stay-at-home/safer-at-home mandates issued in Colorado (March 2020-May 2020) impacted lifestyle behaviors and mental health among individuals with overweight or obesity participating in two separate behavioral weight loss trials (n = 82).

Methods

Questionnaires were used to collect qualitative and quantitative data on challenges to weight loss presented by the COVID-19 pandemic, including changes in dietary intake, physical activity, sedentary behavior, and mental health during the stay-at-home/safer-at-home mandates.

Results

Using a convergent mixed method approach integrating qualitative and quantitative data, the greatest challenge experienced by participants was increased stress and anxiety, which led to more unhealthy behaviors. The majority perceived it to be harder to adhere to the prescribed diet (81%) and recommended physical activity (68%); however, self-reported exercise on weekdays increased significantly and 92% of participants lost weight or maintained weight within ±1% 5-6 weeks following the stay-at-home mandate.

Conclusion

Study results suggest that obesity treatment programs should consider and attempt to address the burden of stress and anxiety stemming from the COVID-19 pandemic and other sources due to the negative effects they can have on weight management and associated behaviors.

Creating an algorithm to identify indices of sleep quantity and quality from a wearable armband in adults

Objective

Material and Methods

Results

Conclusion

Motivational profiles and change in physical activity during a weight loss intervention: a secondary data analysis

BACKGROUND

High levels of moderate-to-vigorous intensity physical activity (MVPA) are strongly associated with sustained weight loss, however the majority of adults are unsuccessful in maintaining high levels of MVPA long-term. Our goal was to identify profiles based on exercise motives, and examine the association between motivational profile and longitudinal changes in MVPA during a weight loss intervention.

METHODS

Adults with overweight or obesity (n = 169, mean ± SE; age 39 ± 0.7 years, BMI 34.4 ± 0.3 kg/m2, 83% female) underwent an 18-month behavioral weight loss program, including 6 months of supervised exercise, followed by 6 months of unsupervised exercise. Participants self-reported behavioral regulations for exercise at baseline (BREQ-2). Latent profile analysis identified subgroups from external, introjected, identified, and intrinsic regulations measured at baseline. Mean differences in device-measured total MVPA were compared across motivational profiles at baseline, after 6 months of supervised exercise and after a subsequent 6 months of unsupervised exercise.

RESULTS

Three motivational profiles emerged: high autonomous (high identified and intrinsic, low external regulations; n = 52), high combined (high scores on all exercise regulations; n = 25), and moderate combined (moderate scores on all exercise regulations; n = 92). Motivational profile was not associated with baseline level of MVPA or the increase in MVPA over the 6-month supervised exercise intervention (high autonomous: 21 ± 6 min/d; high combined: 20 ± 9 min/d; moderate combined: 33 ± 5 min/d; overall P > 0.05). However, during the transition from supervised to unsupervised exercise, MVPA decreased, on average, within all three profiles, but the high autonomous profile demonstrated the least attenuation in MVPA (- 3 ± 6 min/d) compared to the moderate combined profile (- 20 ± 5 min/d; P = 0.043).

CONCLUSIONS

Results were in alignment with the Self-Determination Theory. Adults motivated by autonomous reasons (value benefits of exercise, intrinsic enjoyment) may be more likely to sustain increases in MVPA once support is removed, whereas participants with moderate-to-high scores on all types of exercise regulations may need additional long-term support in order to sustain initial increases in MVPA.

CLINICAL TRIAL REGISTRATION

NCT01985568. Registered 24 October 2013.

Predictors of long-term weight loss trajectories during a behavioral weight loss intervention: An exploratory analysis

BACKGROUND

Substantial interindividual variability in response to behavioral weight loss interventions remains a critical challenge in obesity treatment. An improved understanding of the complex factors that contribute to this variability may improve obesity treatment outcomes.

OBJECTIVE

To identify weight change trajectories during a behavioral weight loss intervention and to explore differences between trajectory groups in sociodemographic, biologic, behavioral, and psychosocial factors.

METHODS

Adults (n = 170, 40 ± 9 years, BMI 34 ± 4 kg/m2, 84% female) participated in an 18-month behavioral weight loss intervention. Weight was measured at 0, 3, 6, 9, 12, 15, 18, and 24 months. Among participants with at least two weights after baseline (n = 140), clusters of longitudinal trajectories of changes in weight were identified using a latent class growth mixture model. The association between baseline factors or changes in factors over time and trajectory group was examined.

RESULTS

Two weight change trajectories were identified: "weight regainers" (n = 91) and "weight loss maintainers" (n = 49). Black participants (90%, 19/21) were more likely than non-Black participants to be regainers versus maintainers (p < 0.01). Maintainers demonstrated greater increases in device-measured physical activity, autonomous motivation for exercise, diet self-efficacy, cognitive restraint, and engagement in weight management behaviors and greater reductions in barriers for exercise, disinhibition, and depressive symptoms over 24 months versus regainers (p < 0.05).

CONCLUSION

Maintainers and regainers appear to be distinct trajectories that are associated with specific sociodemographic, behavioral, and psychosocial factors. Study results suggest potential targets for more tailored, multifaceted interventions to improve obesity treatment outcomes.

Weight and body composition changes affect resting energy expenditure predictive equations during a 12-month weight-loss intervention

OBJECTIVE

Mathematical equations that predict resting energy expenditure (REE) are widely used to derive calorie prescriptions during weight-loss interventions. Although such equations are known to introduce group- and individual-level error into REE prediction, their validity has largely been assessed in weight-stable populations. Therefore, this study sought to characterize how weight change affects the validity of commonly used REE predictive models throughout a 12-month weight-loss intervention.

METHODS

Changes in predictive error of four models (Mifflin-St-Jeor, Harris-Benedict, Owen, and World Health Organization/Food and Agriculture) were assessed at 1-, 6-, and 12-month time points in adults (n = 66, 76% female, aged 18-55 years, BMI = 27-45 kg/m² ) enrolled in a randomized clinical weight-loss trial.

RESULTS

All equations experienced significant negative shifts in bias (measured - predicted REE) toward overprediction from baseline to 1 month (p < 0.05). Three equations showed reversal of bias in the positive direction (toward underprediction) from baseline to 12 months (p < 0.05). Early changes in bias were correlated with decreased fat-free mass (p ≤ 0.01).

CONCLUSIONS

Changes in body composition and mass during a 12-month weight-loss intervention significantly affected REE predictive error in adults with overweight and obesity. Weight history should be considered when using mathematical models to predict REE during periods of weight fluctuation.

The Gut Microbiota during a Behavioral Weight Loss Intervention

Altered gut microbiota has been linked to obesity and may influence weight loss. We are conducting an ongoing weight loss trial, comparing daily caloric restriction (DCR) to intermittent fasting (IMF) in adults who are overweight or obese. We report here an ancillary study of the gut microbiota and selected obesity-related parameters at the baseline and after the first three months of interventions. During this time, participants experienced significant improvements in clinical health measures, along with altered composition and diversity of fecal microbiota. We observed significant associations between the gut microbiota features and clinical measures, including weight and waist circumference, as well as changes in these clinical measures over time. Analysis by intervention group found between-group differences in the relative abundance of Akkermansia in response to the interventions. Our results provide insight into the impact of baseline gut microbiota on weight loss responsiveness as well as the early effects of DCR and IMF on gut microbiota.

Temporal patterns of physical activity in successful weight loss maintainers

BACKGROUND/OBJECTIVES

Individuals successful at weight loss maintenance engage in high amounts of physical activity (PA). Understanding how and when weight loss maintainers accumulate PA within a day and across the week may inform PA promotion strategies and recommendations for weight management.

METHODS

We compared patterns of PA in a cohort of weight loss maintainers (WLM, n = 28, maintaining ≥13.6 kg weight loss for ≥1 year, BMI 23.6 ± 2.3 kg/m²), controls without obesity (NC, n = 30, BMI similar to current BMI of WLM, BMI 22.8 ± 1.9 kg/m²), and controls with overweight/obesity (OC, n = 26, BMI similar to pre-weight loss BMI of WLM, 33.6 ± 5.1 kg/m²). PA was assessed during 7 consecutive days using the activPAL^TM activity monitor. The following variables were quantified; sleep duration, sedentary time (SED), light-intensity PA (LPA), moderate-to-vigorous intensity PA (MVPA), and steps. Data were examined to determine differences in patterns of PA across the week and across the day using mixed effect models.

RESULTS

Across the week, WLM engaged in ≥60 min of MVPA on 73% of days, significantly more than OC (36%, p < 0.001) and similar to NC (59%, p = 0.10). Across the day, WLM accumulated more MVPA in the morning (i.e., within 3 h of waking) compared to both NC and OC (p < 0.01). WLM engaged in significantly more MVPA accumulated in bouts ≥10 min compared to NC and OC (p < 0.05). Specifically, WLM engaged in more MVPA accumulated in bouts of ≥60 min compared to NC and OC (p < 0.05).

CONCLUSIONS

WLM engage in high amounts of MVPA (≥60 min/d) on more days of the week, accumulate more MVPA in sustained bouts, and accumulate more MVPA in the morning compared to controls. Future research should investigate if these distinct patterns of PA help to promote weight loss maintenance.

Multiomic Predictors of Short-Term Weight Loss and Clinical Outcomes During a Behavioral-Based Weight Loss Intervention

OBJECTIVE

Identifying predictors of weight loss and clinical outcomes may increase understanding of individual variability in weight loss response. We hypothesized that baseline multiomic features, including DNA methylation (DNAme), metabolomics, and gut microbiome, would be predictive of short-term changes in body weight and other clinical outcomes within a comprehensive weight loss intervention.

METHODS

Healthy adults with overweight or obesity (n = 62, age 18-55 years, BMI 27-45 kg/m2 , 75.8% female) participated in a 1-year behavioral weight loss intervention. To identify baseline omic predictors of changes in clinical outcomes at 3 and 6 months, whole-blood DNAme, plasma metabolites, and gut microbial genera were analyzed.

RESULTS

A network of multiomic relationships informed predictive models for 10 clinical outcomes (body weight, waist circumference, fat mass, hemoglobin A1c , homeostatic model assessment of insulin resistance, total cholesterol, triglycerides, C-reactive protein, leptin, and ghrelin) that changed significantly (P < 0.05). For eight of these, adjusted R2 ranged from 0.34 to 0.78. Our models identified specific DNAme sites, gut microbes, and metabolites that were predictive of variability in weight loss, waist circumference, and circulating triglycerides and that are biologically relevant to obesity and metabolic pathways.

CONCLUSIONS

These data support the feasibility of using baseline multiomic features to provide insight for precision nutrition-based weight loss interventions.

Underreporting of energy intake in weight loss maintainers

BACKGROUND

Individuals with overweight or obesity commonly underreport energy intake (EI), but it is unknown if the tendency to underreport persists in formerly obese individuals who lose significant weight and maintain their weight loss over long periods of time.

OBJECTIVE

Assess the accuracy of self-reported EI in successful weight loss maintainers (WLM) compared with controls of normal body weight (NC) and controls with overweight/obesity (OC).

METHODS

Participants for this case-controlled study were recruited in 3 groups: WLM [n = 26, BMI (in kg/m2) 24.1 ± 2.3; maintaining ≥13.6 kg weight loss for ≥1 y], NC (n = 33, BMI 22.7 ± 1.9; similar to current BMI of WLM), and OC (n = 32, BMI 34.0 ± 4.6; similar to pre-weight loss BMI of WLM). Total daily energy expenditure (TDEE) was measured over 7 d using the doubly labeled water (DLW) method, and self-reported EI was concurrently measured from 3-d diet diaries. DLW TDEE and self-reported EI were compared to determine accuracy of self-reported EI.

RESULTS

WLM underreported EI (median, interquartile range) (-605, -915 to -314 kcal/d) to a greater degree than NC (-308, -471 to -68 kcal/d; P < 0.01) but not more than OC (-310, -970 to 18 kcal/d; P = 0.21). WLM also showed a greater degree of relative underreporting (-25.3%, -32.9% to -12.5%) compared with NC (-14.3%, -19.6% to -3.1%; P = 0.02) but not OC (-11.2%, -34.1% to -0.7%; P = 0.16). A greater proportion of WLM was classified as underreporters (30.8%) than NC (9.1%; P = 0.05) but not OC (28.1%; P = 1.00).

CONCLUSIONS

WLM underreported EI in both absolute and relative terms to a greater extent than NC but not OC. These findings call into question the accuracy of self-reported EI in WLM published in previous studies and align with recent data suggesting that WLM rely less on chronic EI restriction and more on high levels of physical activity to maintain weight loss. This trial was registered at clinicaltrials.gov as NCT03422380.

Impact of Combined Hormonal Contraceptive Use on Weight Loss: A Secondary Analysis of a Behavioral Weight-Loss Trial

OBJECTIVE

This study aimed to perform a preliminary investigation of the impact of combined hormonal contraceptive (CHC) use on weight loss during an 18-month behavioral weight-loss trial.

METHODS

Adults (n = 170; 18-55 years; BMI 27-42 kg/m² ) received a weight-loss intervention that included a reduced-calorie diet, a progressive exercise prescription, and group-based behavioral support. Premenopausal women (n = 110) were classified as CHC users (CHC, n = 17) or non-CHC users (non-CHC, n = 93). Changes in weight were examined within groups using a linear mixed model, adjusted for age and randomized group assignment.

RESULTS

At 6 M, weight was reduced from baseline in both CHC (mean, -6.7 kg; 95% CI: -9.8 to -3.7 kg) and non-CHC (-9.1 kg; -9.1 to -6.4 kg). Between 6 and 18 M, CHC regained weight (4.9 kg; 0.9 to 8.9 kg), while weight remained relatively unchanged in non-CHC (-0.1 kg; -1.8 to 1.6 kg). At 18 M, weight was relatively unchanged from baseline in CHC (-1.8 kg; -7.3 to 3.6 kg) and was reduced from baseline in non-CHC (-7.9 kg; -10.2 to -5.5 kg).

CONCLUSIONS

In this secondary data analysis, CHC use was associated with weight regain after initial weight loss. Prospective studies are needed to further understand the extent to which CHC use influences weight loss and maintenance.

SAT-575 Association Between Baseline Fitness and Changes in Physical Activity and Weight Loss in an 18-Month Behavioral Weight Loss Program

BACKGROUND: Baseline cardiovascular fitness may be a significant predictor of future success in a comprehensive behavioral weight loss program (BWLP). Yet, few studies have examined the association between baseline fitness and future weight loss.

PURPOSE: To determine the association between baseline fitness and changes in body weight and device-measured levels of moderate-to-vigorous physical activity (MVPA) during a BWLP.

METHODS: Adults (n=85) were enrolled in an 18-month BWLP combining a calorie-restricted diet, group-based behavioral support, and 6 months of supervised exercise (progressing to 300 min/wk of moderate-intensity) followed by 12 months of unsupervised exercise. Data from 60 completers (age 41.0±9.5 years, BMI 34.6±4.2 kg/m2, 80% female) were used in this analysis. MVPA was measured over 1 week with the Sensewear Armband at months 0, 6, 12, and 18. Fitness (VO2max) was measured on a treadmill using indirect calorimetry and categorized based on published age and sex norms (Physical Fitness Specialist Certification Manual, 1997). A linear mixed effects model with unstructured covariance was used to examine the association between baseline fitness category and changes in body weight, total MVPA, and MVPA in bouts ≥10 min at the four time points.

RESULTS: Of the 60 completers, 33% (n=20) were classified as having very poor fitness, 45% (n=27) poor, 18% (n=11) fair, 3% (n=2) good, and 0% (n=0) excellent or superior. Due to the low proportion of participants categorized as having fair or better fitness, we created a binary fitness variable (very poor vs. poor or better). Baseline BMI was higher in those in the very poor category compared to those in the poor or better category (36.2±4.2 vs 33.7±4.0, p=0.03). There were no significant differences between the two fitness categories in weight change at 6 or 12 months. However, at 18 months, mean weight loss was 4.3±1.7 kg in those in the very poor category and 8.2±1.2 kg in those in the poor or better category, with a marginally significant between-group difference (p=0.07). There were no differences in changes in total or bout MVPA. However, those with very poor fitness had lower bout MVPA at baseline vs. those with poor or better fitness (16±20 vs 33±31 min/d, p=0.03). At 18 months, both groups increased bout MVPA, however bout MVPA remained lower in the very poor vs. poor or better group (24±29 vs 42±29 min/d, p=0.03). Total MVPA showed a similar pattern.

CONCLUSION: Baseline fitness may moderate 18-month weight loss, as those with very poor fitness lost less weight compared to those with poor or better fitness levels. Those with poor or better fitness at baseline achieved significantly higher mean levels of MVPA at 18 months compared to those with very poor fitness. Participants with very poor fitness at baseline may require additional exercise support during a BWLP to achieve the high levels of MVPA recommended for weight loss maintenance.

The Impact of Timing of Exercise Initiation on Weight Loss: An 18-Month Randomized Clinical Trial

OBJECTIVE

This study aimed to evaluate the impact of timing of exercise initiation on weight loss within a behavioral weight loss program.

METHODS

Adults with overweight or obesity (N = 170; age 18-55 years; BMI 25-42 kg/m² ; 83.5% women) were enrolled in an 18-month behavioral weight loss program consisting of a reduced-calorie diet, exercise, and group-based support. The standard group (STD) received a supervised exercise program (progressing to 300 min/wk of moderate-intensity aerobic exercise) during months 0 to 6. The sequential group (SEQ) was asked to refrain from changing exercise during months 0 to 6 and received the supervised exercise program during months 7 to 12. On completion of supervised exercise, both groups were instructed to continue 300 min/wk of moderate-intensity exercise for the study duration.

RESULTS

At 6 months, the STD group exhibited greater reductions in body weight (-8.7 ± 0.7 kg) compared with the SEQ group (-6.9 ± 0.6 kg; P = 0.047). Between 6 and 18 months, the STD group regained more weight (2.5 ± 0.8 kg vs. 0.0 ± 0.8 kg; P = 0.02). At 18 months, there were no between-group differences in changes in weight (STD: -6.9 ± 1.2 kg; SEQ: -7.9 ± 1.2 kg), fat mass, lean mass, physical activity, or attrition.

CONCLUSIONS

Both immediate and delayed exercise initiation within a behavioral weight loss program resulted in clinically meaningful weight loss at 18 months. Thus, timing of exercise initiation can be personalized based on patient preference.

Physical Activity Energy Expenditure and Total Daily Energy Expenditure in Successful Weight Loss Maintainers

OBJECTIVE

The objective of this study was to compare physical activity energy expenditure (PAEE) and total daily energy expenditure (TDEE) in successful weight loss maintainers (WLM) with normal weight controls (NC) and controls with overweight/obesity (OC).

METHODS

Participants were recruited in three groups: WLM (n = 25, BMI 24.1 ± 2.3 kg/m² ; maintaining ≥ 13.6-kg weight loss for ≥ 1 year), NC (n = 27, BMI 23.0 ± 2.0 kg/m² ; similar to current BMI of WLM), and OC (n = 28, BMI 34.3 ± 4.8 kg/m² ; similar to pre-weight loss BMI of WLM). TDEE was measured using the doubly labeled water method. Resting energy expenditure (REE) was measured using indirect calorimetry. PAEE was calculated as (TDEE - [0.1 × TDEE] - REE).

RESULTS

PAEE in WLM (812 ± 268 kcal/d, mean ± SD) was significantly higher compared with that in both NC (621 ± 285 kcal/d, P < 0.01) and OC (637 ± 271 kcal/d, P = 0.02). As a result, TDEE in WLM (2,495 ± 366 kcal/d) was higher compared with that in NC (2,195 ± 521 kcal/d, P = 0.01) but was not significantly different from that in OC (2,573 ± 391 kcal/d).

CONCLUSIONS

The high levels of PAEE and TDEE observed in individuals maintaining a substantial weight loss (-26.2 ± 9.8 kg maintained for 9.0 ± 10.2 years) suggest that this group relies on high levels of energy expended in physical activity to remain in energy balance (and avoid weight regain) at a reduced body weight.

No consistent evidence of a disproportionately low resting energy expenditure in long-term successful weight-loss maintainers

Background

Evidence in humans is equivocal in regards to whether resting energy expenditure (REE) decreases to a greater extent than predicted for the loss of body mass with weight loss, and whether this disproportionate decrease in REE persists with weight-loss maintenance.

Objectives

We aimed to1) determine if a lower-than-predicted REE is present in a sample of successful weight-loss maintainers (WLMs) and 2) determine if amount of weight loss or duration of weight-loss maintenance are correlated with a lower-than-predicted REE in WLMs.

Design

Participants (18-65 y old) were recruited in 3 groups: WLMs (maintaining ≥13.6 kg weight loss for ≥1 y, n = 34), normal-weight controls [NCs, body mass index (BMI; in kg/m2) similar to current BMI of WLMs, n = 35], and controls with overweight/obesity (OCs, BMI similar to pre-weight-loss maximum BMI of WLMs, n = 33). REE was measured (REEm) with indirect calorimetry. Predicted REE (REEp) was determined via 1) a best-fit linear regression developed with the use of REEm, age, sex, fat-free mass, and fat mass from our control groups and 2) three standard predictive equations.

Results

REEm in WLMs was accurately predicted by equations developed from NCs and OCs (±1%) and by 3 standard predictive equations (±3%). In WLMs, individual differences between REEm and REEp ranged from -257 to +163 kcal/d. A lower REEm compared with REEp was correlated with amount of weight lost (r = 0.36, P < 0.05) but was not correlated with duration of weight-loss maintenance (r = 0.04, P = 0.81).

Conclusions

We found no consistent evidence of a significantly lower REE than predicted in a sample of long-term WLMs based on predictive equations developed from NCs and OCs as well as 3 standard predictive equations. Results suggest that sustained weight loss may not always result in a substantial, disproportionately low REE. This trial was registered at clinicaltrials.gov as NCT03422380.

Objectively Measured Physical Activity and Sedentary Behavior in Successful Weight Loss Maintainers

OBJECTIVE

The objective of this study was to compare patterns of objectively measured moderate-to-vigorous physical activity (MVPA, ≥ 3.00 metabolic equivalents [METs]), light-intensity physical activity (LPA, 1.50-2.99 METs), and sedentary behavior (SB, < 1.50 METs) in successful weight loss maintainers (WLMs), normal weight controls (NC), and controls with overweight/obesity (OC).

METHODS

Participants (18-65 y) were recruited in three groups: WLM (maintaining ≥ 13.6-kg weight loss for ≥ 1 year, n = 30), NC (BMI matched to current BMI of WLM, n = 33), and OC (BMI matched to pre-weight loss BMI of WLM, n = 27). All participants wore the activPAL for 1 week.

RESULTS

Compared with OC and NC, WLM spent more awake time in total MVPA (WLM: 9.6 ± 3.9%, NC: 7.1 ± 2.1%, OC: 5.9 ± 2.0%; P < 0.01) and more time in sustained (≥ 10 min) bouts of MVPA (WLM: 39 ± 33, NC: 17 ± 14, OC: 9 ± 11 min/d; P < 0.01). Compared with OC, WLM and NC spent more awake time in LPA (WLM: 29.6 ± 7.9%, NC: 29.1 ± 8.3%, OC: 24.8 ± 6.7%; P = 0.04) and less awake time sedentary (WLM: 60.8 ± 9.3%, NC: 63.8 ± 9.5%, OC: 69.3 ± 7.5%; P < 0.01).

CONCLUSIONS

Results provide additional data supporting the important role of MVPA in weight loss maintenance and suggest notable differences in LPA and SB between normal weight individuals and those with overweight/obesity. Increasing LPA and/or decreasing SB may be additional potential targets for weight management interventions.