Comparing methods to normalize insulin secretion shows the process may not be
needed.
Authors Slepchenko KG, Corbin KL, Nunemaker CS
Submitted By Submitted Externally on 4/8/2019
Status Published
Journal The Journal of endocrinology
Year 2019
Date Published 3/1/2019
Volume : Pages Not Specified : Not Specified
PubMed Reference 30870813
Abstract Glucose-stimulated insulin secretion (GSIS) is a well-accepted method to
investigate the physiological and pathophysiological function of islets.
However, there is little consensus about which method is best for normalizing
and presenting GSIS data. In this study, we evaluated the sufficiency of islet
area, total protein, total DNA, and total insulin content as parameters to
normalize GSIS data. First, we tested if there is a linear correlation between
each parameter and the number of islets (10, 20, 30, and 40 islets). Islet area,
total protein, and insulin content produced excellent linear correlations with
islet number (R2 >0.9 for each) from the same islet material. Insulin secretion
in 11mM glucose also correlated reasonably well for islet area (R2=0.69),
protein (R2=0.49), and insulin content (R2=0.58). DNA content was difficult to
reliably measure and was excluded from additional comparisons. We next measured
GSIS for 18 replicates of 20 islets each, measuring 3mM and 11mM glucose to
calculate the stimulation index and to compare each normalization parameter.
Using these similar islet masses for each replicate, none of the parameters
produced linear correlations with GSIS (R2<0.05), suggesting that inherent
differences in GSIS dominate small differences in islet mass. We conclude that
when comparing GSIS for islets of reasonably similar size (<50% variance),
normalization does not improve the representation of GSIS data. Normalization
may be beneficial when substantial differences in islet mass are involved. In
such situations, we suggest that using islet cross-sectional area is superior to
other commonly used techniques for normalizing GSIS data.

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