Aims To select a core list of standard outcomes for diabetes to be routinely applied internationally, including patientreported outcomes. Methods We conducted a structured systematic review of outcome measures, focusing on adults with either type 1 or type 2 diabetes. This process was followed by a consensus-driven modified Delphi panel, including a multidisciplinary group of academics, health professionals and people with diabetes. External feedback to validate the set of outcome measures was sought from people with diabetes and health professionals. Results The panel identified an essential set of clinical outcomes related to diabetes control, acute events, chronic complications, health service utilisation, and survival that can be measured using routine administrative data and/or clinical records. Three instruments were recommended for annual measurement of patient-reported outcome measures: the WHO Well-Being Index for psychological well-being; the depression module of the Patient Health Questionnaire for depression; and the Problem Areas in Diabetes scale for diabetes distress. A range of factors related to demographic, diagnostic profile, lifestyle, social support and treatment of diabetes were also identified for case-mix adjustment. Conclusions We recommend the standard set identified in this study for use in routine practice to monitor, benchmark and improve diabetes care. The inclusion of patient-reported outcomes enables people living with diabetes to report directly on their condition in a structured way.

IntroductionPrevious reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methodsWe trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.ResultsSNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).ConclusionsDiabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications.

ObjectiveTo determine the prevalence of diabetic retinopathy (DR) and diabetic macular oedema (DME) and their associated risk factors in patients recently diagnosed with type 2 diabetes.Methods and analysisWe carried out a cross-sectional study from April 2014 to August 2017. We included patients aged ≥18 years. Diabetes was defined as fasting plasma glucose of >7.8 mmol/L or 2-hour postload plasma glucose of >11.1 mmol/L. Non-mydriatic fundus examination with a digital-fundus camera was performed. Three images centred in the macula, optic disc and temporal to the macula were obtained and graded according to the Scottish Scale Classification of Diabetic Retinopathy.Results1232 patients (mean age 51.5 years) with a diabetes duration of 0–5 years were examined. Age-adjusted and sex-adjusted prevalence of DR and DME was 17.4% (95% CI 15.3% to 19.6%) and 6.6% (95% CI 5.4% to 8.2%), respectively. DR was associated with diabetes duration (OR per year=1.20, p<0.001), haemoglobin A1c (HbA1c) from 7.0 to 8.9 (OR=2.19, p<0.001), HbA1c≥9 (OR=2.98, p<0.001) and systolic blood pressure (SBP) (OR=1.16 per 5 mm Hg, p<0.001). DME was associated with diabetes duration (OR per year=1.26, p<0.01), HbA1c from 7.0 to 8.9 (OR=2.26, p<0.05), HbA1c≥9 (OR=2.38, p<0.01), SBP (OR per mm Hg=1.15, p<0.001) and albuminuria (OR=2.45, p<0.01).ConclusionOur study contributes to the evidence of progressive increase in DR and DME risk in early stages of diabetes, supporting the urgent need for early screening.

Context. The photometric and astrometric measurements of the Pleiades DANCe DR2 survey provide an excellent test case for the benchmarking of statistical tools aiming at the disentanglement and characterisation of nearby young open cluster (NYOC) stellar populations. Aims. We aim to develop, test, and characterise of a new statistical tool (intelligent system) for the sifting and analysis of NYOC populations. Methods. Using a Bayesian formalism, with this statistical tool we were able to obtain the posterior distributions of parameters governing the cluster model. It also used hierarchical bayesian models to establish weakly informative priors, and incorporates the treatment of missing values and non-homogeneous (heteroscedastic) observational uncertainties. Results. From simulations, we estimated that this statistical tool renders kinematic (proper motion) and photometric (luminosity) distributions of the cluster population with a contamination rate of 5.8 ± 0.2%. The luminosity distributions and present day mass function agree with the ones found in a recent study, on the completeness interval of the survey. At the probability threshold of maximum accuracy, the classifier recovers ≈90% of the recently published candidate members and finds 10% of new ones. Conclusions. A new statistical tool for the analysis of NYOC is introduced, tested, and characterised. Its comprehensive modelling of the data properties allows it to get rid of the biases present in previous works. In particular, those resulting from the use of only completely observed (non-missing) data and the assumption of homoskedastic uncertainties. Also, its Bayesian framework allows it to properly propagate observational uncertainties into membership probabilities and cluster velocity and luminosity distributions. Our results are in a general agreement with those from the literature, although we provide the most up-to-date and extended list of candidate members of the Pleiades cluster.

Empowerment interventions for chronic diseases are an evolving process. No agreement exists regarding the necessary components and methodologies to be applied. Systematic reviews have assessed the effect of self-management interventions. Improvements in illness beliefs, adherence to drug therapy and glucose monitoring have been reported. In the long term, no major changes have been achieved in weight, physical activity, smoking status, and depression scores. There is a need for additional studies. The CAIPaDi (Centro de Atención Integral del Paciente con Diabetes) program is an intervention designed to provide education and empowerment techniques (using simple low-cost interactive tools) over a short period of time followed by at-distance support using internet or cell phone technology. The target population consists of patients with type 2 diabetes, free of chronic complications who are non-smokers. The intervention is composed of four monthly visits followed by a continuous at-distance support system. At each visit, patients stay for six hours in the center. Information is presented in group sessions. Empowerment techniques are applied during individual exchanges with the team or during facilitated group sessions. In summary, empowerment programs are an unmet need in many healthcare services. This review also discusses relevant studies and patents in the management of type 2 diabetes.