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Objective To assess the effectiveness of sitagliptin compared to sulfonylureas as add-on to metformin in adults with type 2 diabetes mellitus from both randomised controlled trials (RCTs) and ‘real-world’ non-randomised studies. Methods and analyses We conducted a systematic review of EMBASE, MEDLINE, CENTRAL and grey literature for RCTs and non-randomised studies. We reported outcomes relating to change in HbA1c, fasting glucose, weight, blood pressure and lipids from baseline and need for treatment change. No study investigating macrovascular and microvascular diabetes complications was found. Meta-analysis was used where studies were sufficiently homogenous. Results Seven RCTs and five non-randomised studies were eligible for inclusion from 1335 articles retrieved. Meta-analysis of three homogenous RCTs revealed a statistically significant decrease in weight with sitagliptin when compared to sulfonylureas (weighted mean difference (WMD) −2.05 kg; 95% CI −2.38 to −1.71); however, a similar change from baseline in HbA1c (WMD 0.05; 95% CI −0.03 to 0.12), fasting glucose (WMD 0.11; 95% CI −0.08 to –0.29), blood pressure, lipids and the proportion achieving HbA1c

Valéry Bocquet Competence Center for Methodology and Statistics, Luxembourg Institute of Health, LuxembourgDiabetes is a disease whose global prevalence has been rising year after year, and by 2014 more than 400 million individuals were diagnosed with diabetes.1 As a consequence, screening of patients with type 1 or type 2 diabetes has become important, both to estimate the prevalence of diabetes and to treat affected individuals. For that purpose, a two-step algorithm suggested by Sharma et al2 was recently published, whose aims were to identify type 1 or type 2 individuals from a primary care database. The first step of the algorithm was based on the diagnostic records, treatment given, and results obtained from clinical tests. The second part was based on the combination of diagnostic codes, prescribed medications, age at the time of diagnosis, and finally whether the case was prevalent or incident.View original paper by Sharma et al

Manuj Sharma,1 Irene Petersen,1,2 Irwin Nazareth,1 Sonia J Coton,1 1Department of Primary Care and Population Health, University College London, London, UK; 2Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark Background: Research into diabetes mellitus (DM) often requires a reproducible method for identifying and distinguishing individuals with type 1 DM (T1DM) and type 2 DM (T2DM). Objectives: To develop a method to identify individuals with T1DM and T2DM using UK primary care electronic health records. Methods: Using data from The Health Improvement Network primary care database, we developed a two-step algorithm. The first algorithm step identified individuals with potential T1DM or T2DM based on diagnostic records, treatment, and clinical test results. We excluded individuals with records for rarer DM subtypes only. For individuals to be considered diabetic, they needed to have at least two records indicative of DM; one of which was required to be a diagnostic record. We then classified individuals with T1DM and T2DM using the second algorithm step. A combination of diagnostic codes, medication prescribed, age at diagnosis, and whether the case was incident or prevalent were used in this process. We internally validated this classification algorithm through comparison against an independent clinical examination of The Health Improvement Network electronic health records for a random sample of 500 DM individuals. Results: Out of 9,161,866 individuals aged 0–99 years from 2000 to 2014, we classified 37,693 individuals with T1DM and 418,433 with T2DM, while 1,792 individuals remained unclassified. A small proportion were classified with some uncertainty (1,155 [3.1%] of all individuals with T1DM and 6,139 [1.5%] with T2DM) due to unclear health records. During validation, manual assignment of DM type based on clinical assessment of the entire electronic record and algorithmic assignment led to equivalent classification in all instances. Conclusion: The majority of individuals with T1DM and T2DM can be readily identified from UK primary care electronic health records. Our approach can be adapted for use in other health care settings. Keywords: diabetes and endocrinology, epidemiology, public health, databases, algorithm&nbsp

Objective To investigate trends in incident and prevalent diagnoses of type 2 diabetes mellitus (T2DM) and its pharmacological treatment between 2000 and 2013. Design Analysis of longitudinal electronic health records in The Health Improvement Network (THIN) primary care database. Setting UK primary care. Participants In total, we examined 8 838 031 individuals aged 0–99 years. Outcome measures The incidence and prevalence of T2DM between 2000 and 2013, and the effect of age, sex and social deprivation on these measures were examined. Changes in prescribing patterns of antidiabetic therapy between 2000 and 2013 were also investigated. Results Overall, 406 344 individuals had a diagnosis of T2DM, of which 203 639 were newly diagnosed between 2000 and 2013. The incidence of T2DM rose from 3.69 per 1000 person-years at risk (PYAR) (95% CI 3.58 to 3.81) in 2000 to 3.99 per 1000 PYAR (95% CI 3.90 to 4.08) in 2013 among men; and from 3.06 per 1000 PYAR (95% CI 2.95 to 3.17) to 3.73 per 1000 PYAR (95% CI 3.65 to 3.82) among women. Prevalence of T2DM more than doubled from 2.39% (95% CI 2.37 to 2.41) in 2000 to 5.32% (95% CI 5.30 to 5.34) in 2013. Being male, older, and from a more socially deprived area was strongly associated with having T2DM, (p