Predictors Of Weight Change Research Articles

Trajectories of daily antipsychotic use and weight gain in people hospitalized for the first episode of psychosis.

We need to better understand the risk factors and predictors of medication-related weight gain to improve metabolic health of individuals with schizophrenia. This study explores how trajectories of antipsychotic medication (AP) use impact body weight early in the course of schizophrenia. We recruited 92 participants with first-episode psychosis (FEP, n=92) during their first psychiatric hospitalization. We prospectively collected weight, body mass index (BMI), metabolic markers, and exact daily medication exposure during 6-week hospitalization. We quantified the trajectory of AP medication changes and AP polypharmacy using a novel approach based on meta-analytical ranking of medications and tested it as a predictor of weight gain together with traditional risk factors. Most people started treatment with risperidone (n=57), followed by olanzapine (n=29). Then, 48% of individuals remained on their first prescribed medication, while 33% of people remained on monotherapy. Almost half of the individuals (39/92) experienced escalation of medications, mostly switch to AP polypharmacy (90%). Only baseline BMI was a predictor of BMI change. Individuals in the top tercile of weight gain, compared to those in the bottom tercile, showed lower follow-up symptoms, a trend for longer prehospitalization antipsychotic treatment, and greater exposure to metabolically problematic medications. Early in the course of illness, during inpatient treatment, baseline BMI is the strongest and earliest predictor of weight gain on APs and is a better predictor than type of medication, polypharmacy, or medication switches. Baseline BMI predicted weight change over a period of weeks, when other traditional predictors demonstrated a much smaller effect.

Microbiome variations induced by delta9-tetrahydrocannabinol predict weight reduction in obese mice

IntroductionObesity and high-fat diets induce consistent alterations in gut microbiota composition. Observations from epidemiological reviews and experiments also illustrate weight regulation effects of delta9-tetrahydrocannabinol (THC) with microbiome shifts. Therefore, we investigated the weight-loss potential of THC in obese mice models and to elucidate the contribution of specific gut microbiome changes in THC-induced weight loss.MethodsHigh-fat diet induced obese mice were treated with oral THC supplementation for two weeks and compared with controls. In addition to measuring weight, fecal samples were obtained at various timepoints, sequenced for bacterial 16s rRNA content and analyzed using QIIME2. Alpha and beta diversity were computed followed by linear mixed effects (LME) modeling of bacterial relative abundance relationship to THC treatment and weight change.ResultsIn both male and female mice, the THC group had significantly greater average weight loss than controls (−17.8% vs. −0.22%, p<0.001 and −13.8% vs. +2.9%, p<0.001 respectively). Male mice had 8 bacterial taxonomic features that were both significantly different in relative abundance change over time with THC and correlated with weight change. An LME model using three bacterial features explained 76% of the variance in weight change with 24% of variation explained by fixed effects of feature relative abundance alone. The model also accurately predicted weight change in a second male mouse cohort (R=0.64, R2=0.41, p=<0.001). Female mice had fewer significant predictive features and were difficult to model, but the male-produced 3-feature model still accurately predicted weight change in the females (R=0.66, R2=0.44, p<0.001).ConclusionUsing a stepwise feature selection approach, our results indicate that sex-specific gut microbiome composition changes play some role in THC-induced weight loss. Additionally, we illustrated the concept of microbiome feature-based modeling to predict weight changes.

Comparison of Nonlinear Models to Describe the Growth of Tuj and Romanov x Tuj (F1) Lambs

This study aimed to identify the most suitable model for explaining weight changes in purebred Tuj (n=35) and Romanov x Tuj (RoxTuj) (F1) (n=25) lambs using non-linear models. Single-born lambs of both breeds and genders were included in the evaluation. Five different non-linear growth models were compared: Brody, Gompertz, Logistic, Richards, and Weibull. The best model for describing growth was chosen based on four criteria: coefficient of determination (R²), mean square error (MSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Models with the highest R² and the lowest MSE, AIC, and BIC values were considered the best fit for the data. It was observed that the Brody model had the highest R2 and lowest MSE, AIC and BIC values for Tuj and RoxTuj (F1) female and male lambs. The Gompertz, Logistic, and Richards models exhibited similar predictive performance. In contrast, the Weibull model produced significantly different results compared to the other models when predicting weight changes. Therefore, the Brody model was identified as the most effective model for explaining growth patterns in both Tuj and RoxTuj (F1) lambs.

Objectively measured daytime sleepiness predicts weight change among adults: Findings from the Wisconsin Sleep Cohort Study

ObjectiveBody mass index (BMI) trajectories are associated with night-time sleep, but it is not clear how they relate to daytime sleepiness in population data. This study aimed to examine longitudinal associations between levels and changes in daytime sleepiness and BMI trajectories among men and women. MethodsWe estimated growth curve models among 827 participants in the Wisconsin Sleep Cohort Study (mean [sd] age = 55.2 [8.0] years at baseline). The outcome variable was BMI (kg/m2) and the key predictor was daytime sleepiness measured by Multiple Sleep Latency Test (MSLT) scores. Covariates included demographics, health behaviors, retirement status, stimulant use, and depressive symptoms. In sensitivity analyses, we evaluated the potential effects of cardiovascular disease, shift work status, and sleep apnea on the robustness of sleepiness and BMI associations. ResultsAt the between-person level, men who were sleepier had higher BMI levels. At the within-person level, age moderated the positive association between sleepiness and BMI among women. Specifically, young women who became sleepier over time gained more BMI than older women with comparable increases in sleepiness. Furthermore, while BMI tended to increase with age among women, BMI trajectories were steeper among sleepy women than among well-rested women, who experienced less increase in BMI over time. ConclusionThe study suggested that levels and changes in daytime sleepiness as objectively measured by MSLT scores are associated with body mass among adults.

Estimating Changes in Weight and Metabolic Parameters Before and After Treatment With Cariprazine: A Retrospective Study of Electronic Health Records

Weight gain and associated negative cardiometabolic effects can occur as a result of mental illness or treatment with second-generation antipsychotics (SGAs), leading to increased rates of morbidity and mortality. In this analysis, we evaluated the effect of the SGA cariprazine on weight and metabolic parameters in a real-world, retrospective, observational dataset. Electronic health records from the Optum Humedica database (October 1, 2014-December 31, 2020) were analyzed during the 12-month period before starting cariprazine (baseline) and for up to 12 months following cariprazine initiation; approved and off-label indications were included. Body weight trajectories were estimated in the overall patient cohort and at 3-, 6-, and 12-month timepoints (primary objective). Changes in hemoglobin A1c (HbA1c), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides were also evaluated (secondary objectives). Percentages of patients with clinically relevant shifts in body weight, total cholesterol, and fasting triglycerides were also determined. Discontinuation rates for metabolic regulating medications were calculated. Average predicted values were estimated by linear mixed-effects regression models. A total of 2,301 patients were included; average duration of follow-up was 133.7 days. Average predicted weight change for patients during the cariprazine overall follow-up period was +2.4 kg, with predicted weight changes of +0.8 kg (n=811), +1.1 kg (n=350), and +1.4 kg (n=107) at months 3, 6, and 12, respectively. Overall, the majority of patients did not experience clinically significant (≥7%) weight gain (82.8%) or loss (90.5%) after starting cariprazine. Average predicted HbA1c levels (n=189) increased during baseline (0.15%/year) and decreased during cariprazine treatment (-0.2%/year). Average predicted triglyceride levels (n=257) increased during baseline (15.0 mg/dL/year) and decreased during cariprazine treatment (-0.7 mg/dL/year). Predicted LDL (n=247) and HDL (n=255) values decreased during baseline (-7.3 and -1.1 mg/dL/year, respectively); during cariprazine treatment, LDL increased by 5.6 mg/dL/year and HDL decreased by -0.6 mg/dL/year. During follow-up, most patients did not shift from normal/borderline to high total cholesterol (<240 to ≥240 mg/dL; 522 [90.2%]) or fasting triglyceride (<200 to ≥200 mg/dL; 143 [88.8%] patients) levels; shifts from high to normal/borderline levels occurred in 44 (61.1%) patients for total cholesterol and 38 (57.6%) patients for fasting triglycerides. After starting cariprazine, the discontinuation rate per 100 patient-years was 60.4 for antihyperglycemic medication and 87.4 for hyperlipidemia medication. These real-world results support short-term clinical trial findings describing a neutral weight and metabolic profile associated with cariprazine treatment and they expand the dataset to include long-term follow-up.

Mental Health Variables Impact Weight Loss, Especially in Patients with Obesity and Binge Eating: A Mediation Model on the Role of Eating Disorder Pathology.

Various mental health and eating behavior variables have been independently associated with predicting weight loss in individuals with obesity. This study aims to investigate a mediation model that assesses the distinct contributions of these variables in predicting weight changes in patients with obesity following an outpatient behavioral weight loss intervention (BWLI). General mental health (depression, anxiety, stress, impulsivity), eating behavior (cognitive restraint, disinhibition, hunger), eating disorder pathology, and body mass index (BMI) were assessed in a group of 297 patients with obesity at the admission of a BWLI program. BMI was re-evaluated during the final treatment session. A mediation model was employed to examine whether mental health and eating behavior variables predicted BMI changes, with eating disorder pathology serving as a mediator. The model was tested both overall and within two patient subgroups: those with regular binge eating (≥four episodes/month) and those without. In the overall sample (n = 238), the relationships between depression, impulsivity, and cognitive restraint with BMI change were mediated by eating disorder pathology. In the subgroup with regular binge eating (n = 99, 41.6%), the associations between stress and disinhibition with BMI change were additionally mediated by eating disorder pathology. In the subgroup without regular binge eating, eating disorder pathology showed no mediating effect. Multiple mental health and eating behavior variables assessed at admission predicted BMI changes, particularly when mediated by eating disorder pathology in patients with regular binge eating. A comprehensive psychopathological assessment prior to starting BWLI may help identify multiple factors affecting prognosis and treatment outcomes. Long-term follow-up studies in this field are required.

Predictive model for long-term weight recovery after gastrectomy for gastric cancer: an introduction to a web calculator

BackgroundWeight changes after gastrectomy affect not only quality of life but also prognosis and survival. However, it remains challenging to predict the weight changes of individual patients. Using clinicopathological variables, we built a user-friendly tool to predict weight change after curative gastrectomy for gastric cancer.MethodsThe clinical data of 984 patients who underwent curative gastrectomy between 2009 and 2013 were retrospectively reviewed and analyzed. Multivariate logistic regression was performed to identify variables predictive of postoperative weight change. A nomogram was developed and verified via bootstrap resampling.ResultsAge, sex, performance status, body mass index, extent of resection, pathological stage, and postoperative weight change significantly influenced postoperative weight recovery. Postoperative levels of hemoglobin, albumin, ferritin and total iron-binding capacity were significant covariates. The nomogram performed well (concordance index = 0.637); calibration curves indicated appropriate levels of agreement. We developed an online weight prediction calculator based on the nomogram (http://gc-weightchange.com/en/front/).ConclusionsThe novel, Web-calculator based on the predictive model allows surgeons to explore patient weight patterns quickly. The model identifies patients at high risk for weight loss after gastrectomy; such patients require multidisciplinary medical support.

Machine learning approach to predict body weight in adults.

Obesity is an established risk factor for non-communicable diseases such as type 2 diabetes mellitus, hypertension and cardiovascular disease. Thus, weight control is a key factor in the prevention of non-communicable diseases. A simple and quick method to predict weight change over a few years could be helpful for weight management in clinical settings. We examined the ability of a machine learning model that we constructed to predict changes in future body weight over 3 years using big data. Input in the machine learning model were three-year data on 50,000 Japanese persons (32,977 men) aged 19-91 years who underwent annual health examinations. The predictive formulas that used heterogeneous mixture learning technology (HMLT) to predict body weight in the subsequent 3 years were validated for 5,000 persons. The root mean square error (RMSE) was used to evaluate accuracy compared with multiple regression. The machine learning model utilizing HMLT automatically generated five predictive formulas. The influence of lifestyle on body weight was found to be large in people with a high body mass index (BMI) at baseline (BMI ≥29.93 kg/m2) and in young people (<24 years) with a low BMI (BMI <23.44 kg/m2). The RMSE was 1.914 in the validation set which reflects ability comparable to that of the multiple regression model of 1.890 (p = 0.323). The HMLT-based machine learning model could successfully predict weight change over 3 years. Our model could automatically identify groups whose lifestyle profoundly impacted weight loss and factors the influenced body weight change in individuals. Although this model must be validated in other populations, including other ethnic groups, before being widely implemented in global clinical settings, results suggested that this machine learning model could contribute to individualized weight management.

Depressive symptoms and weight change in inpatients with anorexia nervosa: A cross-lagged panel model

ObjectiveAnorexia nervosa (AN) is marked by a high rate of comorbid depression, which raises the question whether depressive symptoms may adversely affect treatment outcome. Thus, we examined whether depressive symptoms at admission would predict weight change from admission to discharge in a large sample of inpatients with AN. In addition, we also explored the reverse direction, that is, whether body mass index (BMI) at admission would predict changes in depressive symptoms. MethodsA sample of 3011 adolescents and adults with AN (4% male) who received inpatient treatment at four Schoen Clinics was analyzed. Depressive symptoms were measured with the Patient Health Questionnaire–9. ResultsBMI significantly increased and depressive symptoms significantly decreased from admission to discharge. BMI and depressive symptoms were unrelated at admission and discharge. Higher BMI at admission predicted smaller decreases in depressive symptoms and higher depressive symptoms at admission predicted larger weight gain. The latter effect, however, was mediated by longer length of stay. ConclusionResults indicate that depressive symptoms do not adversely affect weight gain during inpatient treatment in persons with AN. Instead, higher BMI at admission is predictive of smaller improvements in depressive symptoms but this effect seems to be negligible in terms of clinical relevance.

Impulsivity and reward sensitivity facets as predictors of weight change in children: Differences by binge-eating disorder diagnostic status.

Impulsivity and reward sensitivity are personality traits associated with obesity and binge-eating disorder (BED), but little research has examined prospective associations between these traits and body mass index z-score (BMI-z) differentially for children with and without BED. Using data from the Adolescent Brain and Cognitive Development study, the present analysis examined predictive associations between impulsivity and reward sensitivity facets with BMI-z 1 year later in children with versus without BED. Nine-to-ten-year-old children were recruited across the United States and completed self-report questionnaires and clinical interviews at baseline, and height and weight were taken at baseline and 1-year follow-up. Multivariable models were run with baseline impulsivity and reward sensitivity facets predicting 1-year BMI-z in children with versus without BED. Reward sensitivity and impulsivity were unrelated to BMI-z at follow-up in children without BED. Greater negative urgency (i.e., proclivity to act rashly without forethought in response to negative emotions) and lower positive urgency (i.e., proclivity to act rashly without forethought due to positive emotions) predicted increased BMI-z at 1-year follow-up in children with BED subsample. Findings indicate the importance of developing theoretical models and interventions for obesity prevention in children with BED that address emotional impulsivity.

Persistent, High Levels of Social Jetlag Predict Poor Weight Outcomes in a Weight Gain Prevention Study for Young adults.

Social jetlag (SJL), the discrepancy in sleep timing between weekdays and weekends, is associated with higher BMI and cardiometabolic risk and is common in young adults. We examined whether chronic SJL impacts weight gain in young adults participating in a weight gain prevention trial. Young adults (n = 599, age 18-35; BMI: 21.0-30.9kg/m2) completed assessments at 0, 4, 12, and 24 months. Multilevel mixed growth models were used to examine (1) associations between demographics and longitudinal SJL and (2) longitudinal SJL as a predictor of weight change and cardiometabolic outcomes. SJL was assessed as a continuous and clinically-significant dichotomous (< vs. ≥2h) variable. 38% of participants had clinically-significant SJL at ≥ 1 timepoints (Baseline M ± SD = 1.3±0.89). Younger (b=-0.05, p < 0.001), female (b = 0.18, p = 0.037) and Black (compared to White, b = 0.23, p = 0.045) participants were more likely to have greater SJL. Individuals with high SJL (≥ 2h; between-person effect) were more likely to have greater weight gain over 2 years (b = 0.05, p = 0.028). High SJL did not affect the rate of change in waist circumference or cardiometabolic markers over time. High SJL is associated with greater weight gain over time. Reducing SJL may positively impact weight status in young adults.

Lifestyle behaviors, metabolic disturbances, and weight gain in psychiatric inpatients treated with weight gain-associated medication

Many psychiatric patients suffer from overweight/obesity and subsequent metabolic disturbances, where psychotropic medication is one of the main contributors. However, the magnitude of weight gain ranges individually, which leads to questioning the role of other contributors like lifestyle factors. The present study investigated several lifestyle factors among psychiatric inpatients, their relation to biological factors, and their predictive capability for weight gain during treatment. Using a naturalistic observational study design, psychiatric inpatients of all diagnoses were followed for 4 weeks from the start of treatment with weight gain-associated medication. N = 163 participants entered the study. Lifestyle factors were assessed by patient self-report questionnaires. Body weight change over time was calculated relative to baseline body weight. Our study provides three main findings: (1) Obesity and/or metabolic syndrome (metSy) were associated with emotional eating (disinhibition), craving for fast food and sweets, and weight cycling. (2) Patients without metSy and normal BMI experienced increased sweets craving (also for women), a more positive attitude towards drugs, and an improvement of affect (also for men). (3) Sex, presence of metSy and/or drug dosage interacted with disinhibition change, sweets craving change (trend), and fast food craving change to predict weight change over time. Furthermore, drug attitude change interacted with BMI, drug dosage, and presence of metSy to predict weight change. Lifestyle factors, especially eating behaviors, are related to metabolic disturbances and predict weight gain in interaction with clinical parameters.

Patterns and Predictors of Weight Change Before and After Total Hip Arthroplasty in Class 2 and 3 Obese Patients.

This study aimed to better understand body mass index (BMI) change patterns and factors associated with BMI change before and after total hip arthroplasty (THA) in Class 2 and 3 obese patients, and assess if preoperative or postoperative BMI change affects postoperative clinical outcomes. We retrospectively reviewed World Health Organization Class 2 and 3 obese patients (BMI > 35.0 at surgery) who underwent THA at a tertiary medical center from 2010 to 2020. BMI was recorded at 1 year preoperatively (mean 11.6 months), and at most recent postoperative visit (mean 29.0 months). Baseline demographics and postoperative clinical outcomes were recorded. We reviewed 436 THAs with a mean age of 59.9 (11.5) years. Leading up to surgery 55.5% had unchanged BMI, and postoperatively 48.2% had unchanged BMI. Multivariate logistic regression revealed that those who lost BMI preoperatively were more likely to gain BMI postoperatively (odds ratio [OR] 3.28, confidence interval [CI] 1.83-5.97, P= .005), but those who gained >5% BMI preoperatively had no association with BMI change postoperatively. Those in a higher BMI class preoperatively were less likely to gain BMI preoperatively (Class 3 obese patients: OR 0.001, CI 0.0002-0.004, P < .001). African American patients were more likely to gain BMI preoperatively (OR 2.32, CI 1.16-4.66, P= .017). We did not detect an association between BMI change and postoperative clinical outcomes. In World Health Organization Class 2 or 3 obese patients, most maintained BMI between their first preoperative and final postoperative visit. Preoperatively, Class 3 obese patients were less likely to gain weight than Class 2 obese patients. The primary predictor of postoperative weight gain was preoperative weight loss. Weight change preoperatively and postoperatively were not associated with worse clinical outcomes.

Psychosocial and behavioral correlates of weight loss 12 to 15 years after bariatric surgery.

While significant weight loss occurs post-bariatric surgery, partial weight regain is common. Psychological and dispositional variables have been examined as predictors of weight change, but most studies have focused on the relationship of preoperative constructs to shorter-term postoperative outcomes. The goal of the current study was to examine associations between weight loss and postoperative psychosocial and behavioral factors up to an average of 13.7years after surgery. The current study was conducted at a large bariatric center in a Midwestern U.S. city. The sample was comprised of 125 adult patients who participated in the second wave of a long-term bariatric surgery outcome study, examining weight history, physical activity, and psychological health and functioning. Correlations between percent total weight loss (%TWL) and psychosocial and behavioral variables were examined. The variables that had significant correlations with %TWL were used in stepwise linear regressions to determine their contribution to %TWL. These same variables were tested to determine differences among those in the highest and lowest weight loss quartiles. Life satisfaction, conscientiousness, positive affect, and regular exercise were positively associated with weight loss in the entire sample and were significantly higher among those in the highest versus the lowest weight-loss quartile. Experiencing a stressful event and food addiction symptoms were negatively associated with weight loss. Positive affect, fewer food addiction symptoms, and regular exercise significantly predicted weight loss, accounting for 23% of the variance in %TWL. Long-term weight loss maintenance after bariatric surgery may be related to positive affect, conscientiousness, regular physical activity, and an addictive-type relationship with food. Future studies should explore these relationships and develop approaches to deal with the interaction between dispositional tendencies and lifestyle factors.

Obese patients' characteristics and weight loss outcomes in cardiac rehabilitation: An observational study of registry data

AimCardiac rehabilitation (CR) guidelines advocate weight loss for obese patients but mean weight loss is small. We sought to determine the extent to which obese patients' characteristics prior to CR predict weight loss. MethodsAn observational, pre- and post CR study of routine practice using the UK National Audit of Cardiac Rehabilitation dataset was undertaken. Backward, stepwise, multiple linear regression analysis was used to identify characteristics prior to CR that predicted weight change in obese patients. ResultsIn 29,601 obese patients undertaking CR, mean weight loss was 0.9 kg (SD 4.3; p < 0.001) in men (74% of sample) and 0.5 kg (SD 3.9; p < 0.001) in women. Smoking cessation since the cardiac event independently predicted less weight loss by 1.2 kg (95% CI; 0.9, 1.5 kg; p < 0.001). Diabetes, cardiac surgery, living in a deprived area, being female, low fitness levels and pain independently predicted less weight loss during CR. Higher initial weight, greater age and being employed predicted increased weight loss. ConclusionThis is the first study to identify how the characteristics of obese patients independently predict different amounts of weight loss during CR in free-living individuals. It is also the largest, registry-based study to investigate predictors of weight loss in obese patients in CR. Knowledge of the extent to which obese patients' characteristics predict more or less weight loss can aid: the generation of guidelines; agreement of realistic goals with patients; and tailoring of weight management support.

Energy intake as a short-term biomarker for weight loss in adults with obesity receiving liraglutide: A randomized trial.

Background and ObjectiveObesity is a chronic disease associated with many serious comorbidities. Pharmacologic therapies are approved for the treatment of obesity; however, short‐term biomarkers to predict weight loss are not well understood. This study aimed to determine the ability of single‐meal energy intake (EI) to predict weight loss in participants with obesity treated with liraglutide.MethodsIn this randomized, double‐blind, placebo‐controlled study, participants received subcutaneous liraglutide (titrated to 3.0 mg/day) or placebo once daily, with inpatient assessments at baseline and weeks 3 and 6. The primary endpoint was change from baseline (CFB) in EI during consecutive ad libitum lunch meals at weeks 3 and 6. Secondary endpoints included CFB in 24‐ and 48‐h EI, weight, appetite scores, and gastric emptying measures.ResultsSixty‐one participants were randomized (n = 32, liraglutide; n = 29, placebo). The least squares mean (LSM) difference (95% CI; p‐value) in CFB in EI during ad libitum lunch meals between the liraglutide and placebo groups was −236 (−322, −149; p < 0.0001) kcal at week 3 and –244 (−339, −148, p < 0.0001) kcal at week 6. The liraglutide group experienced significant weight loss at weeks 3 and 6, compared with placebo. Weight loss was significantly correlated with EI, but not with appetite score or gastric emptying.ConclusionsEI during a single meal is a robust clinical predictor of weight changes in participants with obesity. Future clinical trials can utilize EI at a single meal as a predictor of weight loss.

Reward-related gustatory and psychometric predictors of weight loss following bariatric surgery: a multicenter cohort study

ABSTRACTBackgroundReward sensitivity has been proposed as a potential mediator of outcomes for bariatric surgery.ObjectivesWe aimed to determine whether gustatory and psychometric measures of reward-related feeding are predictors of bariatric-induced weight loss.MethodsA multicenter longitudinal cohort study was conducted in patients scheduled for bariatric surgery (surgical group), assessed at baseline and 2 follow-up assessments. Predictions of % weight loss from baseline (%WL) according to baseline gustatory measures, including intensity and pleasantness ratings of sweet and other tastants, and psychometric measures of reward-related feeding behavior, including hedonic hunger scores, were assessed with multivariable linear regression. Exploratory analyses were conducted to test for associations between %WL and changes in gustatory and psychophysical measures, as well as for comparisons with data from patients on the surgery waiting list (control group).ResultsWe included 212 patients, of whom 96 in the surgical group and 50 in the control group were prospectively assessed. The groups were similar at baseline and, as expected, bariatric surgery resulted in higher %WL (BTreatment-Time = 2.4; 95% CI: 2.1–2.8; P < 0.0001). While variation in gustatory measures did not differ between groups, in the surgery group baseline sweet intensity predicted %WL at the primary endpoint (11 to 18 months postoperatively; β = 0.2; B = 0.2, 95% CI: 0.02 to 0.3; P = 0.02), as did hedonic hunger scores (β = −0.2; B = −2.0, 95% CI: −3.8 to −0.3; P = 0.02). Furthermore, at this endpoint, postsurgical reduction of sweet taste intensity and acceptance of sweet foods were associated with %WL (β = −0.3; B = −3.5, 95% CI: −5.8 to −1.3; P = 0.003, and β = −0.2; B = −4.7, 95% CI: −8.5 to −0.8; P = 0.02, respectively). The use of sweet intensity as a predictor of weight change was confirmed in another bariatric cohort.ConclusionsSweet intensity ratings and hedonic hunger scores predict %WL after surgery. The variability of sweet intensity ratings is also associated with %WL, further suggesting they may reflect physiological processes that are variably modulated by bariatric surgery, influencing clinical outcomes.

Design of a randomised controlled trial: does indirect calorimetry energy information influence weight loss in obesity?

IntroductionRespiratory quotient (RQ) provides an indication of the relative balance of carbohydrate and fat oxidation. RQ could serve as an early biomarker of negative energy balance during weight loss. Restriction...

Resting-state connectivity within the brain's reward system predicts weight loss and correlates with leptin.

Weight gain is often associated with the pleasure of eating food rich in calories. This idea is based on the findings that people with obesity showed increased neural activity in the reward and motivation systems of the brain in response to food cues. Such correlations, however, overlook the possibility that obesity may be associated with a metabolic state that impacts the functioning of reward and motivation systems, which in turn could be linked to reactivity to food and eating behaviour and weight gain. In a study involving 44 female participants [14 patients with obesity, aged 20–63 years (mean: 42, SEM: 3.2 years), and 30 matched lean controls, aged 22–60 years (mean: 37, SEM: 1.8 years)], we investigated how ventromedial prefrontal cortex seed-to-voxel resting-state connectivity distinguished between lean and obese participants at baseline. We used the results of this first step of our analyses to examine whether changes in ventromedial prefrontal cortex resting-state connectivity over 8 months could formally predict weight gain or loss. It is important to note that participants with obesity underwent bariatric surgery at the beginning of our investigation period. We found that ventromedial prefrontal cortex–ventral striatum resting-state connectivity and ventromedial–dorsolateral prefrontal cortex resting-state connectivity were sensitive to obesity at baseline. However, only the ventromedial prefrontal cortex–ventral striatum resting-state connectivity predicted weight changes over time using cross-validation, out-of-sample prediction analysis. Such an out-of-sample prediction analysis uses the data of all participants of a training set to predict the actually observed data in one independent participant in the hold-out validation sample and is then repeated for all participants. In seeking to explain the reason why ventromedial pre-frontal cortex–ventral striatum resting-state connectivity as the central hub of the brain’s reward and motivational system may predict weight change over time, we linked weight loss surgery-induced changes in ventromedial prefrontal cortex–ventral striatum resting-state connectivity to surgery-induced changes in homeostatic hormone regulation. More specifically, we focussed on changes in fasting state systemic leptin, a homeostatic hormone signalling satiety, and inhibiting reward-related dopamine signalling. We found that the surgery-induced increase in ventromedial prefrontal cortex–ventral striatum resting-state connectivity was correlated with a decrease in fasting-state systemic leptin. These findings establish the first link between individual differences in brain connectivity in reward circuits in a more tonic state at rest, weight change over time and homeostatic hormone regulation.

Bi-Directional Associations Between Real-Time Affect and Physical Activity in Weight-Discordant Siblings.

This study examined bi-directional associations between affect and physical activity (i.e., moderate-to-vigorous physical activity [MVPA] and total activity counts), and the extent to which weight (body mass index z-score [z-BMI]) moderated these associations. Participants were drawn from a prior study of siblings (N = 77; mean age = 15.4 ± 1.4 years) discordant for weight status (39 nonoverweight siblings, 38 siblings with overweight/obesity) who completed ecological momentary assessment (EMA) with accelerometer-assessed physical activity. Generalized linear mixed models indicated z-BMI moderated trait-level and momentary associations. When adolescents with higher z-BMI reported momentary negative affect, they evidenced less MVPA within the next hour. Across the sample, greater overall activity was associated with lower negative affect. However, at the momentary level, when adolescents with higher (but not lower) z-BMI evidenced greater activity, they reported decreases in negative affect. Findings indicate affective experiences surrounding physical activity differ according to z-BMI. Specifically, momentary negative affect may impede momentary MVPA among youth with higher z-BMI. Further research is warranted to elucidate factors influencing these momentary associations and the extent to which these momentary associations prospectively predict weight change.