Quantifying Pituitary-Adrenal Dynamics and Deconvolution of Concurrent Cortisol and Adrenocorticotropic Hormone Data by Compressed Sensing
Open accessAbstract
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.
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Because the approach had been previously examined for cortisol data in different studies, it was not validated on simulated data.
“As this approach has been previously examined for cortisol data in different studies, we do not validate it on simulated data (33, 34, 38).”
From Sparse System Identification of Leptin Dynamics in Women With Obesity (2022) · cites this paper as [38]
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- Authors
- Faghih, R. T., Dahleh, M. A., Adler, G. K., Klerman, E. B., & Brown, E. N.
- Affiliations
- Massachusetts Institute of Technology (US)
- Brigham and Women's Hospital (US)
- Harvard University (US)
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2015-10
- Volume
- 62
- Issue
- 10
- Pages
- 2379-2388
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- PMC
- PMC4579049
- OpenAlex
- W1987809657
- Cited by
- 30
- References
- 30
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 3 · 2023: 3 · 2022: 2 · 2021: 2 · 2020: 2 · 2019: 10 · 2018: 2 · 2017: 2 · 2016: 1
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- PMC
- Funders
- NIH (R01AR43130 (GKA), M01RR20635 (BWH GCRC), K01AG00661 (EBK), R01GM 53559 (ENB), K24 HL103845, P01-AG09975, K24-HL105664, RC2-HL101340, R01-HL-11408, R01-64105018, HFP01603, HFP02802, DP1 OD003646)
- NASA (NCC9-58)
- NSBRI
- NSF (EFRI-0735956, 0836720)
- National Space Biomedical Research Institute through NASA NCC 9-58
- Topics
- Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Adrenal Hormones and Disorders
- Life Sciences › Neuroscience › Behavioral Neuroscience › Stress Responses and Cortisol
- Keywords
- ACTH, cortisol, pulsatile hormone secretion, pituitary-adrenal axis, negative feedback, compressed sensing, generalized cross-validation, sparse signal recovery
- MeSH terms
- Adrenocorticotropic Hormone, Adult, Algorithms, Female, Humans, Hydrocortisone, Pituitary-Adrenal System, Signal Processing, Computer-Assisted, Models, Statistical, Young Adult
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