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RESEARCH ARTICLE

Endocrine pulse identification using penalized methods and a minimum set of assumptions

¹University of Amsterdam ²Leiden University Medical Center ³TNO Quality of Life

Submitted 23 January 2009 ; revised 20 October 2009 ; accepted in final form 20 October 2009

The detection of hormone secretion episodes is important for understanding normal and abnormal endocrine functioning, but pulse identification from hormones measured with short interval sampling is challenging. Furthermore, to obtain useable results, the model underlying hormone secretion and clearance must be augmented with restrictions based on biologically acceptable assumptions. Here, using the assumption that there are only a few time points at which a hormone is secreted, we used a modern penalized nonlinear least squares setup to select the number of secretion events. We did not assume a particular shape or frequency distribution for the secretion pulses. Our pulse identfication method, VisPulse, worked well with luteinizing hormone (LH), cortisol, growth hormone, or testosterone. In particular, applying our modeling strategy to previous LH data revealed a good correlation between the modeled and measured LH hormone concentrations, the estimated secretion pattern was sparse, and the small and structure-less residuals indicated a proper model with a good fit. We benchmarked our method to AutoDecon, a commonly used hormone secretion model, and performed releasing hormone infusion experiments. The results of these experiments confirmed that our method is accurate and outperforms AutoDecon, especially for detecting silent periods and small secretion events, suggesting a high secretion event resolution. Method validation using (releasing hormone) infusion data revealed sensitivities and selectivities of 0.88 and 0.95 and of 0.69 and 0.91 for VisPulse and AutoDecon, respectively.

secretion; pulsatility; AutoDecon; Model selection; nonlinear fitting