Devin Steenkamp’s research while affiliated with University of Massachusetts Boston and other places

This first workshop on noninvasive glucose monitoring (NIGM) was held at the Massachusetts Institute of Technology (MIT) on October 30, 2024. Six invited speakers, representing industry, academia, and clinics, gave presentations that covered (1) an overview of the NIGM technologies, (2) the state of the art in NIGM technologies, such as near-infrared (NIR), mid-infrared (IR), photoacoustic, and Raman spectroscopies, (3) minimally invasive implantable continuous glucose monitoring (CGM) sensors, and (4) a clinician’s perspective on the impact of the current CGM devices for patient care.

Objective: In hospitalized inpatients, timely administration of prandial insulin with meals is challenging. Furthermore, the glycemic impact of snacking after dinner ("bedtime snacking") without prandial insulin administration has not been previously explored. We present an analysis of the impact of delayed mealtime insulin administration and bedtime snacking on inpatient glycemic control. Research Design and Methods: We conducted a post hoc analysis from the In-Fi study: a randomized controlled trial comparing Fiasp versus insulin aspart (Novolog) in inpatients with type 2 diabetes. Glycemic outcomes were assessed using the Dexcom G6 PRO continuous glucose monitoring (CGM). We analyzed CGM and insulin administration data from 122 randomized subjects who completed the primary study protocol, which included wearing a CGM for ≥4 meals. This analysis evaluates the impact of delayed mealtime insulin administration and bedtime snacking on glucose control. Results: Four-hour postprandial time in range (TIR70-180) was 48% for insulin boluses administered before meals (n = 149) versus 24% when a meal bolus was delayed for >5 min after a meal (mean delay 58.7 min; n = 112; P < 0.001). Bedtime snacking (9 pm-12 am) was associated with significantly higher fasting glucose the next morning (35.2 mg/dL, standard error [SE] = 15.4, P = 0.026) and with a reduced overnight (9 pm-6 am) TIR70-180 (31.9%, SE = 8.06, P < 0.001), adjusting for bedtime sensor glucose. Bedtime snacking was associated with higher overnight glucose standard deviation (12.3 mg/dL, SE = 3.46, P < 0.001) and with higher overnight glucose percentage coefficient of variation (3.6%; SE = 1.7, P = 0.044), adjusting for initial bedtime sensor glucose. Conclusions: Delayed mealtime insulin administration and bedtime snacking without insulin administration are significant causes of postprandial and overnight hyperglycemia in hospitalized inpatients. Adjustments in mealtime insulin protocols, attention to food intake, and the potential inpatient adoption of technology, such as CGM and automated insulin delivery systems, are needed to address this shortcoming in inpatient diabetes care.

Background Clinical interpretation of continuous glucose monitoring (CGM) data for people without diabetes has not been well established. This study aimed to investigate concordance among CGM experts in recommending clinical follow-up for individuals without diabetes, based upon their independent review of CGM data. Methods We sent a survey out to expert clinicians ( n = 18) and asked them to evaluate 20 potentially challenging Dexcom G6 Pro CGM reports (and hemoglobin A1c [HbA1c] and fasting venous blood glucose levels) from individuals without diabetes. Clinicians reported whether they would recommend follow-up and the reasoning for their decision. We performed Fleiss Kappa interrater reliability to determine agreement among clinicians. Results More than half of expert clinicians (56-100%, but no clear consensus) recommended follow-up to individuals who spent >2% time above range (>180 mg/dL), even if HbA1c <5.7% and fasting glucose <100 mg/dL. There were no observed trends for recommending follow-up based on mean glucose or glucose management indicator. Overall, we observed poor agreement in recommendations for who should receive follow-up based on their CGM report (Fleiss Kappa = 0.36). Conclusions High discordance among expert clinicians when interpreting potentially challenging CGM reports for people without diabetes highlights the need for more research in developing normative data for people without diabetes. Future work is required to develop CGM criteria for identifying potentially high-risk individuals who may progress to prediabetes or type 2 diabetes.

Objective: Continuous glucose monitoring (CGM) is an effective tool for individuals with type 2 diabetes (T2D) on insulin. This study evaluated the effect of using CGM to reduce hyperglycemia, by focusing on food and lifestyle choices, in people with T2D not taking insulin. Methods: A 6-month randomized, prospective four-center study was conducted. The primary end point was a within-group reduction in time above range >180 mg/dL (TAR180) at 3 months. Participants were asked not to make diabetes medication changes in the first 3 months. Seventy-two adults not on insulin or sulfonylurea therapy, with glycated hemoglobin (HbA1c) 7.5%-12%, were randomized to use CGM alone (n = 31) or CGM plus a food logging app (n = 41) to aid diabetes management. Participants attended guided education visits. Differences in CGM metrics, HbA1c, and body weight were compared. Results: The CGM alone group decreased TAR180 from 55% at baseline to 27% at 3 months (P < 0.001) and 21% at 6 months (P < 0.001); the CGM plus food logging app group decreased TAR180 from 53% at baseline to 30% at both 3 and 6 months (P < 0.001 for both). For all participants, time in range (70-180 mg/dL) increased from 46% at baseline to 71% at 3 months (P < 0.001) and to 72% at 6 months (P < 0.001). HbA1c and weight were reduced by 1.3% (P < 0.001) and 7 pounds (lbs.) (P < 0.001) for all participants at 6 months. Conclusion: People with T2D not taking insulin showed large, clinically significant improvements in CGM metrics and HbA1c when using either CGM alone or with a food logging app. This occurred with a near absence of medication changes in the first 3 months and were therefore likely due to changes in food and/or lifestyle choices.

OBJECTIVE To evaluate a regimen of inhaled Technosphere insulin (TI) plus insulin degludec in adults with type 1 diabetes, who prestudy were predominately using either an automated insulin delivery (AID) system or multiple daily insulin injections (MDI) with continuous glucose monitoring. RESEARCH DESIGN AND METHODS At 19 sites, adults with type 1 diabetes were randomly assigned to TI plus insulin degludec (N = 62) or usual care (UC) with continuation of prestudy insulin delivery method (N = 61) for 17 weeks. RESULTS Prestudy, AID was used by 48% and MDI by 45%. Mean ± SD HbA1c was 7.57% ± 0.97% at baseline and 7.62% ± 1.06% at 17 weeks in the TI group and 7.59% ± 0.80% and 7.54% ± 0.77%, respectively, in the UC group (adjusted difference 0.11%, 95% CI −0.10 to 0.33, P value for noninferiority = 0.01). HbA1c improved from baseline to 17 weeks by >0.5% (5.5 mmol/mol) in 12 (21%) in the TI group and in 3 (5%) in the UC group and worsened by >0.5% (5.5 mmol/mol) in 15 (26%) in the TI group and in 2 (3%) in the UC group. The most common TI side effect was a brief cough; eight participants discontinued TI due to side effects. CONCLUSIONS In adults with type 1 diabetes, HbA1c after 17 weeks with a regimen of TI and degludec was noninferior to UC, which consisted predominately of either AID or MDI. TI should be considered an option for people with type 1 diabetes, particularly those who are motivated to further reduce postprandial hyperglycemia.

As the learning health network known as the T1D Exchange Quality Improvement Collaborative (T1DX-QI) has expanded on a significant scale to provide care for more than 120,000 people since its inception in 2016, assessing the quality improvement (QI) culture and monitoring clinical outcome improvements of participating centers has become vital to understanding the network’s progress and success. Centers participating in the T1DX-QI complete regular QI culture self-assessments to quantify four evidence-based areas of culture: QI team structure, QI foundation, QI capacity, and QI success. This study builds on a previous baseline analysis to demonstrate self-reported improvements in T1DX-QI centers’ QI culture from 2021 to 2022 and summarizes QI successes experienced by participating centers.