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- Tobias, Deirdre K, et al.
(author)
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Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine
- 2023
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In: Nature Medicine. - 1546-170X. ; 29:10, s. 2438-2457
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Research review (peer-reviewed)abstract
- 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.
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- Battaglia, Manuela, et al.
(author)
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Introducing the Endotype Concept to Address the Challenge of Disease Heterogeneity in Type 1 Diabetes
- 2020
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In: Diabetes Care. - : American Diabetes Association. - 1935-5548 .- 0149-5992. ; 43:1, s. 5-12
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Journal article (peer-reviewed)abstract
- The clinical diagnosis of new-onset type 1 diabetes has, for many years, been considered relatively straightforward. Recently, however, there is increasing awareness that within this single clinical phenotype exists considerable heterogeneity: disease onset spans the complete age range; genetic susceptibility is complex; rates of progression differ markedly, as does insulin secretory capacity; and complication rates, glycemic control, and therapeutic intervention efficacy vary widely. Mechanistic and immunopathological studies typically show considerable patchiness across subjects, undermining conclusions regarding disease pathways. Without better understanding, type 1 diabetes heterogeneity represents a major barrier both to deciphering pathogenesis and to the translational effort of designing, conducting, and interpreting clinical trials of disease-modifying agents. This realization comes during a period of unprecedented change in clinical medicine, with increasing emphasis on greater individualization and precision. For complex disorders such as type 1 diabetes, the option of maintaining the "single disease" approach appears untenable, as does the notion of individualizing each single patient's care, obliging us to conceptualize type 1 diabetes less in terms of phenotypes (observable characteristics) and more in terms of disease endotypes (underlying biological mechanisms). Here, we provide our view on an approach to dissect heterogeneity in type 1 diabetes. Using lessons from other diseases and the data gathered to date, we aim to delineate a roadmap through which the field can incorporate the endotype concept into laboratory and clinical practice. We predict that such an effort will accelerate the implementation of precision medicine and has the potential for impact on our approach to translational research, trial design, and clinical management.
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- Bediaga, Naiara G, et al.
(author)
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Simplifying prediction of disease progression in pre-symptomatic type 1 diabetes using a single blood sample
- 2021
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In: Diabetologia. - : Springer Science and Business Media LLC. - 1432-0428 .- 0012-186X. ; 64:11, s. 2432-2444
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Journal article (peer-reviewed)abstract
- AIMS/HYPOTHESIS: Accurate prediction of disease progression in individuals with pre-symptomatic type 1 diabetes has potential to prevent ketoacidosis and accelerate development of disease-modifying therapies. Current tools for predicting risk require multiple blood samples taken during an OGTT. Our aim was to develop and validate a simpler tool based on a single blood draw.METHODS: Models to predict disease progression using a single OGTT time point (0, 30, 60, 90 or 120 min) were developed using TrialNet data collected from relatives with type 1 diabetes and validated in independent populations at high genetic risk of type 1 diabetes (TrialNet, Diabetes Prevention Trial-Type 1, The Environmental Determinants of Diabetes in the Young [1]) and in a general population of Bavarian children who participated in Fr1da.RESULTS: Cox proportional hazards models combining plasma glucose, C-peptide, sex, age, BMI, HbA1c and insulinoma antigen-2 autoantibody status predicted disease progression in all populations. In TrialNet, the AUC for receiver operating characteristic curves for models named M60, M90 and M120, based on sampling at 60, 90 and 120 min, was 0.760, 0.761 and 0.745, respectively. These were not significantly different from the AUC of 0.760 for the gold standard Diabetes Prevention Trial Risk Score, which requires five OGTT blood samples. In TEDDY, where only 120 min blood sampling had been performed, the M120 AUC was 0.865. In Fr1da, the M120 AUC of 0.742 was significantly greater than the M60 AUC of 0.615.CONCLUSIONS/INTERPRETATION: Prediction models based on a single OGTT blood draw accurately predict disease progression from stage 1 or 2 to stage 3 type 1 diabetes. The operational simplicity of M120, its validity across different at-risk populations and the requirement for 120 min sampling to stage type 1 diabetes suggest M120 could be readily applied to decrease the cost and complexity of risk stratification.
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