Quantifying pituitary-adrenal dynamics and deconvolution of concurrent cortisol and adrenocorticotropic hormone data by compressed sensing

Rose T. Faghih, Munther A. Dahleh, Gail K. Adler, Elizabeth B. Klerman, Emery N. Brown

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Pulsatile release of cortisol from the adrenal glands is governed by pulsatile release of adrenocorticotropic hormone (ACTH) from the anterior pituitary. In return, cortisol has a negative feedback effect on ACTH release. Simultaneous recording of ACTH and cortisol is not typical, and determining the number, timing, and amplitudes of pulsatile events from simultaneously recorded data is challenging because of several factors: 1) stimulator ACTH pulse activity, 2) kinematics of ACTH and cortisol, 3) the sampling interval, and 4) the measurement error. We model ACTH and cortisol secretion simultaneously using a linear differential equations model with Gaussian errors and sparse pulsatile events as inputs to the model. We propose a novel framework for recovering pulses and parameters underlying the interactions between ACTH and cortisol. We recover the timing and amplitudes of pulses using compressed sensing and employ generalized cross validation for determining the number of pulses. We analyze serum ACTH and cortisol levels sampled at 10-min intervals over 24 h from ten healthy women. We recover physiologically plausible timing and amplitudes for these pulses and model the feedback effect of cortisol. We recover 15 to 18 pulses over 24 h, which is highly consistent with the results of another cortisol data analysis approach. Modeling the interactions between ACTH and cortisol allows for accurate quantification of pulsatile events, and normal and pathological states. This could lay the basis for a more physiologically-based approach for administering cortisol therapeutically. The proposed approach can be adapted to deconvolve other pairs of hormones with similar interactions.

    Original languageEnglish (US)
    Article number2427745
    Pages (from-to)2379-2388
    Number of pages10
    JournalIEEE Transactions on Biomedical Engineering
    Volume62
    Issue number10
    DOIs
    StatePublished - Oct 1 2015

    Keywords

    • Algorithms
    • Biological system modeling
    • Biomedical signal processing
    • Compressed sensing
    • Parameter estimation

    ASJC Scopus subject areas

    • Biomedical Engineering

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