Deconvolution of serum cortisol levels by using compressed sensing.
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.
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America ; Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America ; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
Rose T
RT
Faghih
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America ; Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Munther A
MA
Dahleh
Harvard Medical School, Boston, Massachusetts, United States of America ; Division of Endocrinology, Diabetes and Hypertension, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.
Gail K
GK
Adler
Harvard Medical School, Boston, Massachusetts, United States of America ; Division of Sleep Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.
Elizabeth B
EB
Klerman
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America ; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America ; Harvard Medical School, Boston, Massachusetts, United States of America ; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Emery N
EN
Brown
D000324:Adrenocorticotropic Hormone / Q000097:blood; D000328:Adult; D000465:Algorithms; D003346:Corticotropin-Releasing Hormone / Q000097:blood; D005260:Female; D006801:Humans; D006854:Hydrocortisone / Q000097:blood*; D055815:Young Adult
D000324:Adrenocorticotropic Hormone; D003346:Corticotropin-Releasing Hormone; D006854:Hydrocortisone