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The effect of antenatal steroids on metabolic bone disease of prematurity

  • Sara Erol ORCID logo EMAIL logo , Mustafa Senol Akin , Nihan Hilal Hosagasi and Sabriye Korkut
Published/Copyright: March 18, 2025

Abstract

Objectives

The study aimed to evaluate the impact of antenatal steroid administration, a key intervention for reducing early mortality and morbidity in preterm infants, on the development of metabolic bone disease.

Methods

This single-center retrospective study was conducted in a Level III neonatal intensive care unit from October 2020 to December 2023.

Results

It included 173 infants born before 32 weeks of gestation, with a mean birth weight of 1,338 ± 293 g. Metabolic bone disease, diagnosed at four weeks of age based on serum phosphorus and alkaline phosphatase levels, was identified in 26 (15 %) of the infants. Regression analysis examined prenatal factors, including birth weight, intrauterine growth restriction, respiratory distress syndrome, gender, and antenatal steroid exposure, revealing that only lower birth weight was an independent risk factor for metabolic bone disease.

Conclusions

Antenatal steroid administration did not significantly influence the diagnosis of metabolic bone disease when assessed using biochemical markers at four weeks of age. These findings underscore the importance of birth weight in the risk profile for metabolic bone disease while indicating that antenatal steroids are not a contributing factor.


Corresponding author: Sara Erol, MD, Department of Pediatrics, Division of Neonatology, Ankara Bilkent City Hospital Universities Neighbourhood, 1604 Street, Ankara, Türkiye, E-mail:

  1. Research ethics: Ethical approval for this study was granted by the local Institutional Review Board (Approval ID: [E2-23-4569], Date: [19.07.2023]) and the study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

  2. Informed consent: Applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Data available on request due to privacy/ethical/legal restrictions.

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Received: 2025-01-15
Accepted: 2025-02-28
Published Online: 2025-03-18
Published in Print: 2025-05-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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