Besser, REJ;
Ludvigsson, J;
Hindmarsh, PC;
Cole, TJ;
(2018)
Exploring C-peptide loss in type 1 diabetes using growth curve analysis.
PLoS ONE
, 13
(7)
, Article e0199635. 10.1371/journal.pone.0199635.
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Abstract
Objectives: C-peptide (CP) loss in type 1 diabetes (T1D) is highly variable, and factors influencing it are poorly understood. We modelled CP values in T1D patients from diagnosis for up to 6 years, treating the serial data as growth curves plotted against time since diagnosis. The aims were to summarise the pattern of CP loss (i.e. growth curve shape) in individual patients in simple terms, and to identify baseline characteristics that predict this pattern in individuals. Materials and methods: Between 1976 and 2011, 442 T1D patients initially aged <18y underwent 120-minute mixed meal tolerance tests (MMTT) to calculate area under the curve (AUC) CP, at 3, 9, 18, 30, 48 and 72 months after diagnosis (n = 1537). The data were analysed using the novel SITAR mixed effects growth curve model (SuperImposition by Translation And Rotation). It fits a mean AUC growth curve, but also allows the curve’s mean level and rate of fall to vary between individuals so as to best fit the individual patient curves. These curve adjustments define individual curve shape. Results: The square root (√) AUC scale provided the best fit. The mean levels and rates of fall for individuals were normally distributed and uncorrelated with each other. Age at diagnosis and √AUC at 3 months strongly predicted the patient-specific mean levels, while younger age at diagnosis (p<0.0001) and the 120-minute CP value of the 3-month MMTT (p = 0.002) predicted the patient-specific rates of fall. Conclusions: SITAR growth curve analysis is a useful tool to assess CP loss in type 1 diabetes, explaining patient differences in terms of their mean level and rate of fall. A definition of rapid CP loss could be based on a quantile of the rate of fall distribution, allowing better understanding of factors determining CP loss and stratification of patients into targeted therapies.
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