Age-Corrected Beta Cell Mass Following Onset of Type 1 Diabetes Mellitus Correlates with Plasma C-Peptide in Humans

Department of Chemical Engineering, West Virginia University, Morgantown, West Virginia, United States of America.
PLoS ONE (Impact Factor: 3.23). 11/2011; 6(11):e26873. DOI: 10.1371/journal.pone.0026873
Source: PubMed


The inability to produce insulin endogenously precipitates the clinical symptoms of type 1 diabetes mellitus. However, the dynamic trajectory of beta cell destruction following onset remains unclear. Using model-based inference, the severity of beta cell destruction at onset decreases with age where, on average, a 40% reduction in beta cell mass was sufficient to precipitate clinical symptoms at 20 years of age. While plasma C-peptide provides a surrogate measure of endogenous insulin production post-onset, it is unclear as to whether plasma C-peptide represents changes in beta cell mass or beta cell function. The objective of this paper was to determine the relationship between beta cell mass and endogenous insulin production post-onset.
Model-based inference was used to compare direct measures of beta cell mass in 102 patients against contemporary measures of plasma C-peptide obtained from three studies that collectively followed 834 patients post-onset of clinical symptoms. An empirical Bayesian approach was used to establish the level of confidence associated with the model prediction. Age-corrected estimates of beta cell mass that were inferred from a series of landmark pancreatic autopsy studies significantly correlate (p>0.9995) with contemporary measures of plasma C-peptide levels following onset.
Given the correlation between beta cell mass and plasma C-peptide following onset, plasma C-peptide may provide a surrogate measure of beta cell mass in humans. The clinical relevance of this study is that therapeutic strategies that provide an increase in plasma C-peptide over the predicted value for an individual may actually improve beta cell mass. The model predictions may establish a standard historical "control" group - a prior in a Bayesian context - for clinical trials.

Download full-text


Available from: David Klinke,
  • Source
    • "However, beta cell loss is not absolute and, in several patients insulin-positive beta cells and glucose transporters [29], are detectable for many years after diagnosis [31••]. These results, together with the persistence of insulitis, further support the concept that the disease process is chronic; importantly, these findings challenge the traditional view that beta cell loss is virtually complete at the time of clinical onset, as also suggested by a meta-analysis of previously published cases [65, 66]. Importantly, evidence for significant dysfunction of pancreatic beta cells is emerging from unpublished data from the new onset patients in the DiVid study [52] and from our own pancreas transplant recipients with disease recurrence, in which impaired beta cell function is shown despite the presence of insulin-positive cells at biopsy. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The Juvenile Diabetes Research Foundation (JDRF) Network for Pancreatic Organ Donors with Diabetes (JDRF nPOD) was established to obtain human pancreata and other tissues from organ donors with type 1 diabetes (T1D) in support of research focused on disease pathogenesis. Since 2007, nPOD has recovered tissues from over 100 T1D donors and distributed specimens to approximately 130 projects led by investigators worldwide. More recently, nPOD established a programmatic expansion that further links the transplantation world to nPOD, nPOD-Transplantation; this effort is pioneering novel approaches to extend the study of islet autoimmunity to the transplanted pancreas and to consent patients for postmortem organ donation directed towards diabetes research. Finally, nPOD actively fosters and coordinates collaborative research among nPOD investigators, with the formation of working groups and the application of team science approaches. Exciting findings are emerging from the collective work of nPOD investigators, which covers multiple aspects of islet autoimmunity and beta cell biology.
    Current Diabetes Reports 10/2014; 14(10):530. DOI:10.1007/s11892-014-0530-0 · 3.08 Impact Factor
  • Source
    • "Because type 1 diabetes is a complex multifactor disease with a strong genetic component and significant environmental influences, a broad immunological assessment and multifactor algorithm to pick patterns is likely needed to dissect the complex pathogenesis and identify key patterns of disease progression. By using a multivariate logistic regression analysis adjusted for age, BMI, and the value of fasting C-pep at disease onset, we identified here two specific immune cell populations, both measured at disease onset, that were capable to predict C-pep secretion as a surrogate measure of β-cell mass in humans with type 1 diabetes (29). Indeed, the number of CD3+CD16+CD56+ T cells and the percentage of mDC1s were independent predictors of residual C-pep secretion 12 months after diagnosis. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Type 1 diabetes is characterized by autoimmune destruction of pancreatic β-cells in genetically susceptible individuals. Triggers of islet autoimmunity, time course and the precise mechanisms responsible for the progressive β-cell failure are not completely understood. The recent escalation of obesity in affluent countries has been suggested to contribute to the increased incidence of type 1 diabetes. Understanding the link between metabolism and immune tolerance could lead to the identification of new markers for the monitoring of disease onset and progression. We studied several immune cell subsets and factors with high metabolic impact as markers associated with disease progression in high-risk subjects, type 1 diabetes patients at onset, 12 and 24 months after diagnosis. A multiple correlation matrix among different parameters was evaluated statistically and assessed visually on two-dimensional graphs. Markers to predict residual β-cell function up to one year after diagnosis were identified in multivariate logistic regression models. The metaimmunological profile changed significantly over time in patients and a specific signature that associated with worsening disease was identified. A multivariate logistic regression model measuring age, body mass index (BMI), fasting C-peptide, number of circulating CD3(+)CD16(+)CD56(+) cells and the percentage of CD1c(+)CD19(-)CD14(-)CD303(-) type 1 myeloid dendritic cells (mDC1s) at disease onset had a significant predictive value. The identification of a specific meta-immunological profile associated to disease status may contribute to understand the basis of diabetes progression.
    Diabetes 02/2013; 62(7). DOI:10.2337/db12-1273 · 8.10 Impact Factor
  • Source
    • "In addition, the mathematical model provides a prediction of the beta cell mass required to maintain glucose homeostasis. As a validation of the model, one finds that the difference between the observed and predicted beta cell mass (i.e., residual beta cell mass) parallels the observed changes in C-peptide following diagnosis (see Figure 5), as described in [34]. In summary, this model (i.e., prototype) suggests that clinical presentation of the disease is not attributed solely to the destruction of beta cell mass but is the result of a dynamic balance between the production of insulin (i.e., beta cell mass) and the size of the system (i.e., body weight). "
    [Show abstract] [Hide abstract]
    ABSTRACT: A major challenge in immunology is how to translate data into knowledge given the inherent complexity and dynamics of human physiology. Both the physiology and engineering communities have rich histories in applying computational approaches to translate data obtained from complex systems into knowledge of system behavior. However, there are some differences in how disciplines approach problems. By referring to mathematical models as mathematical prototypes, we aim to highlight aspects related to the process (i.e., prototyping) rather than the product (i.e., the model). The objective of this paper is to review how two related engineering concepts, specifically prototyping and “fitness for use,” can be applied to overcome the pressing challenge in translating data into improved knowledge of basic immunology that can be used to improve therapies for disease. These concepts are illustrated using two immunology-related examples. The prototypes presented focus on the beta cell mass at the onset of type 1 diabetes and the dynamics of dendritic cells in the lung. This paper is intended to illustrate some of the nuances associated with applying mathematical modeling to improve understanding of the dynamics of disease progression in humans.
    Computational and Mathematical Methods in Medicine 09/2012; 2012(1):676015. DOI:10.1155/2012/676015 · 0.77 Impact Factor
Show more