Hormone modeling in preterm neonates: establishment of pituitary and steroid hormone reference intervals.

CONTEXT

Reports suggest significant differences in serum levels of hormones in extremely preterm compared with late preterm and full-term infants.

OBJECTIVES

The purpose of this study was to develop reference intervals (RIs) for 3 pituitary hormones and 5 steroid hormones in serum of preterm infants.

DESIGN

Blood samples were collected from 248 (128 male and 120 female) preterm neonates born between 24 and 32 weeks' gestation.

SETTING

PARTICIPANTS were recruited from 3 neonatal intensive care wards in Melbourne, Australia.

PARTICIPANTS

No infant in this cohort had ambiguous genitalia or other endocrine abnormalities. All infants included in the RI determination survived beyond the equivalent of term.

INTERVENTIONS

Serum was analyzed for prolactin, FSH, and LH by automated electrochemiluminescence immunoassay (Roche Cobas 8000-e601). Liquid chromatography coupled with tandem mass spectrometry was used for analysis of 17-hydroxyprogesterone, androstenedione, cortisol, cortisone, and testosterone.

MAIN OUTCOME MEASURES

The robust method was applied to define the central 95% RI, after each hormone measure was transformed using a Box-Cox transformation to correct for asymmetry.

RESULTS

RIs were established for 8 hormones. Gender-specific intervals were developed for FSH, LH, and testosterone. Cortisone and 17- hydroxyprogesterone required division based on gestational age, with neonates born at <30 weeks' gestation demonstrating higher levels than their older counterparts. Androstenedione, cortisol, and prolactin did not require any division within this cohort for RI assignment.

CONCLUSIONS

This report provides the first characterization of serum steroids measured by mass spectrometry in preterm neonates, with the additional characterization of 3 pituitary hormones in infants born at ≤32 weeks' gestation. Use of these data allows for correct interpretation of results for very preterm neonates and reduces the risk of incorrect diagnosis due to misinterpretation of data.