Inpatient and Outpatient Technologies to Assist in the Management of Insulin Dosing.

Several new technologies use computer algorithms to analyze a person's blood glucose response to insulin treatment, calculate the person's next recommended insulin dose, advise the person regarding when to check blood glucose next, and provide alerts regarding glucose control for the individual patient or across a hospital system. This article reviews U.S. Food and Drug Administration (FDA)-approved products designed to help manage insulin dosing for inpatients, as well as those available to provide people with insulin-requiring diabetes support in making adjustments to their basal and/or mealtime insulin doses. Many of these products have a provider interface that allows for remote monitoring of patients' glucose readings and insulin doses. By alleviating some of the burdens of insulin initiation and dose adjustment, these products may facilitate improved glycemic management and patient outcomes.

Redesigning ambulatory care management for uncontrolled type 2 diabetes: a prospective cohort study of the impact of a Boot Camp model on outcomes.

Objective: Type 2 diabetes care management (DCM) is challenging. Few studies report meaningful improvements in clinical care settings, warranting DCM redesign. We developed a Boot Camp to provide timely, patient-centered, technology-enabled DCM. Impact on hemoglobin A1c (HbA1c), emergency department (ED) visits and hospitalizations among adults with uncontrolled type 2 diabetes were examined. Research design and methods: The intervention was designed using the Practical Robust Implementation and Sustainability Model to embed elements of the chronic care model. Adults with HbA1c>9% (75 mmol/mol) enrolled between November 2014 and November 2017 received diabetes education and medication management by diabetes educators and nurse practitioners via initial clinic and subsequent weekly virtual visits, facilitated by near-real-time blood glucose transmission for 90 days. HbA1c and risk for ED visits and hospitalizations at 90 days, and potential savings from reducing avoidable medical utilizations were examined. Boot Camp completers were compared with concurrent, propensity-matched chart controls receiving usual DCM in primary care practices. Results: A cohort of 366 Boot Camp participants plus 366 controls was analyzed. Participants were 79% African-American, 63% female and 59% Medicare-insured or Medicaid-insured and mean age 56 years. Baseline mean HbA1c for cases and controls was 11.2% (99 mmol/mol) and 11.3% (100 mmol/mol), respectively. At 90 days, HbA1c was 8.1% (65 mmol/mol) and 9.9% (85 mmol/mol), p<0.001, respectively. Risk for 90-day all-cause hospitalizations decreased 77% for participants and increased 58% for controls, p=0.036. Mean potential for monetization of US$3086 annually per participant for averted hospitalizations were calculated. Conclusions: Redesigning diabetes care management using a pragmatic technology-enabled approach supported translation of evidence-based best practices across a mixed-payer regional healthcare system. Diabetes educators successfully participated in medication initiation and titration. Improvement in glycemic control, reduction in hospitalizations and potential for monetization was demonstrated in a high-risk cohort of adults with uncontrolled type 2 diabetes. Trial registration number: NCT02925312.

Genetic influences on blood pressure response to the cold pressor test: results from the Heredity and Phenotype Intervention Heart Study.

OBJECTIVES: Blood pressure (BP) response to the cold pressor test (CPT) has been found to predict the development of hypertension and cardiovascular disease in prospective studies. The determinants of BP response to the CPT, including the role of genetic factors, however, are largely unknown. Additionally, to our knowledge, no study has examined the genetics of BP recovery from the CPT, including whether shared genetic factors influence both reactivity and recovery. METHODS: As part of the Heredity and Phenotype Intervention Heart Study, we administered a 2.5 min hand CPT to 835 participants from 18 extended Amish families. We estimated the heritability of BP reactivity and recovery (measured by the incremental area under the curve) and the genetic correlations between baseline, reactivity, and recovery BP phenotypes. RESULTS: After adjusting for relevant covariates, including baseline BP, the heritability estimates for both systolic BP (SBP) and diastolic BP (DBP) reactivity and recovery differed significantly from zero (P < 0.01), with 12-25% of the total variation in BP response attributable to additive genetic effects. The genetic correlations between baseline DBP and response phenotypes were not significantly different from zero, whereas the genetic correlation between DBP reactivity and recovery (0.74) was significantly different from zero and 1 (P < 0.005). The genetic correlation between SBP reactivity and recovery was similar (0.81; P < 0.05). CONCLUSION: We conclude that, independent of baseline BP, BP response to CPT is heritable, and that both shared and unshared genetic factors influence BP reactivity and recovery, thus stressing the importance of identifying genetic variants that influence both traits.

Associations between genetic variants in the NOS1AP (CAPON) gene and cardiac repolarization in the old order Amish.

BACKGROUND: Through a genome-wide association study, we discovered an association of the electrocardiographic QT interval with polymorphisms in the NOS1AP (CAPON) gene. The purpose of the current study was to replicate this association in the Old Order Amish. METHODS: Four NOS1AP SNPs were selected that captured all major haplotypes in the region of interest ( approximately 120 kb segment). Genotyping was completed in 763 subjects from the Heredity and Phenotype Intervention (HAPI) Heart Study. Association analyses were performed using a variance components methodology, accounting for relatedness of individuals. RESULTS: Heritability of the QT interval was 0.50 +/- 0.09 (p = 1.9 x 10(-9)). All four SNPs were common with a high degree of correlation between SNPs. Two of the four SNPs (pairwise r(2) = 0.86) were significantly associated with variation in adjusted QT interval (rs1415262, p = 0.02 and rs10494366, p = 0.006, additive models for both). SNP rs10494366 explained 0.9% of QT interval variability, with an average genetic effect of 6.1 ms. Haplotypes that contained the minor allele for rs10494366 were associated with longer QT interval. CONCLUSIONS: This study provides further evidence that NOS1AP variants influence QT interval and further validates the utility of genome-wide association studies, a relatively new approach to gene discovery.