Alloantibody and autoantibody monitoring predicts islet transplantation outcome in human type 1 diabetes

Lorenzo Piemonti, Matthew J Everly, Paola Maffi, Marina Scavini, Francesca Poli, Rita Nano, Massimo Cardillo, Raffaella Melzi, Alessia Mercalli, Valeria Sordi, Vito Lampasona, Alejandro Espadas de Arias, Mario Scalamogna, Emanuele Bosi, Ezio Bonifacio, Antonio Secchi, Paul I Terasaki, Lorenzo Piemonti, Matthew J Everly, Paola Maffi, Marina Scavini, Francesca Poli, Rita Nano, Massimo Cardillo, Raffaella Melzi, Alessia Mercalli, Valeria Sordi, Vito Lampasona, Alejandro Espadas de Arias, Mario Scalamogna, Emanuele Bosi, Ezio Bonifacio, Antonio Secchi, Paul I Terasaki

Abstract

Long-term clinical outcome of islet transplantation is hampered by the rejection and recurrence of autoimmunity. Accurate monitoring may allow for early detection and treatment of these potentially compromising immune events. Islet transplant outcome was analyzed in 59 consecutive pancreatic islet recipients in whom baseline and de novo posttransplant autoantibodies (GAD antibody, insulinoma-associated protein 2 antigen, zinc transporter type 8 antigen) and donor-specific alloantibodies (DSA) were quantified. Thirty-nine recipients (66%) showed DSA or autoantibody increases (de novo expression or titer increase) after islet transplantation. Recipients who had a posttransplant antibody increase showed similar initial performance but significantly lower graft survival than patients without an increase (islet autoantibodies P < 0.001, DSA P < 0.001). Posttransplant DSA or autoantibody increases were associated with HLA-DR mismatches (P = 0.008), induction with antithymocyte globulin (P = 0.0001), and pretransplant panel reactive alloantibody >15% in either class I or class II (P = 0.024) as independent risk factors and with rapamycin as protective (P = 0.006) against antibody increases. DSA or autoantibody increases after islet transplantation are important prognostic markers, and their identification could potentially lead to improved islet cell transplant outcomes.

Trial registration: ClinicalTrials.gov NCT01346085.

Figures

FIG. 1.
FIG. 1.
Venn diagrams of antibody increase after islet transplantation. Significant antibody increase after transplantation was observed in 39 of 59 patients (66%). Twenty-two of 59 patients (37.3%) had an increase in autoantibodies (left). Single and overlapping GADA, IA-2A, ZnT8A, and DSA increases are reported, with the percentage of patients in each group shown. Twenty-seven of 59 patients (45.7%) had an increase in DSA (right). Single and overlapping IgG DSA class I, IgG DSA class II, IgM DSA class I, and IgM DSA class II increases are reported, with the percentage of patients in each group shown.
FIG. 2.
FIG. 2.
Time of antibodies appearance. Time course of autoantibody (top) or DSA (bottom) increase after first islet infusion.
FIG. 3.
FIG. 3.
Antibody increase and graft function. A: Probability of islet survival according to Kaplan-Meier method, with the increase of antibody as a time-varying covariate by Cox proportional hazards regression model. B: Survival of islet graft functions after increase of antibodies. C: Probability of insulin independence loss according to Kaplan-Meier method, with the increase of antibody as a time-varying covariate by Cox proportional hazards regression model. D: Association between antibody modification–free time and insulin-free time by linear regression analysis. Measurements are shown with dots, linear regression with solid line, and 95% mean prediction interval with dashed lines. Gray dots, still insulin free at last follow-up; black dots, not insulin free at last follow-up.
FIG. 4.
FIG. 4.
Autoantibody and DSA increase and graft function. A: Probability of islet survival according to Kaplan-Meier method, with the increase of antibody as a time-varying covariate by Cox proportional hazards regression model. B: Probability of insulin independence loss according to Kaplan-Meier method, with the increase of antibody as a time-varying covariate by Cox proportional hazards regression model.
FIG. 5.
FIG. 5.
Univariate HRs for antibody increase. All factors analyzed are depicted. ■, HR; line, 95% CI; □, P < 0.05. Ab, antibody; IE, islet equivalents; Ln, natural logarithm; pre-Tx, pretransplant.

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