Linear parameter-varying model to design control laws for an artificial pancreas

The contribution of this work is the generation of a control-oriented model for insulin-glucose dynamic regulation in type 1 diabetes mellitus (T1DM). The novelty of this model is that it includes the time-varying nature, and the inter-patient variability of the glucose-control problem. In addition, the model is well suited for well-known and standard controller synthesis procedures. The outcome is an average linear parameter-varying (LPV) model that captures the dynamics from the insulin delivery input to the glucose concentration output constructed based on the UVA/Padova metabolic simulator. Finally, a system-oriented reinterpretation of the classical ad-hoc 1800 rule is applied to adapt the model's gain. The effectiveness of this approach is quantified both in open- and closed-loop. The first one by computing the root mean square error (RMSE) between the glucose deviation predicted by the proposed model and the UVA/Padova one. The second measure is determined by using the ν-gap as a metric to determine distance, in terms of closed-loop performance, between both models. For comparison purposes, both open- (RMSE) and closed-loop (ν-gap metric) quality indicators are also computed for other control-oriented models previously presented. This model allows the design of LPV controllers in a straightforward way, considering its affine dependence on the time-varying parameter, which can be computed in real-time. Illustrative simulations are included. In addition, the presented modeling strategy was employed in the design of an artificial pancreas (AP) control law that successfully withstood rigorous testing using the UVA/Padova simulator, and that was subsequently deployed in a clinical trial campaign where five adults remained in closed-loop for 36 h. This was the first ever fully closed-loop clinical AP trial in Argentina, and the modeling strategy presented here is considered instrumental in resulting in a very successful clinical outcome.Fil: Colmegna, Patricio Hernán. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sánchez Peña, Ricardo S.. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gondhalekar, R.. Harvard University; Estados Unido

The relationship of individual comorbid chronic conditions to diabetes care quality.

ObjectiveMultimorbidity affects 26 million persons with diabetes, and care for comorbid chronic conditions may impact diabetes care quality. The aim of this study was to determine which chronic conditions were related to lack of achievement or achievement of diabetes care quality goals to determine potential targets for future interventions.Research design and methodsThis is an exploratory retrospective analysis of electronic health record data for 23 430 adults, aged 18-75, with diabetes who were seen at seven Midwestern US health systems. The main outcome measures were achievement of six diabetes quality metrics in the reporting year, 2011 (glycated haemoglobin (HbA1c) control and testing, low-density lipoprotein control and testing, blood pressure control, kidney testing). Explanatory variables were 62 chronic condition indicators. Analyses were adjusted for baseline patient sociodemographic and healthcare utilization factors.ResultsThe 62 chronic conditions varied in their relationships to diabetes care goal achievement for specific care goals. Congestive heart failure was related to lack of achievement of cholesterol management goals. Obesity was related to lack of HbA1c and BP control. Mental health conditions were related to both lack of achievement and achievement of different care goals. Three conditions were related to lack of cholesterol testing, including congestive heart failure and substance-use disorders. Of 17 conditions related to achieving control goals, 16 were related to achieving HbA1c control. One-half of the comorbid conditions did not predict diabetes care quality.ConclusionsFuture interventions could target patients at risk for not achieving diabetes care for specific care goals based on their individual comorbidities

The SOD2 C47T polymorphism influences NAFLD fibrosis severity: evidence from case-control and intra-familial allele association studies.

AIMS: Non-alcoholic fatty liver disease (NAFLD) is a complex disease trait where genetic variations and environment interact to determine disease progression. The association of PNPLA3 with advanced disease has been consistently demonstrated but many other modifier genes remain unidentified. In NAFLD, increased fatty acid oxidation produces high levels of reactive oxygen species. Manganese-dependent superoxide dismutase (MnSOD), encoded by the SOD2 gene, plays an important role in protecting cells from oxidative stress. A common non-synonymous polymorphism in SOD2 (C47T; rs4880) is associated with decreased MnSOD mitochondrial targeting and activity making it a good candidate modifier of NAFLD severity. METHODS: The relevance of the SOD2 C47T polymorphism to fibrotic NAFLD was assessed by two complementary approaches: we sought preferential transmission of alleles from parents to affected children in 71 family trios and adopted a case-control approach to compare genotype frequencies in a cohort of 502 European NAFLD patients. RESULTS: In the family study, 55 families were informative. The T allele was transmitted on 47/76 (62%) possible occasions whereas the C allele was transmitted on only 29/76 (38%) occasions, p=0.038. In the case control study, the presence of advanced fibrosis (stage>1) increased with the number of T alleles, p=0.008 for trend. Multivariate analysis showed susceptibility to advanced fibrotic disease was determined by SOD2 genotype (OR 1.56 (95% CI 1.09-2.25), p=0.014), PNPLA3 genotype (p=0.041), type 2 diabetes mellitus (p=0.009) and histological severity of NASH (p=2.0×10(-16)). CONCLUSIONS: Carriage of the SOD2 C47T polymorphism is associated with more advanced fibrosis in NASH

Reference values: a review

Reference values are used to describe the dispersion of variables in healthy individuals. They are usually reported as population-based reference intervals (RIs) comprising 95% of the healthy population. International recommendations state the preferred method as a priori nonparametric determination from at least 120 reference individuals, but acceptable alternative methods include transference or validation from previously established RIs. The most critical steps in the determination of reference values are the selection of reference individuals based on extensively documented inclusion and exclusion criteria and the use of quality-controlled analytical procedures. When only small numbers of values are available, RIs can be estimated by new methods, but reference limits thus obtained may be highly imprecise. These recommendations are a challenge in veterinary clinical pathology, especially when only small numbers of reference individuals are available

Feedback GAP : study protocol for a cluster-randomized trial of goal setting and action plans to increase the effectiveness of audit and feedback interventions in primary care

Peer reviewedPublisher PD

Psychological, emotional and social impairments are associated with adherence and healthcare spending in type 2 diabetic patients: an observational study

OBJECTIVE: The aim of the present study was to assess the association among anxiety, depression, stress, social support and emotional abilities with adherence and healthcare spending in type 2 diabetic patients. PATIENTS AND METHODS: Sixty-four patients were enrolled and completed: Interpersonal Processes of Care (IPC), 20-item Toronto Alexithymia Scale (TAS-20), Rapid Stress Assessment Scale (RSAS), Morisky Medication Adherence Scale (MMAS-4), International Physical Activity Questionnaire (IPAQ)-Short Form and a socio-anamnestic questionnaire regarding also the healthcare spending. RESULTS: Mathematical linear regressions models were performed showing the predictive effects of: anxiety and social support scores (RSAS) on adherence levels (respectively p =. 019; p =. 016); adherence levels on anxiolytic use (p =.04); aggressiveness scores (RSAS) on the number of general check-ups (p =.031); TAS-20 and physician-patient communication (IPC) on the number of hospitalization days (respectively p=.001; p=.008); physician patient decision making (IPC) scores on physical activity (IPAQ) levels (p=.025); physical activity (IPAQ) on the number of medical examinations (p=.039). CONCLUSIONS: An association among psychosocial impairment, adherence and health- care spending was found. Future studies should investigate the effect of a brief psychological intervention in increasing adherence levels and reducing the healthcare spending in this clinical population

Glycemic Control and Cardiovascular Mortality in Hemodialysis Patients With Diabetes

Previous observational studies using differing methodologies have yielded inconsistent results regarding the association between glycemic control and outcomes in diabetic patients receiving maintenance hemodialysis (MHD). We examined mortality predictability of A1C and random serum glucose over time in a contemporary cohort of 54,757 diabetic MHD patients (age 63 ± 13 years, 51% men, 30% African Americans, 19% Hispanics). Adjusted all-cause death hazard ratio (HR) for baseline A1C increments of 8.0–8.9, 9.0–9.9, and ≥10%, compared with 7.0–7.9% (reference), was 1.06 (95% CI 1.01–1.12), 1.05 (0.99–1.12), and 1.19 (1.12–1.28), respectively, and for time-averaged A1C was 1.11 (1.05–1.16), 1.36 (1.27–1.45), and 1.59 (1.46–1.72). A symmetric increase in mortality also occurred with time-averaged A1C levels in the low range (6.0–6.9%, HR 1.05 [95% CI 1.01–1.08]; 5.0–5.9%, 1.08 [1.04–1.11], and ≤5%, 1.35 [1.29–1.42]) compared with 7.0–7.9% in fully adjusted models. Adjusted all-cause death HR for time-averaged blood glucose 175–199, 200–249, 250–299, and ≥300 mg/dL, compared with 150–175 mg/dL (reference), was 1.03 (95% CI 0.99–1.07), 1.14 (1.10–1.19), 1.30 (1.23–1.37), and 1.66 (1.56–1.76), respectively. Hence, poor glycemic control (A1C ≥8% or serum glucose ≥200 mg/dL) appears to be associated with high all-cause and cardiovascular death in MHD patients. Very low glycemic levels are also associated with high mortality risk

Identifying Clinical Phenotypes of Type 1 Diabetes for the Co-Optimization of Weight and Glycemic Control

Obesity is an increasing concern in the clinical care of youth with type 1 diabetes (T1D). Standard approaches to co-optimize weight and glycemic control are challenged by profound population-level heterogeneity. Therefore, the goal of the dissertation was to apply novel analytic methods to understand heterogeneity in the co-occurrence of weight, glycemia, and underlying patterns of minute-to-minute dysglycemia among youth with T1D. Data from the SEARCH for Diabetes in Youth study were used to characterize subgroups of youth with T1D showing similar weight status and level of glycemic control as distinct ‘weight-glycemia phenotypes’ of T1D. Cross-sectional weight-glycemia phenotypes were identified at the 5+ year follow-up visit (n=1,817) using hierarchical clustering on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c), generated by reinforcement learning tree predictions. Longitudinal weight-glycemia phenotypes spanning eight years were identified with longitudinal k-means clustering using baseline and follow-up BMIz and HbA1c measures (n=570). Logistic regression modeling tested for differences in the emergence of early/subclinical diabetes complications across subgroups. Seven-day blinded continuous glucose monitoring (CGM) data from baseline of the Flexible Lifestyles Empowering Change randomized trial (n=234, 13-16 years, HbA1c 8-13%) was clustered with a neural network approach to identify subgroups of adolescents with T1D and elevated HbA1c sharing patterns in their CGM data as ‘dysglycemia phenotypes.’ We identified six cross-sectional weight-glycemia phenotypes, including four normal-weight, one overweight, and one subgroup with obesity. Subgroups showed striking differences in other sociodemographic and clinical characteristics suggesting underlying health inequity. We identified four longitudinal weight-glycemia phenotypes associated with different patterns of early/subclinical complications, providing evidence that exposure to co-occurring obesity and worsening glycemic control may accelerate the development and increase the burden of co-morbid complications. We identified three dysglycemia phenotypes with significantly different patterns in hypoglycemia, hyperglycemia, glycemic variability, and 18-month changes in HbA1c. Patient-level drivers of the dysglycemia phenotypes appear to be different from risk factors for poor glycemic control as measured by HbA1c. These studies provide pragmatic, clinically-relevant examples of how novel statistics may be applied to data from T1D to derive patient subgroups for tailored interventions to improve weight alongside glycemic control.Doctor of Philosoph