Comparing methods to normalize insulin secretion shows the process may not be needed

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© 2019 Society for Endocrinology Published by Bioscientifica Ltd. 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 11 mM 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 3 mM and 11 mM 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.