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Published on: August 7, 2017
Predictive value of serum MED1 and PGC-1α for bronchopulmonary dysplasia in preterm infants
Mengzhao Li1,2, Wenqiang Sun1, Changchang Fu1
1Department of Neonatology, Children's Hospital of Soochow University, Suzhou, China.
Insights
Predicting bronchopulmonary dysplasia (BPD) in preterm infants is possible using serum levels of MED1 and PGC-1α, along with clinical factors. This combination offers high accuracy for early BPD diagnosis in vulnerable newborns.
Area of Science:
- Neonatal Medicine
- Biomarker Discovery
- Respiratory Health
Background:
- Bronchopulmonary dysplasia (BPD) is a significant complication in preterm infants.
- Early prediction of BPD is crucial for timely intervention and improved outcomes.
- Existing predictive methods may lack sufficient accuracy or require invasive procedures.
Purpose of the Study:
- To develop a predictive model for BPD in preterm infants (gestational age < 32 weeks).
- To evaluate the utility of serum mediator complex subunit 1 (MED1) and peroxisome proliferator-activated receptor gamma coactivator-1alpha (PGC-1α) as biomarkers for BPD.
- To combine novel biomarkers with established clinical risk factors for enhanced BPD prediction.
Main Methods:
- Prospective observational study of 70 preterm infants (GA < 32 weeks).
- Serum MED1 and PGC-1α levels measured at multiple postnatal time points.
- Multifactorial and logistic regression analyses to identify risk factors and construct a predictive model.
- Comparison of biomarker levels between infants with and without BPD.
Main Results:
- Significantly lower serum MED1 (day 1) and PGC-1α (days 1, 7, 28) levels observed in the BPD group.
- A predictive model combining MED1, PGC-1α (day 1), frequent apnea, mechanical ventilation duration, and time to enteral nutrition achieved high accuracy (C-statistic = 0.989).
- The model demonstrated strong predictive performance for BPD development.
Conclusions:
- Serum MED1 and PGC-1α show potential as valuable biomarkers for BPD.
- Combining these biomarkers with clinical data significantly improves early BPD prediction accuracy.
- This approach can assist clinicians in the early diagnosis and management of BPD in preterm infants.
Objective:
This study aimed to predict the bronchopulmonary dysplasia (BPD) in preterm infants with a gestational age(GA) < 32 weeks utilizing clinical data, serum mediator complex subunit 1 (MED1), and serum peroxisome proliferator-activated receptor gamma coactivator-1alpha (PGC-1α).
Methods:
This prospective observational study enrolled 70 preterm infants with GA < 32 weeks. The infants were categorized into two groups: non-BPD group(N = 35) and BPD group(N = 35), including 25 cases with mild BPD and 10 patients with moderate/severe subgroups. We performed multifactorial regression analysis to investigate the postnatal risk factors for BPD. Furthermore, we compared serum levels of biomarkers, including MED1 and PGC-1α, among infants with and without BPD at postnatal days 1, 7, 14, 28, and PMA 36 weeks. A logistic regression model was constructed to predict BPD's likelihood using clinical risk factors and serum biomarkers.
Results:
Serum levels of MED1 on the first postnatal day, PGC-1α on the 1st, 7th, and 28th days, and PMA at 36 weeks were significantly lower in the BPD group than in the non-BPD group (P < 0.05). Furthermore, the predictive model for BPD was created by combing serum levels of MED1 and PGC-1α on postnatal day 1 along with clinical risk factors such as frequent apnea, mechanical ventilation time > 7 d, and time to reach total enteral nutrition. Our predictive model had a high predictive accuracy(C statistics of 0.989) .
Conclusion:
MED1and PGC-1α could potentially serve as valuable biomarkers, combined with clinical factors, to aid clinicians in the early diagnosis of BPD.
