Deconvolution of Serum Cortisol Levels by Using Compressed Sensing
Open accessAbstract
The pulsatile release of cortisol from the adrenal glands is controlled by a hierarchical system that involves corticotropin releasing hormone (CRH) from the hypothalamus, adrenocorticotropin hormone (ACTH) from the pituitary, and cortisol from the adrenal glands. Determining the number, timing, and amplitude of the cortisol secretory events and recovering the infusion and clearance rates from serial measurements of serum cortisol levels is a challenging problem. Despite many years of work on this problem, a complete satisfactory solution has been elusive. We formulate this question as a non-convex optimization problem, and solve it using a coordinate descent algorithm that has a principled combination of (i) compressed sensing for recovering the amplitude and timing of the secretory events, and (ii) generalized cross validation for choosing the regularization parameter. Using only the observed serum cortisol levels, we model cortisol secretion from the adrenal glands using a second-order linear differential equation with pulsatile inputs that represent cortisol pulses released in response to pulses of ACTH. Using our algorithm and the assumption that the number of pulses is between 15 to 22 pulses over 24 hours, we successfully deconvolve both simulated datasets and actual 24-hr serum cortisol datasets sampled every 10 minutes from 10 healthy women. Assuming a one-minute resolution for the secretory events, we obtain physiologically plausible timings and amplitudes of each cortisol secretory event with R (2) above 0.92. Identification of the amplitude and timing of pulsatile hormone release allows (i) quantifying of normal and abnormal secretion patterns towards the goal of understanding pathological neuroendocrine states, and (ii) potentially designing optimal approaches for treating hormonal disorders.
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The proposed architecture has been previously verified for cortisol in healthy subjects and in patients with chronic fatigue and fibromyalgia syndromes.
“The proposed architecture has been previously verified for cortisol in healthy subjects and also on patients suffering from chronic fatigue and fibromyalgia syndromes (32–34).”
From Sparse System Identification of Leptin Dynamics in Women With Obesity (2022) · cites this paper as [33]
The Methods (Experiment; page not provided) describe cortisol measurements from 10 healthy women, and the Results report that the model captured their cortisol dynamics. The women were healthy controls for a separate fibromyalgia study; this paper does not report testing the method in patients with fibromyalgia or chronic fatigue syndrome. (LLM, conf=0.90)
Experimental — accuracy not yet measuredBecause 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 [33]
The cortisol secretion model used in this research was provided by Faghih et al.
“In this research, we use the cortisol secretion model provided by Faghih et al. (33).”
From Sparse System Identification of Leptin Dynamics in Women With Obesity (2022) · cites this paper as [33]
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Cited by
Sorted by citations
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A Cortisol-Based Energy Decoder for Investigation of Fatigue in Hypercortisolism
Wickramasuriya, D. S., & Faghih, R. T. (2019) · 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857658 · Cited by 23 · see citing works
Self-citationSparse System Identification of Leptin Dynamics in Women With Obesity
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“As this approach has been previously examined for cortisol data in different studies, we do not validate it on simulated data (33, 34, 38).”
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Self-citation
- Authors
- Faghih, R. T., Dahleh, M. A., Adler, G. K., Klerman, E. B., & Brown, E. N.
- Affiliations
- Massachusetts General Hospital (US)
- Massachusetts Institute of Technology (US)
- Brigham and Women's Hospital (US)
- Harvard University (US)
- Corresponding author
- Faghih, Rose T.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-01-28
- Volume
- 9
- Issue
- 1
- Pages
- e85204
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3904842
- OpenAlex
- W2144190916
- Cited by
- 54
- References
- 22
- Citations by year
- 2025: 1 · 2024: 6 · 2023: 6 · 2022: 4 · 2021: 7 · 2020: 3 · 2019: 11 · 2018: 3 · 2017: 5 · 2016: 4 · 2015: 3 · 2014: 1
- FWCI
- 3.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (0735956, 0836720, NCC 9-58)
- National Aeronautics and Space Administration (NASA NCC 9‐58, HFP02802, NCC958, RC2-HL101340)
- Brigham and Women's Hospital
- Deutsches Krebsforschungszentrum
- National Space Biomedical Research Institute (NASA NCC 9‐58, HFP-02802, NCC 9‐58, HFP01603)
- National Institutes of Health (R01-HL-11408, DP1OD003646, R01GM, HFP01603, 5rc2hl101340-02, NIH K24, NCC9-58, HFP02802, RC2-HL101340, AG09975)
- National Institute on Aging (Z01AG000661, K01-AG00661, NIH P01-AG09975, P01AG009975, K01AG000661)
- National Heart, Lung, and Blood Institute (RC2- HL101340, K24HL105664, R01HL114088, NIH K24 HL103845, R01-HL-11408, K24HL103845)
- National Institute of General Medical Sciences (R01-GM53559, R01GM053559)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR043130, NIH R01AR43130)
- National Center for Research Resources (L30 RR020635, UL1RR025758, M01RR20635, M01RR002635)
- Division of Emerging Frontiers in Research and Innovation
- 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
- cortisol secretion, pulsatile hormone secretion, compressed sensing, deconvolution, ACTH, non-convex optimization, coordinate descent, generalized cross-validation, regularization parameter selection, adrenal gland, neuroendocrine regulation
- MeSH terms
- Adrenocorticotropic Hormone, Adult, Algorithms, Corticotropin-Releasing Hormone, Female, Humans, Hydrocortisone, Young Adult
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