Insights into the Feasibility and Acceptability of a Mobile Insulin Titration Application in Clinical Practice and Its Effects on Diabetes Outcomes.

Introduction: Insulin therapy is most effective if patients learn how to properly adjust insulin to achieve glycaemic targets. There is a need for methods and tools that can assist these processes in clinical practice. The purpose of this feasibility study was to evaluate an approach to support insulin dose adjustment in individual patients using a mobile titration application (app). Methods: A cohort of adults (N=36) with type 2 diabetes with suboptimal glycaemia who were starting basal insulin self-titration were trained by a diabetes care and education specialist to use a mobile titration app to guide adjusting insulin doses. Glycaemia, diabetes distress and patient and provider satisfaction were assessed during the first 3 months after initiating basal insulin titration using the mobile app. Results: Mean haemoglobin type A1c (HbA1c) was significantly reduced by an average of 2.1 ± 2.2% from baseline to 3 months (p<0.001). Diabetes distress significantly decreased from baseline to follow-up with scores going down (or improving) across all scales. Both patients and providers reported high levels of satisfaction and positive experiences. Conclusion: The model offers a promising solution to streamline insulin dosage adjustments to achieve specific clinical and self-management goals with high expectations for long-term benefits and warrants further investigation.

Factors Associated With Receipt of Diabetes Self-Management Education and Support for Type 2 Diabetes: Potential for a Population Health Management Approach.

BACKGROUND: Population health management approaches can help target diabetes resources like Diabetes Self-Management Education and Support (DSMES) to individuals at the highest risk of complications and poor outcomes. Little is known about patient characteristics associated with DSMES receipt since widespread uptake of telemedicine for diabetes care in 2020. METHODS: In this retrospective cohort study, we used electronic medical record (EMR) data to assess patterns of DSMES delivery from May 2020 to May 2022 among adults who used telemedicine for type 2 diabetes (T2D) endocrinology care in a large integrated health system. Multilevel regression models were used to evaluate the association of key patient characteristics with DSMES receipt. RESULTS: Of 3530 patients in the overall cohort, 401 patients (11%) received DSMES. In adjusted multivariable logistic regression, higher baseline HbA1c (odds ratios [OR] 3.10 [95% confidence interval 2.22-4.33] for HbA1c ≥9% vs <7%), insulin regimen complexity (OR 3.53 [2.59-4.80] for multiple daily injections vs no insulin), and number of noninsulin medications (OR 1.17 [1.05-1.30] per 1 additional medication) were significantly associated with receipt of DSMES, whereas rurality and area-level deprivation of patient residence were not. CONCLUSIONS: Diabetes Self-Management Education and Support remains underutilized in this cohort of adults using telemedicine to access endocrinology care for T2D. Factors contributing to clinical complexity increased the odds of receiving DSMES. These results support a potential population health management approach using EMR data, which could target DSMES resources to those at higher risk of poor outcomes. This risk-stratified approach may be even more effective now that more people can access DSMES via telemedicine in addition to in-person care.

Examining a Diabetes Self-Management Education and Support Telemedicine Model With High-Risk Patients in a Rural Community.

BACKGROUND: Ongoing support is critical to diabetes self-management education and support (DSMES) effectiveness, but difficult to realize, particularly in areas with limited resources. The objective of this feasibility study was to assess the impact of a virtual support model on diabetes outcomes and acceptability with high-risk patients with type 2 diabetes in a rural community. METHODS: In a 12-month nonrandomized trial in federally qualified health centers (FQHCs), patients with hemoglobin A1c (HbA1c) >9% were referred to the Telemedicine for Reach, Education, Access, Treatment, and Ongoing Support (TREAT-ON) program where a Diabetes Care and Education Specialist provided DSMES through videoconferencing. HbA1c change was compared in 30 patients in the intervention group (IG) to a propensity score-matched retrospective control group (CG) of patients who received in-person DSMES delivered by a DCES. Changes in HbA1c, diabetes distress, empowerment, self-care and acceptability were assessed within the intervention group (IG) between those who did and did not meet self-management goals. RESULTS: The IG experienced similar significant reductions in HbA1c as the CG. Most (64%) IG participants achieved their self-management goal. Goal attainers had a significant HbA1c decrease of 0.21% every 3 months as well as significant reduction in diabetes distress and improvement in general dietary intake. Regardless of goal attainment, IG participants reported high levels of acceptability with TREAT-ON. CONCLUSIONS: This feasibility study suggests that TREAT-ON was well-received and as effective as traditional in-person DSMES. While findings augment ample evidence regarding DSMES benefits, the TREAT-ON model offers additional advantages and provides validation for telehealth to inform future practice in reaching and supporting self-management for high-risk patients in underserved areas. TRIAL REGISTRATION: Clinicaltrials.gov, # NCT04107935.

Patterns of Telemedicine Use and Glycemic Outcomes of Endocrinology Care for Patients With Type 2 Diabetes.

Importance: Telemedicine can increase access to endocrinology care for people with type 2 diabetes (T2D), but patterns of use and outcomes of telemedicine specialty care for adults with T2D beyond initial uptake in 2020 are not known. Objective: To evaluate patterns of telemedicine use and their association with glycemic control among adults with varying clinical complexity receiving endocrinology care for T2D. Design, Setting, and Participants: Retrospective cohort study in a single large integrated US health system. Participants were adults who had a telemedicine endocrinology visit for T2D from May to October 2020. Data were analyzed from June 2022 to October 2023. Exposure: Patients were followed up through May 2022 and assigned to telemedicine-only, in-person, or mixed care (both telemedicine and in-person) cohorts according to visit modality. Main Outcomes and Measures: Multivariable regression models were used to estimate hemoglobin A1c (HbA1c) change at 12 months within each cohort and the association of factors indicating clinical complexity (insulin regimen and cardiovascular and psychological comorbidities) with HbA1c change across cohorts. Subgroup analysis was performed for patients with baseline HbA1c of 8% or higher. Results: Of 11 498 potentially eligible patients, 3778 were included in the final cohort (81 Asian participants [2%], 300 Black participants [8%], and 3332 White participants [88%]); 1182 used telemedicine only (mean [SD] age 57.4 [12.9] years; 743 female participants [63%]), 1049 used in-person care (mean [SD] age 63.0 [12.2] years; 577 female participants [55%]), and 1547 used mixed care (mean [SD] age 60.7 [12.5] years; 881 female participants [57%]). Among telemedicine-only patients, there was no significant change in adjusted HbA1c at 12 months (-0.06%; 95% CI, -0.26% to 0.14%; P = .55) while in-person and mixed cohorts had improvements of 0.37% (95% CI, 0.15% to 0.59%; P < .001) and 0.22% (95% CI, 0.07% to 0.38%; P = .004), respectively. Patients with a baseline HbA1c of 8% or higher had a similar pattern of glycemic outcomes. For patients prescribed multiple daily injections vs no insulin, the 12-month estimated change in HbA1c was 0.25% higher (95% CI, 0.02% to 0.47%; P = .03) for telemedicine vs in-person care. Comorbidities were not associated with HbA1c change in any cohort. Conclusions and Relevance: In this cohort study of adults with T2D receiving endocrinology care, patients using telemedicine alone had inferior glycemic outcomes compared with patients who used in-person or mixed care. Additional strategies may be needed to support adults with T2D who rely on telemedicine alone to access endocrinology care, especially for those with complex treatment or elevated HbA1c.

PET imaging reveals distinctive roles for different regional adipose tissue depots in systemic glucose metabolism in nonobese humans.

Excess amounts of abdominal subcutaneous (SAT) and visceral (VAT) adipose tissue (AT) are associated with insulin resistance, even in normal-weight subjects. In contrast, gluteal-femoral AT (GFAT) is hypothesized to offer protection against insulin resistance. Dynamic PET imaging studies were undertaken to examine the contributions of both metabolic activity and size (volume) of these depots in systemic glucose metabolism. Nonobese, healthy volunteers (n = 15) underwent dynamic PET imaging uptake of [¹8F]FDG at a steady-state (20 mU·m⁻²·min⁻¹) insulin infusion. PET images of tissue [¹8F]FDG activity were coregistered with MRI to derive K values for insulin-stimulated rates of fractional glucose uptake within tissue. Adipose tissue volume was calculated from DEXA and MRI. VAT had significantly higher rates of fractional glucose uptake per volume than SAT (P < 0.05) or GFAT (P < 0.01). K(GFAT) correlated positively (r = 0.67, P < 0.01) with systemic insulin sensitivity [glucose disappearance rate (R(d))] and negatively with insulin-suppressed FFA (r = -0.71, P < 0.01). SAT (r = -0.70, P < 0.01) and VAT mass (r = -0.55, P < 0.05) correlated negatively with R(d), but GFAT mass did not. We conclude that rates of fractional glucose uptake within GFAT and VAT are significantly and positively associated with systemic insulin sensitivity in nonobese subjects. Furthermore, whereas SAT and VAT amounts are confirmed to relate to systemic insulin resistance, GFAT amount is not associated with insulin resistance. These dynamic PET imaging studies indicate that both quantity and quality of specific AT depots have distinct roles in systemic insulin resistance and may help explain the metabolically obese but normal-weight phenotype.

A Risk Stratification Approach to Allocating Diabetes Education and Support Services.

The objective of this study was to describe a predictive modeling approach to risk stratify people with type 2 diabetes for diabetes self-management education and support (DSMES) services. With data from a large health system, a predictive model including age, glycated hemoglobin (HbA1c), and insulin use among other factors, was developed to assess risk of future high HbA1c. The model was retrospectively applied to a cohort of people who received DSMES over a 2-year period to assess the impact of DSMES on glycemia by risk strata. Of 6934 eligible people, 4014 (58%) were in the composite low-risk group and 2604 (38%) were in the composite high-risk group. Mean HbA1c change after DSMES was -0.38% in the low-risk group and -0.84% in the high-risk group. This analysis demonstrates the potential application of predictive modeling as one approach to target DSMES resources to people who will benefit most.

Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature Review.

BACKGROUND: Accurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population. OBJECTIVE: The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes. METHODS: Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers. RESULTS: This review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia. CONCLUSIONS: The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing.

Mechanisms of insulin resistance assessed by dynamic in-vivo positron emission tomography imaging.

PURPOSE OF REVIEW: Skeletal muscle insulin resistance is a hallmark characteristic of type 2 diabetes, although the exact causes of insulin resistance are unknown. In-vivo methods to assess mechanisms that determine insulin resistance in humans are critical to improve our understanding of insulin resistance in obesity and type 2 diabetes. In this review, we examine recent studies utilizing dynamic in-vivo PET imaging in assessing insulin resistance in humans. RECENT FINDINGS: PET imaging of glucose metabolism in vivo has revealed novel and important information about the regulation of glucose metabolism in skeletal muscle. Using dynamic PET imaging, studies have impairments in glucose metabolism at multiple sites, including delivery, phosphorylation, and transport within skeletal muscle. Impairments in glucose phosphorylation as well as glucose transport defects may play an important role in understanding the disorder of skeletal muscle insulin resistance. SUMMARY: PET imaging has great potential to yield significant and promising insight into insulin resistance in skeletal muscle. Dynamic in-vivo PET imaging can provide valuable information regarding the mechanisms and specific loci of skeletal muscle insulin resistance in humans.

Dynamic PET imaging reveals heterogeneity of skeletal muscle insulin resistance.

PURPOSE: Skeletal muscle insulin resistance (IR) often precedes hyperglycemia and type 2 diabetes. However, variability exists within different skeletal muscle types and can be influenced by 3 primary steps of control: glucose delivery, transport, and phosphorylation. We performed dynamic positron emission tomography imaging studies to determine the extent to which heterogeneity in muscle type and control of insulin action contribute to IR. METHODS: Thirteen volunteers from normal weight to obese underwent dynamic positron emission tomography imaging of [15O]H2O, [11C]3-O-methylglucose, and [18F]fluorodeoxyglucose, measuring delivery, transport, and phosphorylation rates, respectively, in soleus and tibialis anterior muscle during a hyperinsulinemic-euglycemic clamp. Subjects were classified as insulin-sensitive (IS) or insulin-resistant (IR) based on the median systemic glucose infusion rate needed to maintain euglycemia. RESULTS: In soleus, transport kinetic rates were significantly higher (P<.05) in IS (0.126±0.028 min(-1)) vs IR (0.051±0.008 min(-1)) subjects. These differences were not as evident in tibialis anterior. These differences were paralleled in overall insulin-stimulated tissue activity, higher in IS (0.017±0.001 mL·cm3·min(-1)) vs IR (0.011±0.002 mL·cm3·min(-1)) in soleus (P<.05), without significant differences in tibialis anterior. No significant differences were observed for either muscle in delivery or phosphorylation. Both muscle types displayed a control shift from an even distribution among the steps in IS to transport exerting greater control of systemic insulin sensitivity in IR. CONCLUSION: Lower glucose transport rates are the major feature underlying IR preceding type 2 diabetes, although substantial heterogeneity in insulin action across muscle types highlight the complexity of skeletal muscle IR.

Interactions among glucose delivery, transport, and phosphorylation that underlie skeletal muscle insulin resistance in obesity and type 2 Diabetes: studies with dynamic PET imaging.

Dynamic positron emission tomography (PET) imaging was performed using sequential tracer injections ([(15)O]H2O, [(11)C]3-O-methylglucose [3-OMG], and [(18)F]fluorodeoxyglucose [FDG]) to quantify, respectively, skeletal muscle tissue perfusion (glucose delivery), kinetics of bidirectional glucose transport, and glucose phosphorylation to interrogate the individual contribution and interaction among these steps in muscle insulin resistance (IR) in type 2 diabetes (T2D). PET imaging was performed in normal weight nondiabetic subjects (NW) (n = 5), obese nondiabetic subjects (OB) (n = 6), and obese subjects with T2D (n = 7) during fasting conditions and separately during a 6-h euglycemic insulin infusion at 40 mU · m(-2) · min(-1). Tissue tracer activities were derived specifically within the soleus muscle with PET images and magnetic resonance imaging. During fasting, NW, OB, and T2D subjects had similar [(11)C]3-OMG and [(18)F]FDG uptake despite group differences for tissue perfusion. During insulin-stimulated conditions, IR was clearly evident in T2D (P < 0.01), and [(18)F]FDG uptake by muscle was inversely correlated with systemic IR (P < 0.001). The increase in insulin-stimulated glucose transport was less (P < 0.01) in T2D (twofold) than in NW (sevenfold) or OB (sixfold) subjects. The fractional phosphorylation of [(18)F]FDG during insulin infusion was also significantly lower in T2D (P < 0.01). Dynamic triple-tracer PET imaging indicates that skeletal muscle IR in T2D involves a severe impairment of glucose transport and additional impairment in the efficiency of glucose phosphorylation.

Imaging of the pituitary.

In the appropriate clinical setting of pituitary hyperfunction or hypofunction, visual field deficit, or cranial nerve palsy, imaging of the pituitary is necessary. This article reviews the normal appearance of the pituitary and its surroundings, emphasizing magnetic resonance imaging. Typical and variant appearances of pituitary pathology are discussed. Because growth of adenoma into surrounding structures is important to surgical management, cavernous sinus invasion and suprasellar spread as well as adenoma mimics are illustrated. Typical examples of pituitary dysfunction from other entities that secondarily affect the gland, hypophysis, or third ventricle are discussed. Some common errors of interpretation are listed.