Meta-Analysis: Pharmacologic Treatment of Obesity

By Zhaoping Li, M. Maglione, W. Tu, W. Mojica, D. Arterburn, L. Shugarman, L. Hilton, M. Suttorp, V. Solomon, Paul Shekelle and 1 more. Annals of Internal Medicine, 2005

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Context The effectiveness of pharmacologic therapy in the treatment of obesity is unclear. Contribution This review of 79 clinical trials involving diet plus the obesity drugs sibutramine, orlistat, fluoxetine, sertraline, bupropion, topiramate, or zonisamide shows that these medications can lead to modest weight reductions of approximately 5 kg or less at 1 year. Available evidence is lacking on the effect of these drugs on long-term weight loss, health outcomes such as cardiovascular events and diabetes, and adverse effects. Implications Those considering pharmacologic treatment for obesity should understand that these drugs can lead to modest weight loss at 1 year, but data on long-term effectiveness and safety are lacking. The Editors Obesity has been defined as excess body fat relative to lean body mass (1) and, in humans, is the result of interactions of the environment with multiple genes. The age-adjusted prevalence of obesity was 30.5% in 19992000 (2). Although it is difficult to precisely estimate the change in prevalence of obesity over time because of changing definitions, nearly all clinical authorities agree that obesity is reaching epidemic proportions (2-13). Obesity is currently defined as a body mass index (BMI) of 30 kg/m2 or greater. Individuals whose BMI falls between 25 kg/m2 and 29.9 kg/m2 are considered overweight. Attempts to meet the body weight goal of the Healthy People 2000 initiative (7)to reduce the prevalence of overweight among adults to less than 20% of the populationdid not succeed. Still, many Americans are trying. According to a national survey (14), about 40% of women and 25% of men reported that they were currently trying to lose weight. However, most weight loss attempts consist of 6 months of loss followed by gradual regain to baseline (15). The health consequences of obesity include some of the most common chronic diseases in our society. Obesity is an independent risk factor for heart disease (16). Type 2 diabetes mellitus, hypertension and stroke, hyperlipidemia, osteoarthritis, and sleep apnea are all more common in obese individuals (17-19). A recent prospective study involving 900000 U.S. adults reported that increased body weight was associated with increased death rates for all cancer combined and for cancer at multiple specific sites (20). Adult weight gain is associated with increased risk for breast cancer in postmenopausal women (21). Weight loss of 5% to 10% can be associated with marked reductions in the risk for these chronic diseases (22). In the Diabetes Prevention Program, weight loss of about 5% to 6% among persons with a BMI of 34 kg/m2, along with increased physical activity, resulted in a 58% reduction in the incidence of diabetes (23). In response to the increase in obesity, pharmaceutical treatments for obesity have become both more numerous and more commonly used. Drugs prescribed for weight loss can be divided into 2 categoriesappetite suppressants and lipase inhibitorson the basis of their putative mechanisms of action. Appetite suppressants can be further subdivided on the basis of the neurotransmitters they are believed to affect. This article, which reviews the available evidence on medications used as obesity treatment in adults (Table 1), is part of a larger evidence report prepared for the Agency for Healthcare Research and Quality titled Pharmacological and Surgical Treatment of Obesity. The larger report is available at www.ncbi.nlm.nih.gov/books/bv.fcgi?rid=hstat1a.chapter.19289. Table 1. Prescription Medications Used for Weight Loss Methods Literature Search and Selection Our search for controlled human studies of pharmacologic treatments of obesity began with an electronic search of MEDLINE on 16 October 2002. Subsequently, our librarian conducted current awareness search updates on 22 May, 2 June, 12 June, and 3 July 2003. We also searched the Cochrane Controlled Clinical Trials Register Database and existing systematic reviews. Full details of the search strategy are available in the larger evidence report. To be accepted for analysis, a study of drug therapy had to be a controlled clinical trial that assessed the effect of one of the pharmaceutical agents in humans and reported at least 6-month weight loss outcomes in pounds or kilograms. We made an exception for topiramate, for which most trials reported only percentage of weight loss. Patients in included studies needed to have a BMI of 27 kg/m2 or greater (Appendix Table). The technical expert panel for our evidence report determined which pharmaceutical agents would be included. The panel chose sibutramine, orlistat, phentermine, and diethylpropion, all of which have been approved by the U.S. Food and Drug Administration, as well as other medications being used for weight loss, including fluoxetine, bupropion, sertraline, topiramate, and zonisamide. Extraction of Study-Level Variables and Results Three reviewers, working in groups of 2, extracted data from the same articles and resolved disagreements by consensus. A senior physician resolved any remaining disagreements. We used the Jadad score to evaluate the quality of the studies, using information on study design, method of random assignment, blinding, and withdrawal (34). Jadad scores range from 0 (lowest quality) to 5 (highest quality). We also collected information on withdrawal and dropout rates and calculated the percentage of attrition by dividing the number of patients providing follow-up data by the number of patients initially enrolled. Of the medications we assessed, 3 had up-to-date existing meta-analyses (sibutramine, phentermine, and diethylpropion) and 4 others had a sufficient number of new studies to justify a new meta-analysis (orlistat, fluoxetine, bupropion, and topiramate). However, because heterogeneity was too great for the fluoxetine studies, they are summarized narratively. Selection of Trials for Meta-Analysis The outcome of interest was weight loss between baseline and follow-up. To make our analyses comparable, we stratified them in the same manner as did the recent meta-analysis on sibutramine (35). We defined data collected at 6 months to be data collected at any point between 16 and 24 weeks; likewise, 1-year follow-up data were those collected at any point between 44 and 54 weeks. If a study presented data for 2 or more time points in an interval, for example, 16 and 18 weeks, we chose the longest follow-up measurement for our analysis. Mean Difference For each trial, we extracted the follow-up mean weight loss for the control group, the follow-up mean weight loss for the medication group, and the standard deviation for each group. We then calculated a mean difference for each study, which was the difference between follow-up mean weight loss in the control group versus the medication group. Sensitivity Analyses We conducted sensitivity analyses on 4 study dimensions: Jadad quality score (2 vs. 3), year of publication (1998 or earlier vs. 1999 or later), completion rate (<80% vs. 80% and <70% vs. 70%), and dosage. We tested for differences between subgroups (for example, high-quality vs. lower-quality studies) by conducting a meta-regression analysis using a single dichotomous variable to indicate subgroup membership. We conducted sensitivity analyses to determine the possible impact of dropouts. In these analyses, we assumed that all patients who dropped out had a weight loss of zero. The mean weight loss for a particular study was then recalculated on the basis of the complete sample of both responders and dropouts. We assumed that the standard deviation of weight loss for a study did not change and recalculated the standard error on the basis of the complete sample size. We then conducted a pooled analysis for each medication and follow-up time as performed in the original approach. Meta-Analysis of Weight Loss For the 6-month and 12-month analyses, we estimated a pooled DerSimonianLaird random-effects estimate (36) of the overall mean difference. The mean differences in the individual trials are weighted by both within-study variation and between-study variation in this synthesis. We also report P values derived from the chi-square test of heterogeneity based on the Cochran Q-test (37), and the I2 statistic (38). This latter statistic represents the percentage of study variability that is due to heterogeneity rather than chance and is independent of the number of studies and the effect size metric. Publication Bias We assessed the possibility of publication bias by evaluating a funnel plot. We also conducted an adjusted rank correlation test (39) as a formal statistical test for publication bias. Extraction of Data on Adverse Events We assessed evidence of adverse events from randomized, controlled trials (RCTs) only. We did not include observational studies or case series data. Each trial included in the weight loss analysis was examined to determine whether it reported data on adverse events. Adverse events were recorded as the number of events or the number of people, depending on how the trial chose to report events. Most trials recorded the number of events rather than the number of unique people who experienced the event. Each event was counted as if it represented a unique individual. Because a single individual might have experienced more than 1 event, this assumption may have overestimated the number of people who had an adverse event. Meta-Analysis of Adverse Events For subgroups of events that occurred in 2 or more trials, at least once in the medication group and at least once in the control group, we performed a meta-analysis to estimate the pooled odds ratio and its associated 95% CI. Given that many of the events were rare, we used exact conditional inference to perform the pooling rather than applying the usual asymptotic methods that assume normality. For interpretability, for any significant pooled odds ratio greater than 1 (which indicates that the odds of the adverse

Subfields: Pharmacology, Pharmacy, Surgery

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