Martina A Steurer1,2,3, Scott Oltman4, Rebecca J Baer5,6, Sky Feuer5, Liang Liang7, Randi A Paynter4,5, Larry Rand8, Kelli K Ryckman9, Roberta L Keller10, Laura L Jelliffe-Pawlowski4,5. 1. Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA. SteurerMullerM@peds.ucsf.edu. 2. Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA. SteurerMullerM@peds.ucsf.edu. 3. California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA. SteurerMullerM@peds.ucsf.edu. 4. Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA. 5. California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA. 6. Department of Pediatrics, University of California San Diego, La Jolla, CA, USA. 7. Department of Genetics, Stanford University, Palo Alto, CA, USA. 8. Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Francisco, San Francisco, CA, USA. 9. Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA, USA. 10. Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA.
Abstract
BACKGROUND: There is an emerging evidence that pulmonary hypertension is associated with amino acid, carnitine, and thyroid hormone aberrations. We aimed to characterize metabolic profiles measured by the newborn screen (NBS) in infants with persistent pulmonary hypertension of the newborn (PPHN) METHODS: Nested case-control study from population-based database. Cases were infants with ICD-9 code for PPHN receiving mechanical ventilation. Controls receiving mechanical ventilation were matched 2:1 for gestational age, sex, birth weight, parenteral nutrition administration, and age at NBS collection. Infants were divided into derivation and validation datasets. A multivariable logistic regression model was derived from candidate metabolites, and the area under the receiver operator characteristic curve (AUROC) was generated from the validation dataset. RESULTS: We identified 1076 cases and 2152 controls. Four metabolites remained in the final model. Ornithine (OR 0.32, CI 0.26-0.41), tyrosine (OR 0.48, CI 0.40-0.58), and TSH 0.50 (0.45-0.55) were associated with decreased odds of PPHN; phenylalanine was associated with increased odds of PPHN (OR 4.74, CI 3.25-6.90). The AUROC was 0.772 (CI 0.737-0.807). CONCLUSIONS: In a large, population-based dataset, infants with PPHN have distinct, early metabolic profiles. These data provide insight into the pathophysiology of PPHN, identifying potential therapeutic targets and novel biomarkers to assess the response.
BACKGROUND: There is an emerging evidence that pulmonary hypertension is associated with amino acid, carnitine, and thyroid hormone aberrations. We aimed to characterize metabolic profiles measured by the newborn screen (NBS) in infants with persistent pulmonary hypertension of the newborn (PPHN) METHODS: Nested case-control study from population-based database. Cases were infants with ICD-9 code for PPHN receiving mechanical ventilation. Controls receiving mechanical ventilation were matched 2:1 for gestational age, sex, birth weight, parenteral nutrition administration, and age at NBS collection. Infants were divided into derivation and validation datasets. A multivariable logistic regression model was derived from candidate metabolites, and the area under the receiver operator characteristic curve (AUROC) was generated from the validation dataset. RESULTS: We identified 1076 cases and 2152 controls. Four metabolites remained in the final model. Ornithine (OR 0.32, CI 0.26-0.41), tyrosine (OR 0.48, CI 0.40-0.58), and TSH 0.50 (0.45-0.55) were associated with decreased odds of PPHN; phenylalanine was associated with increased odds of PPHN (OR 4.74, CI 3.25-6.90). The AUROC was 0.772 (CI 0.737-0.807). CONCLUSIONS: In a large, population-based dataset, infants with PPHN have distinct, early metabolic profiles. These data provide insight into the pathophysiology of PPHN, identifying potential therapeutic targets and novel biomarkers to assess the response.
Authors: Jonathan D Reiss; Alan L Chang; Jonathan A Mayo; Katherine Bianco; Henry C Lee; David K Stevenson; Gary M Shaw; Nima Aghaeepour; Karl G Sylvester Journal: Pediatr Res Date: 2021-10-20 Impact factor: 3.953
Authors: Scott P Oltman; Elizabeth E Rogers; Rebecca J Baer; Elizabeth A Jasper; James G Anderson; Martina A Steurer; Matthew S Pantell; Mark A Petersen; J Colin Partridge; Deborah Karasek; Kharah M Ross; Sky K Feuer; Linda S Franck; Larry Rand; John M Dagle; Kelli K Ryckman; Laura L Jelliffe-Pawlowski Journal: Pediatr Res Date: 2020-10-01 Impact factor: 3.756
Authors: Chengyin Ye; Jinghua Wu; Jonathan D Reiss; Tiffany J Sinclair; David K Stevenson; Gary M Shaw; Donald H Chace; Reese H Clark; Lawrence S Prince; Xuefeng Bruce Ling; Karl G Sylvester Journal: Nutrients Date: 2022-08-28 Impact factor: 6.706