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Biomedical subjects

Roman Hovorka

Publications and source records attributed to Roman Hovorka.

2 recordsLinked to original sources

Relationship between participant-reported outcomes, residual beta cell function and metabolic parameters in youth with newly diagnosed type 1 diabetes.

AIMS/HYPOTHESIS: Clinical trials of interventions to preserve beta cell function in new-onset type 1 diabetes frequently employ participant-reported outcome measures (PROMs). However, the expected changes in PROMs scores immediately following diagnosis and their association with residual beta cell function, metabolic markers and continuous glucose monitoring (CGM) are unclear. METHODS: Repeated PROMs including Paediatric Quality of Life Inventory diabetes module (PedsQL) and hypoglycaemia fear survey (HFS) were recorded from participants aged 10-18 years with newly diagnosed type 1 diabetes and their parents in two clinical trials: CLOuD (N=97, hybrid closed loop [HCL] vs multiple daily injections [MDI]) and USTEKID (N=72, ustekinumab immunotherapy vs placebo). Scores were compared with serial mixed meal-stimulated C-peptide levels (AUC C-peptide), HbA1c and CGM data. RESULTS: PedsQL and HFS scores for children/adolescents and their parents showed wide variation between individuals but did not change substantially within individuals over the first 48 months from diagnosis. Baseline scores were highly predictive of scores at 12-48 months (p<0.001). PedsQL scores were higher (better) in those reported by children/adolescents than by their parents (p<0.01). In contrast, HFS scores were higher in parents than children (p<0.001), indicating more fear. Strong correlations were observed between child and parent scores (p<0.001). No significant improvement in these scores was detected following intervention (ustekinumab or HCL). Meta-analysis revealed modest but statistically significant associations between HbA1c and PedsQL (&#x3b2;(std)=-0.11; 95% CI -0.20, -0.03) and HFS (&#x3b2;(std)=0.11; 95% CI 0.00, 0.21), and between CGM time in range and PedsQL (&#x3b2;(std)=0.14; 95% CI 0.03, 0.26) but not HFS (&#x3b2;(std)=-0.05; 95% CI -0.16, 0.06). Beta cell function (AUC C-peptide) was strongly associated with HbA1c (&#x3b2;(std)=-0.29; 95% CI -0.39, -0.20) and CGM time in range (&#x3b2;(std)=0.41; 95% CI 0.30, 0.52). Higher beta cell function showed a trend towards better PedsQL (&#x3b2;(std)=0.11; 95% CI -0.03, 0.25) and lower HFS (&#x3b2;(std)=-0.05; 95% CI -0.17, 0.07) but this did not reach statistical significance. CONCLUSIONS/INTERPRETATION: PedsQL and HFS scores changed little during the first 48 months after diagnosis of type 1 diabetes. These scores showed modest but statistically significant associations with measures of glucose management (HbA1c and CGM time in range), whereas the relationships with residual beta cell function (C-peptide) were weaker and did not reach significance. The modest size of these effects suggests current PROMs capture only limited aspects of the clinical benefit associated with beta cell preservation. Future research should incorporate psychometric instruments that are specifically adapted for young people using modern diabetes technologies and undergoing disease-modifying therapy, to ensure outcomes are meaningfully represented in early-stage type 1 diabetes trials.

Adolescent

Evolution of the Artificial Pancreas: Components and Integration-CGMs, Insulin, and AP Systems.

The landmark Diabetes Control and Complications Trial (DCCT) showed that glucose control is critical to reducing the risk of diabetes-related complications. This chapter outlines a series of innovations and investigations that followed the DCCT, aimed at minimizing the risk of hypoglycemia while further improving glucose control. The chapter presents an example of innovations in wired enzyme technology that facilitated the movement from capillary glucose monitoring to continuous glucose monitoring (CGM) and ultimately, the first-factory calibrated CGM system. The next glycemic management innovation was to connect CGM data to an insulin pump containing an algorithm able to adjust insulin delivery based on the changing glucose levels and trends. The key features of automated insulin delivery (AID) systems, currently approved in the United States, are presented. The AID summary table includes type of pump, type and function of the insulin delivery algorithm, the data management system, and the indications for use. The next section explores the innovation of alternative routes of insulin delivery to move toward the goal of a fully automated insulin delivery system. The main trials in developing and implementing an implantable intraperitoneal programmable system are summarized. The last section explores if sensor input in addition to glucose levels such as continuous sensing of ketone, lactate, or insulin levels may provide valuable feedback to move us closer to a fully autonomous AID system. Much of this diabetes innovation and investigation work has been supported by the National Institute of Diabetes and Digestive and Kidney Diseases over that last 75 years.

Humans