The influence of glucose-lowering therapies on cancer risk in type 2 diabetes

AIMS/HYPOTHESIS: The risk of developing a range of solid tumours is increased in type 2 diabetes, and may be influenced by glucose-lowering therapies. We examined the risk of development of solid tumours in relation to treatment with oral agents, human insulin and insulin analogues. METHODS: This was a retrospective cohort study of people treated in UK general practices. Those included in the analysis developed diabetes >40 years of age, and started treatment with oral agents or insulin after 2000. A total of 62,809 patients were divided into four groups according to whether they received monotherapy with metformin or sulfonylurea, combined therapy (metformin plus sulfonylurea), or insulin. Insulin users were grouped according to treatment with insulin glargine, long-acting human insulin, biphasic analogue and human biphasic insulin. The outcome measures were progression to any solid tumour, or cancer of the breast, colon, pancreas or prostate. Confounding factors were accounted for using Cox proportional hazards models. RESULTS: Metformin monotherapy carried the lowest risk of cancer. In comparison, the adjusted HR was 1.08 (95% CI 0.96-1.21) for metformin plus sulfonylurea, 1.36 (95% CI 1.19-1.54) for sulfonylurea monotherapy, and 1.42 (95% CI 1.27-1.60) for insulin-based regimens. Adding metformin to insulin reduced progression to cancer (HR 0.54, 95% CI 0.43-0.66). The risk for those on basal human insulin alone vs insulin glargine alone was 1.24 (95% CI 0.90-1.70). Compared with metformin, insulin therapy increased the risk of colorectal (HR 1.69, 95% CI 1.23-2.33) or pancreatic cancer (HR 4.63, 95% CI 2.64-8.10), but did not influence the risk of breast or prostate cancer. Sulfonylureas were associated with a similar pattern of risk as insulin. CONCLUSIONS/INTERPRETATION: Those on insulin or insulin secretagogues were more likely to develop solid cancers than those on metformin, and combination with metformin abolished most of this excess risk. Metformin use was associated with lower risk of cancer of the colon or pancreas, but did not affect the risk of breast or prostate cancer. Use of insulin analogues was not associated with increased cancer risk as compared with human insulin

The Diabetes Pearl: Diabetes biobanking in The Netherlands

Contains fulltext : 109720.pdf (publisher's version ) (Open Access)ABSTRACT: BACKGROUND: Type 2 diabetes is associated with considerable comorbidity and severe complications, which reduce quality of life of the patients and require high levels of healthcare. The Diabetes Pearl is a large cohort of patients diagnosed with type 2 diabetes, covering different geographical areas in the Netherlands. The aim of the study is to create a research infrastructure that will allow the study of risk factors, including biomarkers and genetic determinants for severe diabetes complications. METHODS/DESIGN: Baseline examinations began November 2009 and will continue through 2012. By the end of 2012, it is expected that 7000 patients with type 2 diabetes will be included in the Diabetes Pearl cohort. To ensure quality of the data collected, standard operation procedures were developed and used in all 8 recruitment centers. From all patients who provide informed consent, the following information is collected: personal information, medication use, physical examination (antropometry, blood pressure, electrocardiography (ECG), retina photographs, ankle-brachial index, peripheral vibration perception), self-report questionnaire (socio-economic status, lifestyle, (family) history of disease, and psychosocial well-being), laboratory measurements (glucose, A1c, lipid profile, kidney function), biobank material (storage of urine and blood samples and isolated DNA). All gathered clinical data and biobank information is uploaded to a database for storage on a national level. Biobanks are maintained locally at all recruitment centers. DISCUSSION: The Diabetes Pearl is large-scale cohort of type 2 diabetes patients in the Netherlands aiming to study risk factors, including biomarkers and genetic markers, for disease deterioration and the development of severe diabetes complications. As a result of the well-designed research design and the national coverage, the Diabetes Pearl data can be of great value to national and international researchers with an interest in diabetes related research

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

<p>Abstract</p> <p>Background</p> <p>The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.</p> <p>Methods</p> <p>We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.</p> <p>Results</p> <p>Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).</p> <p>Conclusions</p> <p>We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.</p

Use of hormonal contraceptive methods by women with diabetes.

This study sought to establish use of hormonal contraception in UK women aged between 15 and 44 years with type 1 or type 2 diabetes compared with comparison groups with no diabetes. A cross sectional study design was used to compare 947 cases of type 1 diabetes and 365 cases of type 2 diabetes with comparison groups matched for age. Subjects were selected from the General Practice Research Database (GPRD)

Miscoding, misclassification and misdiagnosis of diabetes in primary care.

Aims:  To determine the effectiveness of self-audit tools designed to detect miscoding, misclassification and misdiagnosis of diabetes in primary care. Methods:  We developed six searches to identify people with diabetes with potential classification errors. The search results were automatically ranked from most to least likely to have an underlying problem. Eight practices with a combined population of 72 000 and diabetes prevalence 2.9% (n = 2340) completed audit forms to verify whether additional information within the patients' medical record confirmed or refuted the problems identified. Results:  The searches identified 347 records, mean 42 per practice. Pre-audit 20% (n = 69) had Type 1 diabetes, 70% (n = 241) had Type 2 diabetes, 9% (n = 30) had vague codes that were hard to classify, 2% (n = 6) were not coded and one person was labelled as having gestational diabetes. Of records, 39.2% (n = 136) had important errors: 10% (n = 35) had coding errors; 12.1% (42) were misclassified; and 17.0% (59) misdiagnosed as having diabetes. Thirty-two per cent (n = 22) of people with Type 2 diabetes (n = 69) were misclassified as having Type 1 diabetes; 20% (n = 48) of people with Type 2 diabetes (n = 241) did not have diabetes; of the 30 patients with vague diagnostic terms, 50% had Type 2 diabetes, 20% had Type 1 diabetes and 20% did not have diabetes. Examples of misdiagnosis were found in all practices, misclassification in seven and miscoding in six. Conclusions:  Volunteer practices successfully used these self-audit tools. Approximately 40% of patients identified by computer searches (5.8% of people with diabetes) had errors; misdiagnosis is commonest, misclassification may affect treatment options and miscoding in omission from disease registers and the potential for reduced quality of care