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Most individuals with type 2 diabetes also have obesity, and treatment with some diabetes medications, including insulin, can cause further weight gain. No approved chronic weight management medications have been prospectively investigated in individuals with overweight or obesity and insulin-treated type 2 diabetes. The primary objective of this study was to assess the effect of liraglutide 3.0 mg versus placebo on weight loss in this population. RESEARCH DESIGN AND METHODS Satiety and Clinical AdipositydLiraglutide Evidence (SCALE) Insulin was a 56-week, randomized, double-blind, placebo-controlled, multinational, multicenter trial in individuals with overweight or obesity and type 2 diabetes treated with basal insulin and £2 oral antidiabetic drugs. RESULTS Individuals were randomized to liraglutide 3.0 mg (n 5 198) or placebo (n 5 198), combined with intensive behavioral therapy (IBT). At 56 weeks, mean weight change was 25.8% for liraglutide 3.0 mg versus 21.5% with placebo (estimated treatment difference 24.3% [95% CI 25.5; 23.2]; P < 0.0001). With liraglutide 3.0 mg, 51.8% of individuals achieved ‡5% weight loss versus 24.0% with placebo (odds ratio 3.41 [95% CI 2.19; 5.31]; P < 0.0001). Liraglutide 3.0 mg was associated with significantly greater reductions in mean HbA 1c and mean daytime glucose values and less need for insulin versus placebo, despite a treat-to-glycemic-target protocol. More hypoglycemic events were observed with placebo than liraglutide 3.0 mg. No new safety or tolerability issues were observed. CONCLUSIONS In individuals with overweight or obesity and insulin-treated type 2 diabetes, liraglutide 3.0 mg as an adjunct to IBT was superior to placebo regarding weight loss and improved glycemic control despite lower doses of basal insulin and without increases in hypoglycemic events.

High body mass index (BMI) is known to be associated with various conditions, including type 2 diabetes (T2D), osteoarthritis, cardiovascular disease (CVD) and sleep apnoea; however, the impact of intentional weight loss on the risk of these and other outcomes is not well quantified. We examined the effect of weight loss on ten selected outcomes in a population from the UK Clinical Practice Research Datalink (CPRD) GOLD database. Included individuals were >18 years old at the index date (first BMI value between January 2001 and December 2010). They were categorised by their weight pattern between year 1 post-index and year 4 post-index (baseline period) as having stable weight (−5% to +5%) or weight loss (−25% to −10%, plus evidence of intervention or dietary advice to confirm intention to lose weight). For inclusion, individuals also required a BMI of 25.0–50.0 kg/m2 at the start of the follow-up period, during which the occurrence of ten obesity-related outcomes was recorded. Cox proportional hazard models adjusted for BMI, comorbidities, age, sex and smoking status were used to estimate relative risks for weight loss compared with stable weight. Individuals in the weight-loss cohort had median 13% weight loss. Assuming a BMI of 40 kg/m2 before weight loss, this resulted in risk reductions for T2D (41%), sleep apnoea (40%), hypertension (22%), dyslipidaemia (19%) and asthma (18%). Furthermore, weight loss was associated with additional benefits, with lower risk of T2D, chronic kidney disease, hypertension and dyslipidaemia compared with maintaining the corresponding stable lower BMI throughout the study. This study provides objective, real-world quantification of the effects of weight loss on selected outcomes, with the greatest benefits observed for the established CVD risk factors T2D, hypertension and dyslipidaemia.

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BackgroundThe aim of the study was to estimate the prevalence of depression in the population diagnosed with diabetes type 2 and to test the hypothesis that the presence of depression in such cases was associated with a) worse glycaemic control, and b) higher healthcare costs.MethodsWe conducted a cross-sectional analysis, from 1st September 2010 to 31st August 2011, among patients with type 2 diabetes aged 35 years and over in the Basque Country. It was identified how many of them had also depression. The database included administrative individual level information on age, sex, healthcare costs, other comorbidities, and values of glycaemic control (HbA1c). Deprivation index variable was used as socioeconomic measure and, to observe the coexistent pathologies, all the patients diagnoses were categorized by Adjusted Clinical Groups. We used a measure of association, a logistic and a linear regression for analysis.Results12.392 (9.8%) of type 2 diabetes patients were diagnosed with depression, being the prevalence 5.2% for males and 15.1% for females. This comorbidity was higher among the most deprived population. There was no association between the presence of depression and glycaemic control. We estimated that the comorbidity average cost per patient/year was 516€ higher than in patients with just type 2 diabetes (P < 0.001) adjusted by the other covariates.ConclusionsWe did not find any relationship between depression and glycaemic control in patients with type 2 diabetes. However, the comorbidity was associated with significantly high healthcare costs compared to that of type 2 diabetes occurring alone, after adjusting by other illness. Thus, there is a need of more precise recognition, screening and monitoring of depression among diabetic population. Evidence-based treatment for depression should be included in type 2 diabetes clinical guidelines.

In the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.

Objective Obesity rates in the United Kingdom are some of the highest in Western Europe, with considerable clinical and societal impacts. Obesity is associated with type 2 diabetes (T2D), osteoarthritis, cardiovascular disease, and increased mortality; however, relatively few studies have examined the occurrence of multiple obesity‐related outcomes in the same patient population. This study was designed to examine the associations between body mass index (BMI) and a broad range of obesity‐related conditions in the same large cohort from a UK‐representative primary care database. Methods Demographic data and diagnosis codes were extracted from the Clinical Practice Research Datalink GOLD database in January 2019. Adults registered for ≥ 3 years were grouped by BMI, with BMI 18.5–24.9 kg/m2 as reference group. Associations between BMI and 12 obesity‐related outcomes were estimated using Cox proportional hazard models, adjusted for age, sex, and smoking. Results More than 2.9 million individuals were included in the analyses and were followed up for occurrence of relevant outcomes for a median of 11.4 years during the study period. Generally, there was a stepwise increase in risk of all outcomes with higher BMI. Individuals with BMI 40.0–45.0 kg/m2 were at particularly high risk of sleep apnea (hazard ratio [95% confidence interval] vs. reference group: 19.8 [18.9–20.8]), T2D (12.4 [12.1–12.7]), heart failure (3.46 [3.35–3.57]), and hypertension (3.21 [3.15–3.26]). Conclusions This study substantiates evidence linking higher BMI to higher risk of a range of serious health conditions, in a large, representative UK cohort. By focusing on obesity‐related conditions, this demonstrates the wider clinical impact and the healthcare burden of obesity, and highlights the vital importance of management, treatment approaches, and public health programs to mitigate the impact of this disease.

Aims Obesity and cardiovascular diseases (CVDs) often co-occur, likely increasing the intensity of healthcare resource utilization (HCRU). This retrospective, observational database study examined the joint effect of obesity and cardiovascular risk status on HCRU and compared HCRU between body mass index (BMI) categories and CVD-risk categories in the UK. Methods Patient demographics and data on CVD and BMI were obtained from the UK Clinical Practice Research Datalink. Cardiovascular risk status, calculated using the Framingham Risk Equation, was used to categorize people into high-risk and low-risk groups, while a CVD diagnosis was used to define the established CVD group. Patients were split into BMI categories using the standard World Health Organization classifications. For each CVD and BMI category, mean number and costs of general practitioner contacts, hospital admissions and prescriptions were estimated. Results The final study population included 1,600,709 patients. Data on CVD status were available on just over one-quarter of the sample (28.6%) and BMI data for just less than half (43.2%). The number of general practitioner contacts and prescriptions increased with increasing BMI category for each of the three CVD-risk groups. The group with established CVD had the greatest utilization of all components of healthcare resource, followed by high CVD risk then low CVD-risk groups. Conclusion Increasing BMI category and CVD-risk status both affected several HCRU components. These findings highlight the importance of timely obesity management and treatment of CVD-risk factors as a means of preventing increasing HCRU.