Combining biological and psychosocial baseline variables did not improve prediction of outcome of a very-low-energy diet in a clinic referral population

Development and validation of a weight-loss predictor to assist weight loss management

This study aims to develop and validate a modeling framework to predict long-term weight change on the basis of self-reported weight data. The aim is to enable focusing resources of health systems on individuals that are at risk of not achieving their goals in weight loss interventions, which would help both health professionals and the individuals in weight loss management. The weight loss prediction models were built on 327 participants, aged 21-78, from a Finnish weight coaching cohort, with at least 9 months of self-reported follow-up weight data during weight loss intervention. With these data, we used six machine learning methods to predict weight loss after 9 months and selected the best performing models for implementation as modeling framework. We trained the models to predict either three classes of weight change (weight loss, insufficient weight loss, weight gain) or five classes (high/moderate/insufficient weight loss, high/low weight gain). Finally, the prediction accuracy was validated with an independent cohort of overweight UK adults (n = 184). Of the six tested modeling approaches, logistic regression performed the best. Most three-class prediction models achieved prediction accuracy of > 50% already with half a month of data and up to 97% with 8 months. The five-class prediction models achieved accuracies from 39% (0.5 months) to 89% (8 months). Our approach provides an accurate prediction method for long-term weight loss, with potential for easier and more efficient management of weight loss interventions in the future. A web application is available: https://elolab.shinyapps.io/WeightChangePredictor/ .The trial is registered at clinicaltrials.gov/ct2/show/NCT04019249 (Clinical Trials Identifier NCT04019249), first posted on 15/07/2019.

Sex/Gender Differences in Obesity Prevalence, Comorbidities, and Treatment

PURPOSE OF REVIEW

Obesity is a heterogeneous condition, yet sex/gender is rarely considered in the prevention or clinical care of this disease. This review examined and evaluated recent literature regarding the influence of sex and gender on obesity prevalence, comorbidities, and treatment in adults.

RECENT FINDINGS

Obesity is more prevalent in women than men in most countries, but in some countries and population subgroups, this gap is more pronounced. Several obesity-related comorbidities, including type 2 diabetes and hypertension, demonstrate sex-specific pathways. Women, compared to men, are more likely to be diagnosed with obesity and seek and obtain all types of obesity treatment including behavioral, pharmacological, and bariatric surgery. Men tend to have greater absolute weight loss, but this difference is attenuated once accounting for baseline weight. Obesity is a multifactorial condition with complex interactions among sex/gender, sociocultural, environmental, and physiological factors. More sex/gender research is needed to investigate mechanisms underlying sex/gender differences in prevalence, comorbidities, and treatment, identify ways to increase men's interest and participation in obesity treatment, and examine differences in obesity prevalence and treatments for transgender and gender non-conforming individuals.

Predictors of weight loss in obese patients with obstructive sleep apnea

PURPOSE

Consistent predictors of weight loss outcomes with very low-energy diets (VLEDs) in obstructive sleep apnea (OSA) have not been identified. This study aimed to identify variables predictive of weight loss success in obese patients with OSA undertaking an intensive weight loss programme.

METHODS

We analysed biological, psychological, and behavioural variables as potential predictors of weight loss in obese patients with OSA after a 2-month VLED followed by one of two 10-month weight loss maintenance diets. Actigraphy, in-lab polysomnography, urinary catecholamines, and various psychological and behavioural variables were measured at baseline, 2, and 12 months. Spearman's correlations analysed baseline variables with 2-month weight loss, and 2-month variables with 2-12 month-weight change.

RESULTS

Forty-two patients completed the VLED and thirty-eight completed the maintenance diets. Actigraphy data revealed that late bedtime (r_s =  - 0.45, p =  < 0.01) was correlated with 2-month weight loss. The change in the time that participants got out of bed (rise-time) from baseline to two months was also correlated with 2-month weight loss (r_s = 0.36, p = 0.03). The Impact of Weight on Quality of Life-Lite questionnaire (IWQOL) Public Distress domain (r_s =  - 0.54, p =  < 0.01) and total (r_s =  - 0.38, p = 0.02) scores were correlated with weight loss maintenance from 2 to 12 months.

CONCLUSIONS

Results from this small patient sample reveal correlations between actigraphy characteristics and weight loss in obese patients with OSA. We suggest the IWQOL may also be a useful clinical tool to identify OSA patients at risk of weight regain after initial weight loss.

CLINICAL TRIAL REGISTRATION

This clinical trial was prospectively registered on 18/02/2013 with the Australia and New Zealand Clinical Trials Registry (ACTRN12613000191796).

PUBLIC REGISTRY TITLE

Sleep, Lifestyle, Energy, Eating, Exercise Program for the management of sleep apnea patients indicated for weight loss treatment: A randomised, controlled pilot study. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=363680.

Baseline Metabolic Variables Do Not Predict Weight Regain in Premenopausal Women

OBJECTIVE

The aim of this study was to investigate whether baseline (pre-weight loss) metabolic variables can predict weight regain.

METHODS

About 117 women with overweight completed a weight loss program to achieve BMI < 25 kg/m2 and were followed for 2 years. Resting metabolic rate, respiratory quotient, insulin sensitivity, and serum leptin concentration were measured pre-weight loss, while on energy balance, and as predictors of weight regain at 1 and 2 years. Rate and amount of weight loss also were examined as predictors, as these outcomes may reflect metabolic phenotype.

RESULTS

Average weight loss was 12 (SD 2.5) kg, and regain was 48% (SD 35%) and 80% (SD 52%) at 1 and 2 years, respectively. In regression modeling, metabolic variables (both pre-weight loss and changes with weight loss) did not predict weight regain. However, initial weight loss and time to achieve BMI < 25 were significant predictors of weight regain at 1 and 2 years, even after adjusting for confounders.

CONCLUSIONS

Baseline (pre-weight loss) resting metabolic rate, respiratory quotient, insulin sensitivity, and leptin did not predict weight regain. However, a larger and faster weight loss was associated with a lower weight regain. Understanding the mechanisms behind interindividual variation in magnitude and rate of weight loss is needed to ensure better weight loss maintenance.

Baseline Psychosocial and Demographic Factors Associated with Study Attrition and 12-Month Weight Gain in the DIETFITS Trial

OBJECTIVE

The purpose of this study was to examine correlates of failure-trial attrition and weight gain-in a randomized clinical weight-loss trial.

METHODS

The Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) trial included 609 adults (18-50 years; BMI 28-40). Participants were randomized to a 12-month healthy low-fat or healthy low-carbohydrate diet for weight loss. At baseline, participants completed psychosocial, demographic, and anthropometric measures. Stepwise logistic regressions identified baseline factors associated with (1) study attrition and (2) among trial completers, weight gain at 12 months.

RESULTS

Having higher baseline food addiction and self-efficacy was linked to treatment failure. Being younger, not having a college education, having higher outcome expectations and quality of life, and having lower social functioning and self-control increased the odds of trial attrition. Identifying as other than non-Hispanic white; not being married or cohabitating; having higher cognitive restraint and self-control; and having lower amotivation, family encouragement, and physical limitations increased the odds of gaining weight by treatment's end.

CONCLUSIONS

Participants' baseline psychosocial and demographic factors may support or impede successful weight loss. Trialists should attend to these factors when designing treatments in order to promote participants' likelihood of completing the trial and achieving their weight-loss goals.

Mechanisms of weight regain after weight loss - the role of adipose tissue

One of the biggest challenges in the management of obesity is the prevention of weight regain after successful weight loss. Weight regain after weight loss has large interindividual variation. Although many factors probably contribute to this variation, we hypothesize that variability in biological responses associated with weight loss-induced shrinking of subcutaneous adipocytes has an important role. In this Review, we show that weight loss-induced variations in cellular stress, extracellular matrix remodelling, inflammatory responses, adipokine secretion and lipolysis seem to be associated with the amount of weight that is regained after successful weight loss. Weight regain could therefore, at least in part, depend on a combination of these factors. Further research on the causality of these associations could aid the development of effective strategies to prevent weight regain after successful weight loss.