Relationship between retinal vessel diameters and retinopathy in the Inter99 Eye Study

Purpose: To examine the association between retinal vessel diameters and retinopathy in participants with and without type 2 diabetes in a Danish population-based cohort. Methods: The study included 878 persons aged 30 to 60 years from the Inter99 Eye Study. Retinopathy was defined as a presence of one or more retinal hemorrhages or one or more microaneurysms. Vessel diameters were expressed as central retinal artery equivalent diameter (CRAE) and central retinal vein equivalent diameter (CRVE). Multiple linear regression analyses were performed. Results: Among participants with diabetes, CRAE was 6.3 µm (CI 95%: 1.0 to 11.6, p = 0.020) wider and CRVE was 7.9 µm (CI 95%: 0.7 to 15.2, p = 0.030) wider in those with retinopathy compared to those without retinopathy, after adjusting for age, gender, HbA1c, blood pressure, smoking, serum total and HDL cholesterol. In all participants, CRAE increased with presence of retinopathy (p = 0.005) and with smoking (p = 0.001), and CRAE decreased with hypertension (p < 0.001), high HDL cholesterol (p = 0.016) and age (p < 0.001). Central retinal vein equivalent diameter increased with presence of retinopathy (p = 0.022) and with smoking (p < 0.001), and decreased with higher HDL cholesterol (p < 0.001) and age (p = 0.015). Female gender was associated with wider CRVE (p = 0.029). Conclusions: Wider retinal vessel diameters were associated with the presence of retinopathy in participants with diabetes, but not in participants without diabetes. The associations between retinal vessel diameters and known retinopathy risk factors were confirmed. These results suggest that information obtained by non-invasive imaging of the interior of the eye can contribute to a better understanding of systemic disease processes

Early lens aging is accelerated in subjects with a high risk of ischemic heart disease: an epidemiologic study

BACKGROUND: Ischemic heart disease (IHD) is one of the most important causes of mortality and morbidity in the Western world. There is a relationship between aging of the lens of the human eye and cardiovascular disease. The present study was conducted to examine if the risk of ischemic heart disease could be estimated by fluorophotometric assessment of lens aging. METHODS: A total of 421 subjects were included. Risk of IHD was estimated from non-ocular data using the Precard (® )software. Lens aging was quantified by lens fluorometry. RESULTS: The risk of IHD was strongly related to lens fluorophore accumulation (p = 0.001). The relationship between IHD and lens aging was attributable to tobacco smoking and dysglycemia. CONCLUSION: The risk of ischemic heart disease related to smoking and diabetes mellitus can be estimated using the aging of the lens of the eye as a biomarker for generalized tissue-damage

Non-Replication of Genome-Wide Based Associations between Common Variants in INSIG2 and PFKP and Obesity in Studies of 18,014 Danes

(rs17782313) as genetic risk factors. = 2,158) from Steno Diabetes Center. rs17782313 were observed. rs7566605 may influence the level of BMI in combination with the level of physical activity

Targeted prevention in primary care aimed at lifestyle-related diseases:a study protocol for a non-randomised pilot study

Background: The consequences of lifestyle-related disease represent a major burden for the individual as well as for society at large. Individual preventive health checks to the general population have been suggested as a mean to reduce the burden of lifestyle-related diseases, though with mixed evidence on effectiveness. Several systematic reviews, on the other hand, suggest that health checks targeting people at high risk of chronic lifestyle-related diseases may be more effective. The evidence is however very limited. To effectively target people at high risk of lifestyle-related disease, there is a substantial need to advance and implement evidence-based health strategies and interventions that facilitate the identification and management of people at high risk. This paper reports on a non-randomized pilot study carried out to test the acceptability, feasibility and short-term effects of a healthcare intervention in primary care designed to systematically identify persons at risk of developing lifestyle-related disease or who engage in health-risk behavior, and provide targeted and coherent preventive services to these individuals. Methods: The intervention took place over a three-month period from September 2016 to December 2016. Taking a two-pronged approach, the design included both a joint and a targeted intervention. The former was directed at the entire population, while the latter specifically focused on patients at high risk of a lifestyle-related disease and/or who engage in health-risk behavior. The intervention was facilitated by a digital support system. The evaluation of the pilot will comprise both quantitative and qualitative research methods. All outcome measures are based on validated instruments and aim to provide results pertaining to intervention acceptability, feasibility, and short-term effects. Discussion: This pilot study will provide a solid empirical base from which to plan and implement a full-scale randomized study with the central aim of determining the efficacy of a preventive health intervention. Trial registration: Registered at Clinical Trial Gov (Unique Protocol ID: TOFpilot2016). Registered 29 April 2016. The study adheres to the SPIRIT guidelines

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

What determines the cost-effectiveness of diabetes screening?

Aims/hypothesis: The cost-effectiveness of screening for diabetes is unknown but has been modelled previously. None of these models has taken account of uncertainty. We aimed to describe these uncertainties in a model where the outcome was CHD risk. Subjects and methods: Our model used population data from the Danish Inter99 study, and simulations were run in a theoretical population of 1,000,000 individuals. CHD risk was estimated using the UK Prospective Diabetes Study (UKPDS) risk engine, and risk reduction from published randomised clinical trials. Probabilistic sensitivity analysis was used to provide confidence intervals for modelled outputs. Uncertain parameter values were independently simulated from distributions derived from existing literature and deterministic sensitivity analysis performed using multiple model runs under different strategy choices and using extreme parameter estimates. Results: In the least conservative model (low costs and multiplicative risk reduction for combined treatments), the 95% confidence interval of the incremental cost-effectiveness ratio varied from £23,300–82,000. The major contributors to this uncertainty were treatment risk reduction model parameters: the risk reduction for hypertension treatment and UKPDS risk model intercept. Overall cost-effectiveness ratio was not sensitive to decisions about which groups to screen, nor the costs of screening or treatment. It was strongly affected by assumptions about how treatments combine to reduce risk. Conclusions/interpretation: Our model suggests that there is considerable uncertainty about whether or not screening for diabetes would be cost-effective. The most important but uncertain parameter is the effect of treatment. In addition to directly influencing current policy decisions, health care modelling can identify important unknown or uncertain parameters that may be the target of future research

Rare truncating variants in the sarcomeric protein titin associate with familial and early-onset atrial fibrillation

Common genetic variants in structural proteins contribute to risk of atrial fibrillation (AF). Here, using whole-exome sequencing, the authors identify rare truncating variants in TTN that associate with familial and early-onset AF and show defects in cardiac sarcomere assembly in ttn.2-mutant zebrafish