AIMS/HYPOTHESIS: Efficient islet isolation is an important prerequisite for successful clinical islet transplantation. Although progressively improved, islet yield and quality are, however, unpredictable and variable and require standardisation. METHODS: Since 1989 we have processed 437 pancreases using the automated method. The donor characteristics, pancreas procurement, and digestion and purification procedures including a wide enzyme characterisation of these pancreases were analysed and correlated with islet yield and transplant outcome. RESULTS: By univariate analysis, islet yield was significantly associated with donor age (r=0.16; p=0.0009), BMI (r=0.19; p=0.0004), good pancreas condition (p=0.0031) and weight (r=0.15; p=0.0056), total collagenase activity (r=0.22; p=0.0001), adjusted collagenase activity/mg (r=0.18; p=0.0002), collagenase activity/solution volume (r=0.18; p=0.0002) and neutral protease activity/solution volume (r=0.14; p=0.0029). A statistically significant contribution to the variability of islet yield in a multivariate analysis performed on donor variables was found for donor BMI (p=0.0008). In a multivariate analysis performed on pancreas variables a contribution was found for pancreas weight (p=0.0064), and for a multivariate analysis performed on digestion variables we found a contribution for digestion time (p=0.0048) and total collagenase activity (p=0.0001). Twenty-four patients with type 1 diabetes received single islet preparations from single donors. In these patients, multivariate analyses showed that the reduction in insulin requirement was significantly associated with morphological aspects of islets (p=0.0010) and that 1-month C-peptide values were associated with islet purity (p=0.0071). CONCLUSIONS/ INTERPRETATION: These data provide baseline donor, digestion and purification selection criteria for islet isolation using the automated method and indicate that the morphological aspect may be a clinically relevant measure of islets on which the decision for transplant can be based.
AIMS/HYPOTHESIS: Efficient islet isolation is an important prerequisite for successful clinical islet transplantation. Although progressively improved, islet yield and quality are, however, unpredictable and variable and require standardisation. METHODS: Since 1989 we have processed 437 pancreases using the automated method. The donor characteristics, pancreas procurement, and digestion and purification procedures including a wide enzyme characterisation of these pancreases were analysed and correlated with islet yield and transplant outcome. RESULTS: By univariate analysis, islet yield was significantly associated with donor age (r=0.16; p=0.0009), BMI (r=0.19; p=0.0004), good pancreas condition (p=0.0031) and weight (r=0.15; p=0.0056), total collagenase activity (r=0.22; p=0.0001), adjusted collagenase activity/mg (r=0.18; p=0.0002), collagenase activity/solution volume (r=0.18; p=0.0002) and neutral protease activity/solution volume (r=0.14; p=0.0029). A statistically significant contribution to the variability of islet yield in a multivariate analysis performed on donor variables was found for donor BMI (p=0.0008). In a multivariate analysis performed on pancreas variables a contribution was found for pancreas weight (p=0.0064), and for a multivariate analysis performed on digestion variables we found a contribution for digestion time (p=0.0048) and total collagenase activity (p=0.0001). Twenty-four patients with type 1 diabetes received single islet preparations from single donors. In these patients, multivariate analyses showed that the reduction in insulin requirement was significantly associated with morphological aspects of islets (p=0.0010) and that 1-month C-peptide values were associated with islet purity (p=0.0071). CONCLUSIONS/ INTERPRETATION: These data provide baseline donor, digestion and purification selection criteria for islet isolation using the automated method and indicate that the morphological aspect may be a clinically relevant measure of islets on which the decision for transplant can be based.
Authors: F Bertuzzi; A M Davalli; R Nano; C Socci; F Codazzi; R Fesce; V Di Carlo; G Pozza; F Grohovaz Journal: Diabetes Date: 1999-10 Impact factor: 9.461
Authors: J R Lakey; G L Warnock; R V Rajotte; M E Suarez-Alamazor; Z Ao; A M Shapiro; N M Kneteman Journal: Transplantation Date: 1996-04-15 Impact factor: 4.939
Authors: F Vargas; M Vives-Pi; N Somoza; L Alcalde; P Armengol; M Martí; L Serradell; M Costa; J Fernandez-Llamazares; A Sanmartí; R Pujol-Borrell Journal: Transplantation Date: 1996-06-15 Impact factor: 4.939
Authors: E A Ryan; J R Lakey; R V Rajotte; G S Korbutt; T Kin; S Imes; A Rabinovitch; J F Elliott; D Bigam; N M Kneteman; G L Warnock; I Larsen; A M Shapiro Journal: Diabetes Date: 2001-04 Impact factor: 9.461
Authors: P Y Benhamou; P C Watt; Y Mullen; S Ingles; Y Watanabe; Y Nomura; C Hober; M Miyamoto; T Kenmochi; E P Passaro Journal: Transplantation Date: 1994-06-27 Impact factor: 4.939
Authors: Wilson Km Wong; Guozhi Jiang; Anja E Sørensen; Yi Vee Chew; Cody Lee-Maynard; David Liuwantara; Lindy Williams; Philip J O'Connell; Louise T Dalgaard; Ronald C Ma; Wayne J Hawthorne; Mugdha V Joglekar; Anandwardhan A Hardikar Journal: JCI Insight Date: 2019-07-30