Browsing by Author "Petrelli, Alessandra"

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    Introducing the Endotype Concept to Address the Challenge of Disease Heterogeneity in Type 1 Diabetes
    (American Diabetes Association, 2020-01) Battaglia, Manuela; Ahmed, Simi; Anderson, Mark S.; Atkinson, Mark A.; Becker, Dorothy; Bingley, Polly J.; Bosi, Emanuele; Brusko, Todd M.; DiMeglio, Linda A.; Evans-Molina, Carmella; Gitelman, Stephen E.; Greenbaum, Carla J.; Gottlieb, Peter A.; Herold, Kevan C.; Hessner, Martin J.; Knip, Mikael; Jacobsen, Laura; Krischer, Jeffrey P.; Long, S. Alice; Lundgren, Markus; McKinney, Eoin F.; Morgan, Noel G.; Oram, Richard A.; Pastinen, Tomi; Peters, Michael C.; Petrelli, Alessandra; Qian, Xiaoning; Redondo, Maria J.; Roep, Bart O.; Schatz, Desmond; Skibinski, David; Peakman, Mark; Pediatrics, School of Medicine
    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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    The Disposition Index in Autoantibody-Positive Individuals at Risk for Type 1 Diabetes
    (American Diabetes Association, 2025) Ismail, Heba M.; Cuthbertson, David; Galderisi, Alfonso; Libman, Ingrid; Jacobsen, Laura; Moran, Antoinette; Petrelli, Alessandra; Atkinson, Mark; Redondo, Maria J.; Hannon, Tamara; Mather, Kieren J.; Sosenko, Jay M.; Pediatrics, School of Medicine
    Since little is known about the disposition index (DI) in autoantibody-positive individuals, we have assessed whether DI has a similar association between insulin secretion and sensitivity to the association observed in other populations. In TrialNet Pathway to Prevention (TNPTP; n = 6,620) and Diabetes Prevention Trial-Type 1 (DPT-1; n = 704) study participants, two secretion-sensitivity pairs, each representing a DI, were analyzed cross-sectionally at baseline: area under the curve (AUC) C-peptide/AUC glucose (AUC ratio) and Matsuda index (MI) from TNPTP oral glucose tolerance tests (oral DI), first-phase insulin response (FPIR), and 1 / fasting insulin (1/FI) from DPT-1 from intravenous glucose tolerance tests (DI). Participants were followed for progression to type 1 diabetes (T1D). Within the normal and diabetes glucose ranges, associations of AUC ratio with MI in TNPTP and FPIR with 1/FI in DPT-1 had inverse curvilinear patterns with convexities to the origin. After logarithmic transformations to linearize the secretion and sensitivity measures, the inverse slope was steeper for the diabetes range (P < 0.0001). In a Cox regression model including the AUC ratio and MI as variables and another model including FPIR and 1/FI, the interaction terms of secretion × sensitivity (i.e., the DI/oral DI) predicted stage 3 T1D in both (P < 0.0001). The DI remained significantly predictive (P < 0.0001) when the DPT-1 risk score was added as a covariate in regression models. In autoantibody-positive populations, insulin secretion is inversely related to sensitivity in a quasi-hyperbolic relationship in normal and diabetes ranges of glucose. The DI can be represented by a statistical and physiologic interaction between secretion and sensitivity that is predictive of stage 3 T1D. Article highlights: Little is known about the relationship between secretion and sensitivity in individuals at risk for type 1 diabetes (T1D). This study examined the relationship between secretion and sensitivity in antibody-positive relatives at risk for T1D and the predictive value of the disposition index (DI) as a measure of β-cell function. Secretion and sensitivity measures in two independent antibody-positive populations at risk for T1D had an inverse relationship across different glycemic ranges. The DI using different measures of secretion and sensitivity was predictive of T1D. The DI is a useful biomarker of β-cell function in individuals at risk for T1D.
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    Trajectory of beta cell function and insulin clearance in stage 2 type 1 diabetes: natural history and response to teplizumab
    (Springer, 2025) Galderisi, Alfonso; Sims, Emily K.; Evans‑Molina, Carmella; Petrelli, Alessandra; Cuthbertson, David; Nathan, Brandon M.; Ismail, Heba M.; Herold, Kevan C.; Moran, Antoinette; Pediatrics, School of Medicine
    Aims/hypothesis: We aimed to analyse TrialNet Anti-CD3 Prevention (TN10) data using oral minimal model (OMM)-derived indices to characterise the natural history of stage 2 type 1 diabetes in placebo-treated individuals, to describe early metabolic responses to teplizumab and to explore the predictive capacity of OMM measures for disease-free survival rate. Methods: OMM-estimated insulin secretion, sensitivity and clearance and the disposition index were evaluated at baseline and at 3, 6 and 12 months post randomisation in placebo- and teplizumab-treated groups, and, within each group, in slow- and rapid-progressors (time to stage 3 disease >2 or ≤ 2 years). OMM metrics were also compared with the standard AUC C-peptide. Percentage changes in CD8+ T memory cell and programmed death-1 (PD-1) expression were evaluated in each group. Results: Baseline metabolic characteristics were similar between 28 placebo- and 39 teplizumab-treated participants. Over 12 months, insulin secretion declined in placebo-treated and rose in teplizumab-treated participants. Within groups, placebo slow-progressors (n=14) maintained insulin secretion and sensitivity, while both declined in placebo rapid-progressors (n=14). Teplizumab slow-progressors (n=28) maintained elevated insulin secretion, while teplizumab rapid-progressors (n=11) experienced mild metabolic decline. Compared with rapid-progressor groups, insulin clearance significantly decreased between baseline and 3, 6 and 12 months in the slow-progressor groups in both treatment arms. In aggregate, both higher baseline insulin secretion (p=0.027) and reduced 12 month insulin clearance (p=0.045) predicted slower progression. A >25% loss of insulin secretion at 3 months had specificity of 0.95 (95% CI 0.86, 1.00) to identify rapid-progressors and correctly classified the 2 year risk for progression in 92% of participants, with a sensitivity of 0.19 (95% CI 0.08, 0.30). OMM-estimated insulin secretion outperformed AUC C-peptide to differentiate groups by treatment or to predict progression. Metabolic changes were paralleled by relative frequency of change in PD-1+ CD8+ T effector memory cells. Conclusions/interpretation: OMM measures characterise the metabolic heterogeneity in stage 2 diabetes, identifying differences between rapid- and slow-progressors, and heterogeneous impacts of immunotherapy, suggesting the need to account for these differences when designing and interpreting clinical trials.