Allison Martin Nguyen

The acyclic CB1R inverse agonist taranabant mediates weight loss by increasing energy expenditure and decreasing caloric intake

Cannabinoid 1 receptor (CB1R) inverse agonists are emerging as a potential obesity therapy. However, the physiological mechanisms by which these agents modulate human energy balance are incompletely elucidated. Here, we describe a comprehensive clinical research study of taranabant, a structurally novel acyclic CB1R inverse agonist. Positron emission tomography imaging using the selective CB1R tracer [(18)F]MK-9470 confirmed central nervous system receptor occupancy levels ( approximately 10%-40%) associated with energy balance/weight-loss effects in animals. In a 12-week weight-loss study, taranabant induced statistically significant weight loss compared to placebo in obese subjects over the entire range of evaluated doses (0.5, 2, 4, and 6 mg once per day) (p < 0.001). Taranabant treatment was associated with dose-related increased incidence of clinical adverse events, including mild to moderate gastrointestinal and psychiatric effects. Mechanism-of-action studies suggest that engagement of the CB1R by taranabant leads to weight loss by reducing food intake and increasing energy expenditure and fat oxidation.

Evidence for prospective associations among depression and obesity in population-based studies

Obesity may lead to depression or be one of its consequences. We reviewed population‐based studies in order to, first, identify the most commonly used research methods, and, second, to evaluate the strength of evidence for prospective associations among obesity and depression. We examined 25 studies, of which 10 tested ‘obesity‐to‐depression’ pathways, and 15 tested ‘depression‐to‐obesity’ pathways. Descriptive statistics summarized the frequency with which various measurements, designs and data analytic strategies were used. We tallied the number of studies that reported any vs. no statistically significant associations, and report on effect sizes, identified moderating variables within reports, and sought common findings across studies. Results indicated considerable methodological heterogeneity in the literature. Depression was assessed by clinical interview in 44% of studies, weight and height were directly measured in 32%, and only 12% used both. In total, 80% of the studies reported significant obesity‐to‐depression associations, with odds ratios generally in the range of 1.0 to 2.0, while only 53% of the studies reported significant depression‐to‐obesity associations. Sex was a common moderating variable. Thus, there was good evidence that obesity is prospectively associated with increased depression, with less consistent evidence that depression leads to obesity. Recommendations for future research regarding study samples, measurement and data analysis are provided.

Predictors of Attrition and Weight Loss Success: Results from a Randomized Controlled Trial

Attrition is a common problem in weight loss trials. The present analysis examined several baseline and early-treatment process variables, as predictors of attrition and outcome in a clinical trial that combined pharmacotherapy and behavior therapy for weight loss. Participants were 224 obese adults who were treated with sibutramine alone, lifestyle modification alone, combined therapy, or sibutramine plus brief lifestyle modification. Predictors included baseline characteristics (e.g., demographic, weight-related, psychological, and consumption-related variables), plus attendance, adherence, and weight loss in the early weeks of treatment. Outcomes were attrition and weight loss success (i.e., >or=5% reduction in body weight) at 1 year. Multivariable models, adjusting for other relevant variables, found that younger age and greater baseline depressive symptoms were related to increased odds of attrition (ps <or= 0.003). Greater early weight loss marginally reduced the odds of attrition (p = 0.06). Predictors of weight loss success at 1 year were Caucasian ethnicity (p = 0.04), lower baseline depressive symptoms (p = 0.04), and weight loss during the first 3 weeks of treatment (p < 0.001). Thus, depressive symptoms at baseline were a significant predictor of both attrition and weight loss success. As a process variable, early weight loss appears to have more predictive value than early attendance at treatment sessions or early adherence.

Intentional weight loss and changes in symptoms of depression: a systematic review and meta-analysis

Objective:Obesity is related to increased risk of several health complications, including depression. Many studies have reported improvements in mood with weight loss, but results have been equivocal. The present meta-analysis examined changes in symptoms of depression that were reported in trials of weight loss interventions. Between-groups comparisons of different weight loss methods (for example, lifestyle modification, diet-alone and pharmacotherapy) were examined, as were within-group changes for each treatment type.Method:MEDLINE was searched for articles published between 1950 and January 2009. Several obesity-related terms were intersected with terms related to depression. Results were filtered to return only studies of human subjects, published in English. Of 5971 articles, 394 were randomized controlled trials. Articles were excluded if they did not report mean changes in weight or symptoms of depression, included children or persons with psychiatric disorders (other than depression), or provided insufficient data for analysis. Thirty-one studies (n=7937) were included. Two authors independently extracted a description of each study treatment, sample characteristics, assessment methods and changes in weight and symptoms of depression. Treatments were categorized as lifestyle modification, non-dieting, dietary counseling, diet-alone, exercise-alone, pharmacotherapy, placebo or control interventions.Results:Random effects models found that lifestyle modification was superior to control and non-dieting interventions for reducing symptoms of depression, and marginally better than dietary counseling and exercise-alone programs. Exercise-alone programs were superior to controls. No differences were found for comparisons of pharmacologic agents and placebos. Within-group analyses found significant reductions in symptoms of depression for nearly all active interventions. A meta-regression found no relationship between changes in weight and changes in symptoms of depression in lifestyle modification interventions.Conclusions:On average, obese individuals in weight loss trials experienced reductions in symptoms of depression. Future studies should examine incidence and resolution of clinically significant depressive disorders with weight loss interventions.

Performance-Based or Self-Report Measures of Physical Function : Which Should Be Used in Clinical Trials of Hip Fracture Patients?

OBJECTIVES To assess the validity, sensitivity to change, and responsiveness of 3 self-report and 4 performance-based measures of physical function: activity measure for postacute care (AM-PAC) Physical Mobility and Personal Care scales, the Medical Outcomes Study 36-Item Short Form Health Survey Physical Function scale (SF-36 PF), the Physical Functional Performance test (PFP-10), the Short Physical Performance Battery (SPPB), a 4-meter gait speed, and the six-minute walk test (6MWT). DESIGN A prospective observational study of patients after a hip fracture. Assessments were performed at baseline and 12 weeks postenrollment. SETTING Inpatient and outpatient rehabilitation facilities in Norway, the United Kingdom, Sweden, Israel, Germany, the United States, Denmark, and Spain. PARTICIPANTS A sample of study participants (N=108) who had a hip fracture. INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES Assessments of validity (known-groups, concurrent, construct, and predictive), sensitivity to change (effect size, standardized response mean [SRM], SE of measure, minimal detectable change (MDC), and responsiveness (optimal operating cut-points and area under the curve) between baseline and 12-week follow-up. RESULTS All physical function measures achieved comparably acceptable levels of validity. Odds ratios in predicting patient Global Assessment of Improvement at 12 weeks were as follows: AM-PAC Physical Mobility scale, 5.3; AM-PAC Personal Care scale, 3.6; SF-36 PF, 4.3; SPPB, 2.0; PFP-10, 2.5; gait speed, 1.9; and 6MWT, 2.4. Effect sizes and SRM exceeded 1 SD for all 7 measures. Percent of patients who exceeded the MDC(90) at week 12 were as follows: AM-PAC Physical Mobility scale, 90%; AM-PAC Personal Care scale, 74%; SF-36 PF, 66%; SPPB, 36%; PFP-10, 75%; gait speed, 69%; and 6MWT, 75%. When evaluating responsiveness using the area under receiver operating curves for each measure, all measures had acceptable responsiveness, and no pattern emerged of superior responsiveness depending on the type of measure used. CONCLUSIONS Findings reveal that the validity, sensitivity, and responsiveness of self-report measures of physical function are comparable to performance-based measures in a sample of patients followed after fracturing a hip. From a psychometric perspective, either type of functional measure would be suitable for use in clinical trials where improvement in function is an endpoint of interest. The selection of the most appropriate type of functional measure as the primary endpoint for a clinical trial will depend on other factors, such as the measures feasibility or the strength of the association between the hypothesized mechanism of action of the study intervention and a functional outcome measure.

One-year health-related quality of life outcomes in weight loss trial participants: comparison of three measures

BackgroundThe literature on changes in health-related quality of life (HRQOL) in weight loss studies is inconsistent, and few studies use more than one type of measure. The purpose of the current study was to compare one-year changes in HRQOL as a function of weight change using three different measures: a weight-related measure (Impact of Weight on Quality of Life-Lite [IWQOL-Lite)]) and two generic measures (SF-36; EQ-5D).MethodsData were obtained from 926 participants (mean Body Mass Index (BMI) (kg/m2) = 35.4; 84% female; mean age = 49.5 years) in a placebo-controlled randomized trial for weight loss. At baseline and one-year, participants completed all three HRQOL measures. HRQOL was compared across weight change categories (≥ 5% and 0–4.9% gain, 0–4.9%, 5.0–9.9% and ≥ 10% loss), using effect sizes.ResultsThe weight-related measure of HRQOL exhibited greater improvements with one-year weight loss than either of the generic instruments, with effect sizes ranging from 0.24 to 0.62 for 5–9.9% weight reductions and 0.44 to 0.95 for ≥ 10% reductions. IWQOL-Lite Self-Esteem also showed a small improvement with weight gain. Changes in the two generic measures of HRQOL were inconsistent with each other, and in the case of the SF-36, variable across domains. For participants gaining ≥ 5% of weight, the greatest reductions in HRQOL occurred with respect to SF-36 Mental Health, MCS, and Vitality, with effect sizes of -0.82, -0.70, and -0.63 respectively.ConclusionThis study found differences between weight-related and generic measures of health-related quality of life in a one-year weight loss trial, reflecting the potential value of using more than one measure in a trial. Although weight loss was generally associated with improved IWQOL-Lite, physical SF-36 subscale and EQ-5D scores, a small amount of weight gain was associated with a slight improvement on weight-specific HRQOL and almost no change on the EQ-5D, suggesting the need for further research to more fully study these relationships. We believe our findings have relevance for weight loss patients and obesity clinicians/researchers in informing them of likely HRQOL outcomes associated with varying amounts of weight loss or gain.

Attrition from Randomized Controlled Trials of Pharmacological Weight Loss Agents: A Systematic Review and Analysis

Clinical trials of obesity treatments have been limited by substantial dropout. Participant‐level variables do not reliably predict attrition, and study‐level variables have not yet been examined. We searched MEDLINE and identified 24 large randomized controlled trials of weight loss medications. These trials were comprised of 23 placebo and 32 drug groups. Two authors independently extracted the following for each treatment group: (i) treatment received; (ii) design characteristics (inclusion of a lead‐in period, selection of participants with weight‐related comorbidities, study location and number of study visits); (iii) sample characteristics (sample size, % female, and mean baseline age and body mass index); and (iv) attrition (total, adverse event [AE]‐related and non‐AE‐related) at 1 year. The primary outcome was total attrition, which was significantly related to treatment (i.e. 34.9%, 28.6%, 28.3% and 35.1% in placebo, orlistat, sibutramine and rimonabant groups, respectively, P < 0.0001). In adjusted multivariable models, total attrition was significantly lower in groups that completed a pre‐randomization lead‐in period than in those that did not (29.1% vs. 39.9%, P < 0.01). Gender also was significantly related to total attrition; groups with more women had higher dropout (P < 0.01). The pattern was similar for predicting non‐AE‐related attrition. Findings suggest ways to design studies that maximize retention.

Voluntary weight loss: systematic review of early phase body composition changes

Weight loss follows when adult humans enter a phase of negative energy balance brought about by reducing energy intake and/or increasing energy expenditure. The weight loss period is usually viewed as a continuous process, ending when energy equilibrium is achieved at a lower weight or with death following depletion of fuel stores. However, growing evidence supports the expanded view that induction of negative energy balance leads to well‐defined physiological effects characterized by three discrete phases (I‐III). At present there are no comprehensive reviews of the ‘early’ phase of weight loss, a gap highlighted by recent interest in rapidly testing new treatments with short‐term protocols. Herein we show from earlier reports and with new data that weight loss during phase I is: mathematically quantifiable with a t1/2 < 1‐week and 4‐ to 6‐week duration; includes well‐defined rapidly evolving body composition and energy expenditure changes; and is moderated by multiple factors including subject sex and activity level, nutrients ingested at baseline and during the negative energy balance period, and hormone and pharmacologic treatments. Our in depth review collectively characterizes phase I as a distinct weight loss period while revealing important knowledge gaps that can be filled with appropriately designed future studies.

Effect of NPY5R antagonist MK-0557 on weight regain after very-low-calorie diet-induced weight loss

Objective: To evaluate whether MK‐0557, a highly selective, orally administered neuropeptide Y Y5 receptor antagonist, could limit weight regain after very‐low‐calorie diet (VLCD)‐induced weight loss.

How much may I eat? Calorie estimates based upon energy expenditure prediction equations.

How much may I eat? Most healthcare workers, when asked this question, have insufficient knowledge to educate their patients on a healthy energy intake level. In this review we examine the available methods for estimating adult energy requirements with a focus on the newly developed National Academy of Sciences/Institute of Medicine (NAS/IOM) doubly‐labelled water total energy expenditure (TEE) prediction equations. An overview is first provided of the traditional factorial method of estimating energy requirements. We then extend this overview by exploring the development of the NAS/IOM TEE prediction models and their role in estimating energy requirments as a function of sex, age, weight, height and physical activity level. The NAS/IOM prediction models were developed for evaluating group energy requirements, although the formulas can be applied in individual ‘example’ patients for educational purposes. Potential limitations and interpretation issues of both the factorial and NAS/IOM methods are examined. This information should provide healthcare professionals with the tools and understanding to appropriately answer the question, ‘How much may I eat?’