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Record W2151669647 · doi:10.2337/dc08-s258

Efficacy and Safety of Rimonabant for Improvement of Multiple Cardiometabolic Risk Factors in Overweight/Obese Patients

2008· article· en· W2151669647 on OpenAlexafffund
Luc Van Gaal, Xavier Pi‐Sunyer, Jean‐Pierre Després, Christine McCarthy, André Scheen

Bibliographic record

VenueDiabetes Care · 2008
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversité Laval
FundersFonds Wetenschappelijk OnderzoekGlaxoSmithKlineVlaamse regeringPfizerAstraZenecaEli Lilly and CompanyPfizer CanadaNovo NordiskSanofiAbbott Laboratories
KeywordsRimonabantMedicineOverweightType 2 diabetesDyslipidemiaInternal medicinePopulationDiabetes mellitusPlaceboMetforminBody mass indexWeight lossObesityEndocrinologyAntagonistEnvironmental healthCannabinoid receptor

Abstract

fetched live from OpenAlex

OBJECTIVE: To better define the efficacy and safety of rimonabant, the first selective cannabinoid type 1 (CB(1)) receptor antagonist, in a large population of overweight and obese patients using pooled efficacy data from three Phase III nondiabetes Rimonabant in Obesity and Related Metabolic Disorders (RIO) studies, selected efficacy data from the RIO-Diabetes study, and pooled safety data for all four RIO studies. RESEARCH DESIGN AND METHODS: The RIO studies enrolled patients who were either overweight (BMI >27 kg/m(2)) with at least one comorbidity (i.e., hypertension, dyslipidemia, or, for RIO-Diabetes, type 2 diabetes) or obese. All patients received daily treatment with rimonabant (5 or 20 mg) or placebo for 1 year plus a hypocaloric diet (600 kcal/day deficit) and advice on increased physical activity. RIO-Europe (n = 1,508), RIO-North America (n = 3,045), and RIO-Lipids (n = 1,036) excluded patients with type 2 diabetes; untreated dyslipidemia was an entry requirement for RIO-Lipids. RIO-Diabetes (n = 1,047) required the presence of type 2 diabetes inadequately controlled by sulfonylurea or metformin monotherapy. RESULTS: The pooled intention-to-treat population comprised 5,580 patients without diabetes (3,165 completed treatment) and 1,047 patients with diabetes (692 completed treatment). Most efficacy measures improved during the 4-week placebo run-in period, except that HDL cholesterol decreased as expected in the early phase of a hypocaloric diet. After 1 year of randomized treatment, changes from baseline with 20 mg rimonabant in the nondiabetic population were as follows: body weight -6.5 kg, waist circumference -6.4 cm, HDL cholesterol +16.4%, triglycerides -6.9%, fasting insulin -0.6 muU/ml, and homeostasis model assessment for insulin resistance (HOMA-IR) -0.2 (all P < 0.001 vs. placebo). In the diabetic population, 20 mg rimonabant reduced A1C levels by 0.6% (P < 0.001 vs. placebo). Regression analysis of change in HDL cholesterol, triglycerides, adiponectin (in RIO-Lipids), and A1C (in RIO-Diabetes) versus body weight at 1 year by ANCOVA suggested that 45-57% of the effect of rimonabant could not be explained by the observed weight loss. At 1 year, adverse events more frequently reported with rimonabant were gastrointestinal, neurological, and psychiatric in nature. Serious adverse events were infrequent and almost equivalent to placebo. Overall discontinuation rates were similar across treatment groups, except discontinuation from adverse events, which occurred more frequently with 20 mg rimonabant versus placebo (most commonly, depressive disorders [1.9 vs. 0.8%], nausea [1.4 vs. 0.1%], mood alterations with depressive symptoms [1.0 vs. 0.6%], and anxiety [1.0 vs. 0.3%]). A thorough review of psychiatric and neurological adverse events was performed. CONCLUSIONS: In overweight/obese patients, 20 mg/day rimonabant produced weight loss and significant improvements in multiple cardiometabolic risk factors such as waist circumference, A1C, HDL cholesterol, and triglycerides. Rimonabant was generally well tolerated, with more frequently reported adverse events being gastrointestinal, neurological, and psychiatric in nature.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.251
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations199
Published2008
Admission routes2
Has abstractyes

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