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Mass Spectrometric Analysis of Glycosphingolipid Antigens
Published on: April 16, 2013
Diurnal variation of steroid hormones and their reference intervals using mass spectrometric analysis
T P Parikh1, B Stolze2, Y Ozarda3
1Program in Reproductive and Adult Endocrinology, National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, Maryland, USA.
Objective:
Accurate measurement of steroid hormones remains challenging. Mass spectrometry affords a reliable means for quantitating steroid profiles accurately. Our objective was to establish and define (1) the extent of diurnal fluctuations in steroid concentrations that potentially necessitate strict adherence to time of sample acquisition and (2) time-dependent steroid reference intervals.
Design:
Nine steroid markers were examined in couplets in males and females.
Methods:
Using isotope dilution high-performance liquid chromatography-tandem mass spectrometric (LC-MS/MS) analysis, we developed a multi-steroid profile requiring only a minimal volume of serum (0.1 mL). Couplet (AM and PM) measurements of steroid hormones for 120 healthy females (F) and 62 healthy males (M) were obtained. Patients were recruited from several participating centers.
Results:
The following diurnal values were noted to be significantly different in both females and males: cortisone, cortisol, corticosterone, 11 deoxycortisol (11 DOC), androstenedione, 17a-hydroxyprogesterone (17 OHP) and dehydroepiandrosterone (DHEA). Testosterone was only found to have significant diurnal variance in males. Progesterone showed no significant difference in AM and PM values for either groups and thus may provide an internal control.
Conclusions:
When diagnosing endocrine disorders, it is imperative to acknowledge the 24-h diurnal variation of the biochemical steroid markers. We highlight the importance of standardization of collection times and appropriate implementation of reference intervals.
Precis:
We identify diurnal fluctuations in steroid concentrations with time of day and emphasize the importance of adhering to firm time of sample acquisition.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

