Diabetes in Older Adults: A Consensus Report

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95492/1/jgs12035.pd

Computer modeling of diabetes and Its transparency: a report on the Eighth Mount Hood Challenge

Objectives The Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes. Methods Participating modeling groups were asked to reproduce the results of two published studies using the input data described in those articles. Gaps in input data were filled with assumptions reported by the modeling groups. Goodness of fit between the results reported in the target studies and the groups’ replicated outputs was evaluated using the slope of linear regression line and the coefficient of determination (R2). After a general discussion of the results, a diabetes-specific checklist for the transparency of model input was developed. Results Seven groups participated in the transparency challenge. The reporting of key model input parameters in the two studies, including the baseline characteristics of simulated patients, treatment effect and treatment intensification threshold assumptions, treatment effect evolution, prediction of complications and costs data, was inadequately transparent (and often missing altogether). Not surprisingly, goodness of fit was better for the study that reported its input data with more transparency. To improve the transparency in diabetes modeling, the Diabetes Modeling Input Checklist listing the minimal input data required for reproducibility in most diabetes modeling applications was developed. Conclusions Transparency of diabetes model inputs is important to the reproducibility and credibility of simulation results. In the Eighth Mount Hood Challenge, the Diabetes Modeling Input Checklist was developed with the goal of improving the transparency of input data reporting and reproducibility of diabetes simulation model results

HbA1c response after insulin initiation in patients with type 2 diabetes mellitus in real life practice:Identifying distinct subgroups

Aims To identify subgroups of patients with type 2 diabetes mellitus (T2DM) following distinct trajectories of HbA1c after insulin initiation and explore underlying differences in clinical characteristics. Materials and methods A cohort study was conducted in patients with T2DM initiating insulin in 2007-2013 with a follow-up of 2 to 4 years. Data were collected from the Groningen Initiative to Analyze Type 2 Diabetes Treatment (GIANTT) database. The primary outcome was subgroups with different trajectories of HbA1c patterns after insulin initiation, as identified by latent class growth modeling. Differences between subgroups were tested using one-way ANOVA, Kruskal-Wallis or chi-square tests, where appropriate. Results From 1459 patients, three subgroups with distinct HbA1c patterns were identified. Group 1 (8%) initially showed a moderate decrease followed by an increase in HbA1c 2 years later, despite receiving more comedication. Group 2 (84%) showed a stable decrease. Group 3 (8%) had a high initial level of HbA1c and a rapid decline within the first year, followed by a slow increase thereafter. Group 1 patients were on average 6-7 years younger than patients in groups 2 and 3 and were more likely to receive sulfonylureas than Group 3 patients. Group 3 patients had a shorter diabetes duration and were less well-controlled for HbA1c, systolic blood pressure and LDL-cholesterol at insulin initiation. Conclusions Most patients showed a stable HbA1c response, but one out of six patients showed either a poor response, or a rapid initial response only after insulin initiation. Response patterns were associated with age, diabetes duration and risk-factor controls at the time of insulin initiation

“The Times They Are A-Changin’” at Diabetes Care

Every five years or so, the editorial team leading Diabetes Care turns over with the appointment of new leadership. This issue of volume 46 represents the first of a new editorial team, making it the tenth group to be responsible for the scientific content of the journal. Starting in 1978 with Jay Skyler as its first editor, Diabetes Care has gone from strength to strength with new initiatives and a steady increase in its influence. This impact has been in line with the charge given at the journal’s founding by the then president of the American Diabetes Association Norbert Freinkel when he wrote, “The new journal is designed to promote better patient care by serving the expanded needs of all health professionals committed to the care of patients with diabetes.

Meeting individualized glycemic targets in primary care patients with type 2 diabetes in Spain

BACKGROUND: Information about the achievement of glycemic targets in patients with type 2 diabetes according to different individualization strategies is scarce. Our aim was to analyze the allocation of type 2 diabetic patients into individualized glycemic targets according to different strategies of individualization and to assess the degree of achievement of adequate control. METHODS: Cross-sectional analysis on 5382 type 2 diabetic patients in primary care setting in Spain between 2011 and 2012. Targets of HbA1c were assigned based on different strategies of individualization of glycemic targets: 1) the ADA/EASD consensus 2) The Spanish Diabetes Society (SED) consensus 3) a strategy that accounts for the risk of hypoglycemia (HYPO) considering the presence of a hypoglycemia during the last year and type of hypoglycemic treatment. Concordance between the different strategies was analyzed. RESULTS: A total of 15.9, 17.1 and 67 % applied to ADA/EASD recommendation of HbA1c target of <6.5, < 7 and <8 % (48, 53 and 64 mmol/mol), and 31.9 and 67.4 % applied to the SED glycemic target of <6.5 and <7.5 % (<48 and 58 mmol/mol). Using the HYPO strategy, 53.5 % had a recommended HbA1c target <7 % (53 mmol/mol). There is a 94 % concordance between the ADA/EASD and SED strategies, and a concordance of 41–42 % between these strategies and HYPO strategy. Using the three different strategies, the overall proportion of patients achieving glycemic targets was 56–68 %. CONCLUSIONS: Individualization of glycemic targets increases the number of patients who are considered adequately controlled. The proposed HYPO strategy identifies a similar proportion of patients that achieve adequate glycemic control than ADA/EASD or SED strategies, but its concordance with these strategies in terms of patient classification is bad

Profiling the mental health of diabetic patients: a cross-sectional survey of Zimbabwean patients

Objective The burden of diabetes mellitus has exponentially increased in low resource settings. Patients with diabetes are more likely to exhibit poor mental health which negatively affects treatment outcomes. However, patients with high levels of social support (SS) are likely to report optimal mental health. We sought to determine how SS affects the report of psychiatric morbidity and health-related quality of life (HRQoL) in 108 diabetic patients in Harare, Zimbabwe. Results The average age of participants was 54.1 (SD 18.6) years. Most of the participants were; females (69.4%), married (51.9%), and were of low level of income (43.5%). 37.1% of the participants exhibited signs of psychiatric morbidity [mean Shona Symptoms Questionnaire score—6.7 (SD 3.2)]. Further, patients also reported lower HRQoL [mean EQ-5D-VAS score—64.1 (SD 15.3)] and high levels of SS [mean Multidimensional Scale of Perceived Social Support score—43.7 (SD 11.5)]. Patients who received greater amount of SS had optimal mental health. Being female, unmarried, lower education attainment, having more comorbid conditions, being diagnosed with type 2 diabetes and having been diagnosed of diabetes for a longer duration were associated with poorer mental health. It is important to develop context-specific interventions to improve diabetic patients’ mental health