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Correlation between urinary cortisol and 6-sulphatoxymelatonin rhythms in field studies of blind subjects.

AbstractOBJECTIVE:
Blind individuals have different types of circadian rhythm disorders. In order to study these in the field reliable markers of circadian phase are required. The aim of the present study was to determine the usefulness of urinary cortisol as a marker rhythm in field studies. This was assessed by investigating the relationship between the cortisol rhythms and the previously determined melatonin rhythms from a large sample of blind people with different types of circadian rhythm disorders.
DESIGN:
Field study
SUBJECT:
Registered blind subjects (n = 49) classified as having light perception or better (n = 19, 12 men, 7 women, aged 23-61 years) or having no conscious perception of light (n = 30, 24 men, 6 women, aged 19-72 years) were studied in their normal environment.
MEASUREMENT:
Sequential 4-hourly urine samples (plus an overnight sample) were collected for 48 h each week for 3-5 weeks. Urinary cortisol and 6-sulphatoxymelatonin were measured by radioimmunoassay.
RESULT:
Irrespective of the type of circadian rhythm (entrained or free-running), there was a significant correlation between the characteristics of the 6-sulphatoxymelatonin and cortisol rhythms in the blind subjects.
CONCLUSION:
Urinary cortisol is recommended as a useful marker of circadian phase in field studies in addition to urinary 6-sulphatoxymelatonin measurements.
AuthorsD J Skene, S W Lockley, K James, J Arendt
JournalClinical endocrinology (Clin Endocrinol (Oxf)) Vol. 50 Issue 6 Pg. 715-9 (Jun 1999) ISSN: 0300-0664 [Print] England
PMID10468942 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
Chemical References
  • Biomarkers
  • 6-sulfatoxymelatonin
  • Melatonin
  • Hydrocortisone
Topics
  • Adult
  • Aged
  • Biomarkers (urine)
  • Blindness (urine)
  • Circadian Rhythm
  • Female
  • Humans
  • Hydrocortisone (urine)
  • Male
  • Melatonin (analogs & derivatives, urine)
  • Middle Aged
  • Regression Analysis

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