Document Detail


Accuracy of several parameters of hypothalamic-pituitary-adrenal axis activity in predicting before surgery the metabolic effects of the removal of an adrenal incidentaloma.
MedLine Citation:
PMID:  20881060     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
CONTEXT: It is unknown whether the metabolic effects of the removal of an adrenal incidentaloma (AI) can be predicted by the assessment of cortisol hypersecretion before surgery.
OBJECTIVE: To evaluate the accuracy of several criteria of hypothalamic-pituitary-adrenal axis activity in predicting the metabolic outcome after adrenalectomy.
DESIGN: Retrospective longitudinal study.
PATIENTS: In 55 surgically treated AI patients (Group 1) before surgery and in 53 nontreated AI patients (Group 2) at the baseline, urinary free cortisol (UFC), cortisol after 1 mg overnight dexamethasone-suppression test (1 mg-DST), ACTH, and midnight serum cortisol (MSC) were measured. In Groups 1 and 2, metabolic parameters were evaluated before and 29.6 ± 13.8 months after surgery and at the baseline and after 35.2 ± 10.9 months respectively.
MAIN OUTCOME MEASURES: The improvement/worsening of weight, blood pressure, glucose, and cholesterol levels (endpoints) was defined by the presence of a >5% weight decrease/increase and following the European Society of Cardiology or the ATP III criteria respectively. The accuracy of UFC, 1 mg-DST, ACTH, and MSC, singularly taken or in combination, in predicting the improvement/worsening of ≥ 2 endpoints was calculated.
RESULTS: The presence of ≥ 2 among UFC>70 μg/24 h (193 nmol/l), ACTH<10 pg/ml (2.2 pmol/l), 1 mg-DST>3.0 μg/dl (83 nmol/l) (UFC-ACTH-DST criterion) had the best accuracy in predicting the endpoints' improvement (sensitivity (SN) 65.2%, specificity (SP) 68.8%) after surgery. In the nontreated AI patients, this criterion predicted the worsening of ≥ 2 endpoints (SN 55.6%, SP 82.9%).
CONCLUSIONS: The UFC-ACTH-DST criterion seems to be the best for predicting the metabolic outcome in surgically treated AI patients.
Authors:
Cristina Eller-Vainicher; Valentina Morelli; Antonio Stefano Salcuni; Claudia Battista; Massimo Torlontano; Francesca Coletti; Laura Iorio; Elisa Cairoli; Paolo Beck-Peccoz; Maura Arosio; Bruno Ambrosi; Alfredo Scillitani; Iacopo Chiodini
Publication Detail:
Type:  Evaluation Studies; Journal Article; Multicenter Study; Research Support, Non-U.S. Gov't     Date:  2010-09-29
Journal Detail:
Title:  European journal of endocrinology / European Federation of Endocrine Societies     Volume:  163     ISSN:  1479-683X     ISO Abbreviation:  Eur. J. Endocrinol.     Publication Date:  2010 Dec 
Date Detail:
Created Date:  2010-11-11     Completed Date:  2010-12-14     Revised Date:  2010-12-30    
Medline Journal Info:
Nlm Unique ID:  9423848     Medline TA:  Eur J Endocrinol     Country:  England    
Other Details:
Languages:  eng     Pagination:  925-35     Citation Subset:  IM    
Affiliation:
Endocrinology and Diabetology Unit, Department of Medical Sciences, University of Milan, Fondazione IRCCS Cà Granda - Ospedale Maggiore Policlinico, Pad. Granelli, Via F. Sforza 35, 20122 Milan, Italy.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Adenoma / metabolism,  surgery
Adrenal Gland Neoplasms / metabolism,  surgery*
Adrenocorticotropic Hormone / blood
Adult
Aged
Dexamethasone / diagnostic use
Female
Humans
Hydrocortisone / secretion,  urine
Hypothalamo-Hypophyseal System / metabolism*
Incidental Findings
Longitudinal Studies
Male
Middle Aged
Pituitary-Adrenal System / metabolism*
Predictive Value of Tests
Retrospective Studies
Sensitivity and Specificity
Chemical
Reg. No./Substance:
50-02-2/Dexamethasone; 50-23-7/Hydrocortisone; 9002-60-2/Adrenocorticotropic Hormone
Comments/Corrections
Comment In:
Nat Rev Endocrinol. 2011 Jan;7(1):3

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Type II transforming growth factor-beta receptor recycling is dependent upon the clathrin adaptor pr...
Next Document:  Identifying dietary patterns by using reduced rank regression.