Use of systems thinking and adapted group model building methods to understand patterns of technology use among older adults with type 1 diabetes: a preliminary process evaluation.

BACKGROUND: A growing number of older adults (ages 65+) live with Type 1 diabetes. Simultaneously, technologies such as continuous glucose monitoring (CGM) have become standard of care. There is thus a need to understand better the complex dynamics that promote use of CGM (and other care innovations) over time in this age group. Our aim was to adapt methods from systems thinking, specifically a participatory approach to system dynamics modeling called group model building (GMB), to model the complex experiences that may underlie different trajectories of CGM use among this population. Herein, we report on the feasibility, strengths, and limitations of this methodology. METHODS: We conducted a series of GMB workshops and validation interviews to collect data in the form of questionnaires, diagrams, and recordings of group discussion. Data were integrated into a conceptual diagram of the "system" of factors associated with uptake and use of CGM over time. We evaluate the feasibility of each aspect of the study, including the teaching of systems thinking to older adult participants. We collected participant feedback on positive aspects of their experiences and areas for improvement. RESULTS: We completed nine GMB workshops with older adults and their caregivers (N = 33). Each three-hour in-person workshop comprised: (1) questionnaires; (2) the GMB session, including both didactic components and structured activities; and (3) a brief focus group discussion. Within the GMB session, individual drawing activities proved to be the most challenging for participants, while group activities and discussion of relevant dynamics over time for illustrative (i.e., realistic but not real) patients yielded rich engagement and sufficient information for system diagramming. Study participants liked the opportunity to share experiences with peers, learning and enhancing their knowledge, peer support, age-specific discussions, the workshop pace and structure, and the systems thinking framework. Participants gave mixed feedback on the workshop duration. CONCLUSIONS: The study demonstrates preliminary feasibility, acceptability, and the value of GMB for engaging older adults about key determinants of complex health behaviors over time. To our knowledge, few studies have extended participatory systems science methods to older adult stakeholders. Future studies may utilize this methodology to inform novel approaches for supporting health across the lifespan.

Correlation Between the Glycemia Risk Index and Longitudinal Hemoglobin A1c in Children and Young Adults With Type 1 Diabetes.

BACKGROUND: The glycemia risk index (GRI) is a composite metric developed and used to estimate quality of glycemia in adults with diabetes who use continuous glucose monitor (CGM) devices. In a cohort of youth with type 1 diabetes (T1D), we examined the utility of the GRI for evaluating quality of glycemia between clinic visits by analyzing correlations between the GRI and longitudinal glycated hemoglobin A1c (HbA1c) measures. METHOD: Using electronic health records and CGM data, we conducted a retrospective cohort study to analyze the relationship between the GRI and longitudinal HbA1c measures in youth (T1D duration ≥1 year; ≥50% CGM wear time) receiving care from a Midwest pediatric diabetes clinic network (March 2016 to May 2022). Furthermore, we analyzed correlations between HbA1c and the GRI high and low components, which reflect time spent with high/very high and low/very low glucose, respectively. RESULTS: In this cohort of 719 youth (aged = 2.5-18.0 years [median = 13.4; interquartile range [IQR] = 5.2]; 50.5% male; 83.7% non-Hispanic White; 68.0% commercial insurance), baseline GRI scores positively correlated with HbA1c measures at baseline and 3, 6, 9, and 12 months later (r = 0.68, 0.65, 0.60, 0.57, and 0.52, respectively). At all time points, strong positive correlations existed between HbA1c and time spent in hyperglycemia. Substantially weaker, negative correlations existed between HbA1c and time spent in hypoglycemia. CONCLUSIONS: In youth with T1D, the GRI may be useful for evaluating quality of glycemia between scheduled clinic visits. Additional CGM-derived metrics are needed to quantify risk for hypoglycemia in this population.

Receipt of Diabetes Specialty Care and Management Services by Older Adults With Diabetes in the United States, 2015-2019: An Analysis of Medicare Fee-for-Service Claims.

OBJECTIVE: We characterized the receipt of diabetes specialty care and management services among older adults with diabetes. RESEARCH DESIGN AND METHODS: Using a 20% random sample of fee-for-service Medicare beneficiaries aged ≥65 years, we analyzed cohorts of type 1 diabetes (T1D) or type 2 diabetes (T2D) with history of severe hypoglycemia (HoH), and all other T2D annually from 2015 to 2019. Outcomes were receipt of office-based endocrinology care, diabetes education, outpatient diabetes health services, excluding those provided in primary care, and any of the aforementioned services. RESULTS: In the T1D cohort, receipt of endocrinology care and any service increased from 25.9% and 29.2% in 2015 to 32.7% and 37.4% in 2019, respectively. In the T2D with HoH cohort, receipt of endocrinology care and any service was 13.9% and 16.4% in 2015, with minimal increases. Age, race/ethnicity, residential setting, and income were associated with receiving care. CONCLUSIONS: These findings suggest that many older adults may not receive specialty diabetes care and underscore health disparities.

Nutritional Status, Dietary Intake, and Nutrition-Related Interventions Among Older Adults With Type 1 Diabetes: A Systematic Review and Call for More Evidence Toward Clinical Guidelines.

There is an emerging population of older adults (≥65 years) living with type 1 diabetes. Optimizing health through nutrition during this life stage is challenged by multiple and ongoing changes in diabetes management, comorbidities, and lifestyle factors. There is a need to understand nutritional status, dietary intake, and nutrition-related interventions that may maximize well-being throughout the life span in type 1 diabetes, in addition to nutrition recommendations from clinical guidelines and consensus reports. Three reviewers used Cochrane guidelines to screen original research (January 1993-2023) and guidelines (2012-2023) in two databases (MEDLINE and CENTRAL) to characterize nutrition evidence in this population. We found limited original research explicitly focused on nutrition and diet in adults ≥65 years of age with type 1 diabetes (six experimental studies, five observational studies) and meta-analyses/reviews (one scoping review), since in the majority of analyses individuals ≥65 years of age were combined with those age ≥18 years, with diverse diabetes durations, and also individuals with type 1 and type 2 diabetes were combined. Further, existing clinical guidelines (n = 10) lacked specificity and evidence to guide clinical practice and self-management behaviors in this population. From a scientific perspective, little is known about nutrition and diet among older adults with type 1 diabetes, including baseline nutrition status, dietary intake and eating behaviors, and the impact of nutrition interventions on key clinical and patient-oriented outcomes. This likely reflects the population's recent emergence and unique considerations. Addressing these gaps is foundational to developing evidence-based nutrition practices and guidelines for older adults living with type 1 diabetes.

Association of Premorbid GLP-1RA and SGLT-2i Prescription Alone and in Combination with COVID-19 Severity.

INTRODUCTION: People with type 2 diabetes are at heightened risk for severe outcomes related to COVID-19 infection, including hospitalization, intensive care unit admission, and mortality. This study was designed to examine the impact of premorbid use of glucagon-like peptide-1 receptor agonist (GLP-1RA) monotherapy, sodium-glucose cotransporter-2 inhibitor (SGLT-2i) monotherapy, and concomitant GLP1-RA/SGLT-2i therapy on the severity of outcomes in individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. METHODS: Utilizing observational data from the National COVID Cohort Collaborative through September 2022, we compared outcomes in 78,806 individuals with a prescription of GLP-1RA and SGLT-2i versus a prescription of dipeptidyl peptidase 4 inhibitors (DPP-4i) within 24 months of a positive SARS-CoV-2 PCR test. We also compared concomitant GLP-1RA/SGLT-2i therapy to GLP-1RA and SGLT-2i monotherapy. The primary outcome was 60-day mortality, measured from the positive test date. Secondary outcomes included emergency room (ER) visits, hospitalization, and mechanical ventilation within 14 days. Using a super learner approach and accounting for baseline characteristics, associations were quantified with odds ratios (OR) estimated with targeted maximum likelihood estimation (TMLE). RESULTS: Use of GLP-1RA (OR 0.64, 95% confidence interval [CI] 0.56-0.72) and SGLT-2i (OR 0.62, 95% CI 0.57-0.68) were associated with lower odds of 60-day mortality compared to DPP-4i use. Additionally, the OR of ER visits and hospitalizations were similarly reduced with GLP1-RA and SGLT-2i use. Concomitant GLP-1RA/SGLT-2i use showed similar odds of 60-day mortality when compared to GLP-1RA or SGLT-2i use alone (OR 0.92, 95% CI 0.81-1.05 and OR 0.88, 95% CI 0.76-1.01, respectively). However, lower OR of all secondary outcomes were associated with concomitant GLP-1RA/SGLT-2i use when compared to SGLT-2i use alone. CONCLUSION: Among adults who tested positive for SARS-CoV-2, premorbid use of either GLP-1RA or SGLT-2i is associated with lower odds of mortality compared to DPP-4i. Furthermore, concomitant use of GLP-1RA and SGLT-2i is linked to lower odds of other severe COVID-19 outcomes, including ER visits, hospitalizations, and mechanical ventilation, compared to SGLT-2i use alone. Graphical abstract available for this article.

Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case-control study.

INTRODUCTION: Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures. RESEARCH DESIGN AND METHODS: Data from a case-control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics. RESULTS: Data from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score. CONCLUSIONS: Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics.

Machine Learning and Health Science Research: Tutorial.

Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths and limitations of ML and to facilitate its integration into their studies, we present here a guideline for integrating ML into an analysis through a structured framework, covering steps from framing a research question to study design and analysis techniques for specialized data types.

Supporting the 'lived expertise' of older adults with type 1 diabetes: An applied focus group analysis to characterize barriers, facilitators, and strategies for self-management in a growing and understudied population.

INTRODUCTION: There is a growing number of older adults (≥65 years) who live with type 1 diabetes. We qualitatively explored experiences and perspectives regarding type 1 diabetes self-management and treatment decisions among older adults, focusing on adopting care advances such as continuous glucose monitoring (CGM). METHODS: Among a clinic-based sample of older adults ≥65 years with type 1 diabetes, we conducted a series of literature and expert informed focus groups with structured discussion activities. Groups were transcribed followed by inductive coding, theme identification, and inference verification. Medical records and surveys added clinical information. RESULTS: Twenty nine older adults (age 73.4 ± 4.5 years; 86% CGM users) and four caregivers (age 73.3 ± 2.9 years) participated. Participants were 58% female and 82% non-Hispanic White. Analysis revealed themes related to attitudes, behaviours, and experiences, as well as interpersonal and contextual factors that shape self-management and outcomes. These factors and their interactions drive variability in diabetes outcomes and optimal treatment strategies between individuals as well as within individuals over time (i.e. with ageing). Participants proposed strategies to address these factors: regular, holistic needs assessments to match people with effective self-care approaches and adapt them over the lifespan; longitudinal support (e.g., education, tactical help, sharing and validating experiences); tailored education and skills training; and leveraging of caregivers, family, and peers as resources. CONCLUSIONS: Our study of what influences self-management decisions and technology adoption among older adults with type 1 diabetes underscores the importance of ongoing assessments to address dynamic age-specific needs, as well as individualized multi-faceted support that integrates peers and caregivers.

A conceptual model of the continuous glucose monitoring integration process for older adults with diabetes developed using participatory systems science methods.

AIMS: Continuous glucose monitoring (CGM) use remains low in older adults. We aimed to develop a conceptual model of CGM integration among older adults with type 1 and type 2 diabetes. METHODS: We previously engaged older adults with type 1 diabetes using participatory system science methods to develop a model of the system of factors that shape CGM integration. To validate and expand the model, we conducted semi-structured interviews with 17 older adults with type 1 and type 2 diabetes and 3 caregivers. Vignettes representing each integration phase were used to elicit outcomes and strategies to support CGM use. Data were analyzed using team-based causal loop diagraming. RESULTS: The model includes six phases spanning (1) CGM uptake; (2) device set-up; acquisition of (3) belief in oneself to use CGM effectively; (4) belief that CGM is preferable to blood glucose monitoring; (5) belief in future CGM benefits CGM; and (6) development of a sense of reliance on CGM. Causal loop diagrams visualize factors and feedback loops shaping outcomes at each phase. Participants proposed support strategies spanning clinical, educational, and behavioral interventions. CONCLUSIONS: The model underscores the complex transition of learning new technology and provides opportunities for tailored support for older adults.

Week-long normoglycaemia in diabetic mice and minipigs via a subcutaneous dose of a glucose-responsive insulin complex.

Glucose-responsive formulations of insulin can increase its therapeutic index and reduce the burden of its administration. However, it has been difficult to develop single-dosage formulations that can release insulin in both a sustained and glucose-responsive manner. Here we report the development of a subcutaneously injected glucose-responsive formulation that nearly does not trigger the formation of a fibrous capsule and that leads to week-long normoglycaemia and negligible hypoglycaemia in mice and minipigs with type 1 diabetes. The formulation consists of gluconic acid-modified recombinant human insulin binding tightly to poly-L-lysine modified by 4-carboxy-3-fluorophenylboronic acid via glucose-responsive phenylboronic acid-diol complexation and electrostatic attraction. When the insulin complex is exposed to high glucose concentrations, the phenylboronic acid moieties of the polymers bind rapidly to glucose, breaking the complexation and reducing the polymers' positive charge density, which promotes the release of insulin. The therapeutic performance of this long-acting single-dose formulation supports its further evaluation and clinical translational studies.

Treatment effect heterogeneity following type 2 diabetes treatment with GLP1-receptor agonists and SGLT2-inhibitors: a systematic review.

BACKGROUND: A precision medicine approach in type 2 diabetes requires the identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. METHODS: We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. RESULTS: Here we show that the majority of included papers have methodological limitations precluding robust assessment of treatment effect heterogeneity. For SGLT2-inhibitors, multiple observational studies suggest lower renal function as a predictor of lesser glycaemic response, while markers of reduced insulin secretion predict lesser glycaemic response with GLP1-receptor agonists. For both therapies, multiple post-hoc analyses of randomized control trials (including trial meta-analysis) identify minimal clinically relevant treatment effect heterogeneity for cardiovascular and renal outcomes. CONCLUSIONS: Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care.

This study reviews the available evidence on which patient features (such as age, sex, and blood test results) are associated with different outcomes for two recently introduced type 2 diabetes medications: SGLT2-inhibitors and GLP1-receptor agonists. Understanding what individual characteristics are associated with different response patterns may help clinical providers and people living with diabetes make more informed decisions about which type 2 diabetes treatments will work best for an individual. We focus on three outcomes: blood glucose levels (raised blood glucose is the primary symptom of diabetes and a primary aim of diabetes treatment is to lower this), heart disease, and kidney disease. We identified some potential factors that reduce effects on blood glucose levels, including poorer kidney function for SGLT2-inhibitors and lower production of the glucose-lowering hormone insulin for GLP1-receptor agonists. We did not identify clear factors that alter heart and kidney disease outcomes for either medication. Most of the studies had limitations, meaning more research is needed to fully understand the factors that influence treatment outcomes in type 2 diabetes.

Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine.

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

Severe (level 3) hypoglycaemia occurrence in a real-world cohort of adults with type 1 or 2 diabetes mellitus (iNPHORM, United States).

AIMS: Among adults with insulin- and/or secretagogue-treated diabetes in the United States, very little is known about the real-world descriptive epidemiology of iatrogenic severe (level 3) hypoglycaemia. Addressing this gap, we collected primary, longitudinal data to quantify the absolute frequency of events as well as incidence rates and proportions. MATERIALS AND METHODS: iNPHORM is a US-wide, 12-month ambidirectional panel survey (2020-2021). Adults with type 1 diabetes mellitus (T1DM) or insulin- and/or secretagogue-treated type 2 diabetes mellitus (T2DM) were recruited from a probability-based internet panel. Participants completing ≥1 follow-up questionnaire(s) were analysed. RESULTS: Among 978 respondents [T1DM 17%; mean age 51 (SD 14.3) years; male: 49.6%], 63% of level 3 events were treated outside the health care system (e.g. by family/friend/colleague), and <5% required hospitalization. Following the 12-month prospective period, one-third of individuals reported ≥1 event(s) [T1DM 44.2% (95% CI 36.8%-51.8%); T2DM 30.8% (95% CI 28.7%-35.1%), p = .0404, α = 0.0007]; and the incidence rate was 5.01 (95% CI 4.15-6.05) events per person-year (EPPY) [T1DM 3.57 (95% CI 2.49-5.11) EPPY; T2DM 5.29 (95% CI 4.26-6.57) EPPY, p = .1352, α = 0.0007]. Level 3 hypoglycaemia requiring non-transport emergency medical services was more common in T2DM than T1DM (p < .0001, α = 0.0016). In total, >90% of events were experienced by <15% of participants. CONCLUSIONS: iNPHORM is one of the first long-term, prospective US-based investigations on level 3 hypoglycaemia epidemiology. Our results underscore the importance of participant-reported data to ascertain its burden. Events were alarmingly frequent, irrespective of diabetes type, and concentrated in a small subsample.

Extending an Antiracism Lens to the Implementation of Precision Public Health Interventions.

Precision public health holds promise to improve disease prevention and health promotion strategies, allowing the right intervention to be delivered to the right population at the right time. Growing concerns underscore the potential for precision-based approaches to exacerbate health disparities by relying on biased data inputs and recapitulating existing access inequities. To achieve its full potential, precision public health must focus on addressing social and structural drivers of health and prominently incorporate equity-related concerns, particularly with respect to race and ethnicity. In this article, we discuss how an antiracism lens could be applied to reduce health disparities and health inequities through equity-informed research, implementation, and evaluation of precision public health interventions. (Am J Public Health. 2023;113(11):1210-1218. https://doi.org/10.2105/AJPH.2023.307386).

The Growing Role of Technology in the Care of Older Adults With Diabetes.

The integration of technologies such as continuous glucose monitors, insulin pumps, and smart pens into diabetes management has the potential to support the transformation of health care services that provide a higher quality of diabetes care, lower costs and administrative burdens, and greater empowerment for people with diabetes and their caregivers. Among people with diabetes, older adults are a distinct subpopulation in terms of their clinical heterogeneity, care priorities, and technology integration. The scientific evidence and clinical experience with these technologies among older adults are growing but are still modest. In this review, we describe the current knowledge regarding the impact of technology in older adults with diabetes, identify major barriers to the use of existing and emerging technologies, describe areas of care that could be optimized by technology, and identify areas for future research to fulfill the potential promise of evidence-based technology integrated into care for this important population.

Statistically Adjusting for Wear Time in Randomized Trials of Continuous Glucose Monitors as a Complement to Intent-to-Treat and As-Treated Analyses: Application and Evaluation in Two Trials.

Background: Randomized trials of continuous glucose monitoring (CGM) often estimate treatment effects using standard intent-to-treat (ITT) analyses. We explored how adjusting for CGM-measured wear time could complement existing analyses by estimating the effect of receiving and using CGM 100% of the time. Methods: We analyzed data from two 6-month CGM trials spanning diverse ages, the Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) and CGM Intervention in Teens and Young Adults with Type 1 Diabetes (CITY) Studies. To adjust the ITT estimates for CGM use, as measured by wear time, we used an instrumental variable (IV) approach with the treatment assignment as an instrument. Outcomes included (1) time in range ([TIR] 70-180 mg/dL), time below range ([TBR] ≤70 mg/dL), and time above range ([TAR] ≥250 mg/dL). We estimated outcomes based on CGM use in the last 28 days of the trial and the full trial. Findings: In the WISDM study, the wear time rates over the 28-day window and full trial period were 93.1% (standard deviation [SD]: 20.4) and 94.5% (SD: 11.9), respectively. In the CITY study, the wear time rates over the 28-day window and full trial period were 82.2% (SD: 26.5) and 83.1% (SD: 21.5), respectively. IV-based estimates for the effect of CGM on TIR, TBR, and TAR suggested greater improvements in glycemic management than the ITT counterparts. The magnitude of the differences was proportional to the level of wear time observed in the trials. Interpretation: In trials of CGM use, the effect of variable wear time is non-negligible. By providing adherence-adjusted estimates, the IV approach may have additional utility for individual clinical decision-making.