Initial Metabolic Profiles Are Associated with 7-Day Survival among Infants Born at 22-25 Weeks of Gestation

Early Newborn Metabolic Patterning and Sudden Infant Death Syndrome

Importance

Sudden infant death syndrome (SIDS) is a major cause of infant death in the US. Previous research suggests that inborn errors of metabolism may contribute to SIDS, yet the relationship between SIDS and biomarkers of metabolism remains unclear.

Objective

To evaluate and model the association between routinely measured newborn metabolic markers and SIDS in combination with established risk factors for SIDS.

Design, Setting, and Participants

This was a case-control study nested within a retrospective cohort using data from the California Office of Statewide Health Planning and Development and the California Department of Public Health. The study population included infants born in California between 2005 and 2011 with full metabolic data collected as part of routine newborn screening (NBS). SIDS cases were matched to controls at a ratio of 1:4 by gestational age and birth weight z score. Matched data were split into training (2/3) and testing (1/3) subsets. Data were analyzed from January 2005 to December 2011.

Exposures

Metabolites measured by NBS and established risk factors for SIDS.

Main Outcomes and Measures

The primary outcome was SIDS. Logistic regression was used to evaluate the association between metabolic markers combined with known risk factors and SIDS.

Results

Of 2 276 578 eligible infants, 354 SIDS (0.016%) cases (mean [SD] gestational age, 38.3 [2.3] weeks; 220 male [62.1%]) and 1416 controls (mean [SD] gestational age, 38.3 [2.3] weeks; 723 male [51.1%]) were identified. In multivariable analysis, 14 NBS metabolites were significantly associated with SIDS in a univariate analysis: 17-hydroxyprogesterone, alanine, methionine, proline, tyrosine, valine, free carnitine, acetyl-L-carnitine, malonyl carnitine, glutarylcarnitine, lauroyl-L-carnitine, dodecenoylcarnitine, 3-hydroxytetradecanoylcarnitine, and linoleoylcarnitine. The area under the receiver operating characteristic curve for a 14-marker SIDS model, which included 8 metabolites, was 0.75 (95% CI, 0.72-0.79) in the training set and was 0.70 (95% CI, 0.65-0.76) in the test set. Of 32 infants in the test set with model-predicted probability greater than 0.5, a total of 20 (62.5%) had SIDS. These infants had 14.4 times the odds (95% CI, 6.0-34.5) of having SIDS compared with those with a model-predicted probability less than 0.1.

Conclusions and Relevance

Results from this case-control study showed an association between aberrant metabolic analytes at birth and SIDS. These findings suggest that we may be able to identify infants at increased risk for SIDS soon after birth, which could inform further mechanistic research and clinical efforts focused on monitoring and prevention.

The Quartile Levels of Thyroid-stimulating Hormone at Newborn Screening Stratified Risks of Neurodevelopmental Impairment in Extremely Preterm Infants: A Population Cohort Study

BACKGROUND

To evaluate whether thyroid-stimulating hormone (TSH) measured during newborn screening (NBS) at birth and at discharge can be surrogate markers for neurodevelopmental impairment (NDI) in extremely preterm infants.

METHODS

The population cohort enrolled infants born <29 weeks' gestation in 2008-2020 in southern Taiwan. Infants with a maternal history of thyroid disorders and infants who required thyroxine supplementation during hospitalization were excluded. TSH levels measured during NBS at birth and at term-equivalent age (TEA)/discharge were respectively categorized into the lowest quartile, the interquartile range, and the highest quartile, which were correlated to NDI outcomes.

RESULTS

Among 392 patients with paired TSH data, 358 (91%) were prospectively followed until a corrected age of 24 months. At birth, infants with lowest-quartile TSH had higher NDI risks (odds ratio [OR] 2.3; 95% confidence interval [CI], 1.3-4.1, P = 0.004) compared to infants with interquartile-range TSH. Conversely, by TEA/discharge, infants with highest-quartile TSH had increased NDI (OR 1.9; 95% CI, 1.0-3.4, P = 0.03). By paired TSH categories, infants persistently in the lowest TSH quartile (48%; aOR 4.4; 95% CI, 1.4-14.5, P = 0.01) and those with a shift from interquartile range to the highest quartile (32%; aOR 2.7; 95% CI, 1.0-7.4, P = 0.046) had increased NDI risks compared with the reference with consistent interquartile-range TSH.

CONCLUSION

Extremely preterm infants persistently in the lowest-quartile TSH level at birth and at discharge had the highest NDI risk. TSH quartile levels measured during NBS may serve as a population surrogate biomarker for assessing NDI risks in infants born extremely preterm.

A Cohort Study of Gastric Fluid and Urine Metabolomics for the Prediction of Survival in Severe Prematurity

Predicting survival in very preterm infants is critical in clinical medicine and parent counseling. In this prospective cohort study involving 96 very preterm infants, we evaluated whether the metabolomic analysis of gastric fluid and urine samples obtained shortly after birth could predict survival in the first 3 and 15 days of life (DOL), as well as overall survival up to hospital discharge. Gas chromatography-mass spectrometry (GC-MS) profiling was used. Uni- and multivariate statistical analyses were conducted to evaluate significant metabolites and their prognostic value. Differences in several metabolites were identified between survivors and non-survivors at the time points of the study. Binary logistic regression showed that certain metabolites in gastric fluid, including arabitol, and succinic, erythronic and threonic acids, were associated with 15 DOL and overall survival. Gastric glyceric acid was also associated with 15 DOL survival. Urine glyceric acid could predict survival in the first 3 DOL and overall survival. In conclusion, non-surviving preterm infants exhibited a different metabolic profile compared with survivors, demonstrating significant discrimination with the use of GC-MS-based gastric fluid and urine analyses. The results of this study support the usefulness of metabolomics in developing survival biomarkers in very preterm infants.

Newborn screen metabolic panels reflect the impact of common disorders of pregnancy

BACKGROUND

Hypertensive disorders of pregnancy and maternal diabetes profoundly affect fetal and newborn growth, yet disturbances in intermediate metabolism and relevant mediators of fetal growth alterations remain poorly defined. We sought to determine whether there are distinct newborn screen metabolic patterns among newborns affected by maternal hypertensive disorders or diabetes in utero.

METHODS

A retrospective observational study investigating distinct newborn screen metabolites in conjunction with data linked to birth and hospitalization records in the state of California between 2005 and 2010.

RESULTS

A total of 41,333 maternal-infant dyads were included. Infants of diabetic mothers demonstrated associations with short-chain acylcarnitines and free carnitine. Infants born to mothers with preeclampsia with severe features and chronic hypertension with superimposed preeclampsia had alterations in acetylcarnitine, free carnitine, and ornithine levels. These results were further accentuated by size for gestational age designations.

CONCLUSIONS

Infants of diabetic mothers demonstrate metabolic signs of incomplete beta oxidation and altered lipid metabolism. Infants of mothers with hypertensive disorders of pregnancy carry analyte signals that may reflect oxidative stress via altered nitric oxide signaling. The newborn screen analyte composition is influenced by the presence of these maternal conditions and is further associated with the newborn size designation at birth.

IMPACT

Substantial differences in newborn screen analyte profiles were present based on the presence or absence of maternal diabetes or hypertensive disorder of pregnancy and this finding was further influenced by the newborn size designation at birth. The metabolic health of the newborn can be examined using the newborn screen and is heavily impacted by the condition of the mother during pregnancy. Utilizing the newborn screen to identify newborns affected by common conditions of pregnancy may help relate an infant's underlying biological disposition with their clinical phenotype allowing for greater risk stratification and intervention.

Evaluation of heparinized syringes for measuring newborn metabolites in neonates with a central arterial line

Newborn metabolic screening is emerging as a novel method for predicting neonatal morbidity and mortality in neonates born very preterm (<32 weeks gestation). The purpose of our study was to determine if blood collected by an electrolyte-balanced dry lithium heparin syringe, as is routine for blood gas measurements, affects targeted metabolite and biomarker levels. Two blood samples (one collected with a heparinized syringe and the other with a non-heparinized syringe) were obtained at the same time from 20 infants with a central arterial line and tested for 49 metabolites and biomarkers using standard procedures for newborn screening. Overall, the median metabolite levels did not significantly differ by syringe type. However, there was wide variability, particularly for amino acids and immunoreactive trypsinogen, for individual paired samples and therefore, consideration should be given to sample collection when using these metabolites in prediction models of neonatal morbidity and mortality.

Metabolic biomarkers of small and large for gestational age newborns

BACKGROUND

Small for gestational age (SGA) and large for gestational age (LGA) newborns are at increased risk for developmental, metabolic and cardiovascular morbidities.

AIMS

To compare the metabolic biomarkers of SGA and LGA infants with those of appropriate for gestational age (AGA) newborns in order to shed more light on a possible pathogenesis of those morbidities.

STUDY DESIGN

An observational retrospective study.

SUBJECTS

70,809 term newborns divided into AGA, SGA, LGA, and severe subcategories (<3rd percentile or ≥97th percentile).

OUTCOME MEASURES

18 metabolites were measured by dried blood tandem mass spectrometry and compared in between groups in univariate and multivariate logistic regression.

RESULTS

SGA newborns had a significant likelihood for elevated methionine, proline, free carnitine, and reduced valine levels compared to AGA newborns (P < .0001). Severe SGA showed more apparent trends including elevated leucine. LGA newborns had a significant likelihood for low citrulline, glutamine, proline, tyrosine, and elevated leucine levels (P ≤ .0033). Severe LGA newborns showed the same trends, with the exception of citrulline and glutamine.

CONCLUSIONS

SGA and LGA newborns demonstrate distinct metabolic biomarkers in newborn screening. Most of the altered metabolites in the SGA group were elevated while those in the LGA group were decreased in comparison to AGA newborns. These trends were more apparent in the severe SGA subgroup while they mostly remained the same in the severe LGA subgroup. Whether these metabolic changes are involved with or can predict long-term outcome awaits further trials.

Newborn metabolic vulnerability profile identifies preterm infants at risk for mortality and morbidity

BACKGROUND

Identifying preterm infants at risk for mortality or major morbidity traditionally relies on gestational age, birth weight, and other clinical characteristics that offer underwhelming utility. We sought to determine whether a newborn metabolic vulnerability profile at birth can be used to evaluate risk for neonatal mortality and major morbidity in preterm infants.

METHODS

This was a population-based retrospective cohort study of preterm infants born between 2005 and 2011 in California. We created a newborn metabolic vulnerability profile wherein maternal/infant characteristics along with routine newborn screening metabolites were evaluated for their association with neonatal mortality or major morbidity.

RESULTS

Nine thousand six hundred and thirty-nine (9.2%) preterm infants experienced mortality or at least one complication. Six characteristics and 19 metabolites were included in the final metabolic vulnerability model. The model demonstrated exceptional performance for the composite outcome of mortality or any major morbidity (AUC 0.923 (95% CI: 0.917-0.929). Performance was maintained across mortality and morbidity subgroups (AUCs 0.893-0.979).

CONCLUSIONS

Metabolites measured as part of routine newborn screening can be used to create a metabolic vulnerability profile. These findings lay the foundation for targeted clinical monitoring and further investigation of biological pathways that may increase the risk of neonatal death or major complications in infants born preterm.

IMPACT

We built a newborn metabolic vulnerability profile that could identify preterm infants at risk for major morbidity and mortality. Identifying high-risk infants by this method is novel to the field and outperforms models currently in use that rely primarily on infant characteristics. Utilizing the newborn metabolic vulnerability profile for precision clinical monitoring and targeted investigation of etiologic pathways could lead to reductions in the incidence and severity of major morbidities associated with preterm birth.

Prognostic Models Predicting Mortality in Preterm Infants: Systematic Review and Meta-analysis

CONTEXT

Prediction models can be a valuable tool in performing risk assessment of mortality in preterm infants.

OBJECTIVE

Summarizing prognostic models for predicting mortality in very preterm infants and assessing their quality.

DATA SOURCES

Medline was searched for all articles (up to June 2020).

STUDY SELECTION

All developed or externally validated prognostic models for mortality prediction in liveborn infants born <32 weeks' gestation and/or <1500 g birth weight were included.

DATA EXTRACTION

Data were extracted by 2 independent authors. Risk of bias (ROB) and applicability assessment was performed by 2 independent authors using Prediction model Risk of Bias Assessment Tool.

RESULTS

One hundred forty-two models from 35 studies reporting on model development and 112 models from 33 studies reporting on external validation were included. ROB assessment revealed high ROB in the majority of the models, most often because of inadequate (reporting of) analysis. Internal and external validation was lacking in 41% and 96% of these models. Meta-analyses revealed an average C-statistic of 0.88 (95% confidence interval [CI]: 0.83-0.91) for the Clinical Risk Index for Babies score, 0.87 (95% CI: 0.81-0.92) for the Clinical Risk Index for Babies II score, and 0.86 (95% CI: 0.78-0.92) for the Score for Neonatal Acute Physiology Perinatal Extension II score.

LIMITATIONS

Occasionally, an external validation study was included, but not the development study, because studies developed in the presurfactant era or general NICU population were excluded.

CONCLUSIONS

Instead of developing additional mortality prediction models for preterm infants, the emphasis should be shifted toward external validation and consecutive adaption of the existing prediction models.

The independent and combined influences of small for gestational age and socioeconomic status on newborn metabolite levels

OBJECTIVES

To determine whether socioeconomic status (SES) and small birthweight for gestational age (SGA) exhibit independent or joint effects on infant levels of 42 metabolites.

STUDY DESIGN

Population-based retrospective cohort of metabolic newborn screening information linked to hospital discharge data. SGA infants defined by birthweight <10th percentile for gestational age by sex. SES was determined by a combined metric including education level, participation in the WIC nutritional assistance program, and receiving California MediCal insurance. We performed linear regression to determine the effects of SES independently, SGA independently, and the interaction of SGA and SES on 42 newborn metabolite levels.

RESULTS

736,435 California infants born in 2005-2011 were included in the analysis. SGA was significantly associated with 36 metabolites. SES was significantly associated with 41 of 42 metabolites. Thirty-eight metabolites exhibited a dose-response relationship between SGA and metabolite levels as SES worsened. Fourteen metabolites showed significant interaction between SES and SGA. Eight metabolites showed significant individual and joint effects of SES and SGA: alanine, glycine, free carnitine, C-3DC, C-5DC, C-16:1, C-18:1, and C-18:2.

CONCLUSIONS

SES and SGA exhibited independent effects on a majority of metabolites and joint effects on select metabolites. A better understanding of how SES and SGA status are related to infant metabolites may help identify maternal and newborn interventions that can lead to better outcomes for infants born SGA.

Association between newborn screening analytes and hypoxic ischemic encephalopathy

Hypoxic ischemic encephalopathy (HIE) is a major cause of neonatal mortality and morbidity. Our study sought to examine whether patterns of newborn screening analytes differed between infants with and without neonatal HIE in order to identify opportunities for potential use of these analytes for diagnosis in routine clinical practice. We linked a population-based newborn screening registry with health databases to identify cases of HIE among term infants (≥37 weeks' gestation) in Ontario from 2010-2015. Correlations between HIE and screening analytes were examined using multivariable logistic regression models containing clinical factors and individual screening analytes (acyl-carnitines, amino acids, fetal-to-adult hemoglobin ratio, endocrine markers, and enzymes). Among 731,841 term infants, 3,010 were diagnosed with HIE during the neonatal period. Multivariable models indicated that clinical variables alone or in combination with hemoglobin values were not associated with HIE diagnosis. Although the model was improved after adding acyl-carnitines and amino acids, the ability of the model to identify infants with HIE was moderate. Our findings indicate that analytes associated with catabolic stress were altered in infants with HIE; however, future research is required to determine whether amino acid and acyl-carnitine profiles could hold clinical utility in the early diagnosis or clinical management of HIE. In particular, further research should examine whether cord blood analyses can be used to identify HIE within a clinically useful timeframe or to guide treatment and predict long-term health outcomes.

Association between newborn screening analyte profiles and infant mortality

Objective

To assess whether newborn screening analytes could be utilized beyond their traditional application to identify infants at high risk of mortality within the first 6 months of life.

Methods

We linked a province-wide newborn screening registry with health administrative databases to identify infant deaths within 6 months in a source population of live-born infants between 2010 and 2014. We used a nested case-control study design, in which all infant deaths between 7 days and 6 months of age were included as cases, and a random sample of infants from the source population were selected as controls and were matched to cases at a ratio of 10:1. We examined the association between mortality and screening analytes (acylcarnitines, amino acids, fetal-to-adult hemoglobin ratio, endocrine markers, and enzymes) using lasso regression to fit multivariable models.

Results

Among 350 infant deaths between 7 days and 6 months of age, and 3498 matched controls with complete data, our multivariable model demonstrated only modest ability to identify infant deaths (optimism-corrected c-statistic: 0.61, 95% confidence interval: 0.50–0.71).

Conclusions

We did not find newborn screening analytes to be strongly predictive of infant mortality between 7 days and 6 months of age in the general population of newborns. Future studies should investigate whether predictive modeling within more homogeneous cause-of-death categories could lead to improved predictive ability for infant mortality.

Targeted urine metabolomics in preterm neonates with intraventricular hemorrhage

Intraventricular hemorrhage (IVH) is a major cause of morbidity and mortality in preterm neonates. Elucidation of the mechanisms underlying IVH and/or development of disease biomarkers is essential. The aim of the study was to investigate the urine metabolic profile of preterm neonates (gestational age < 32 weeks) IVH and explore the role of metabolomics in understanding pathophysiological mechanisms of the disease from which novel biomarkers could be derived. In this single-center, prospective, case-control study, urine samples were collected from seven preterm infants with early IVH (IVH group) and from 11 preterm ones without IVH (control group) on days 1, 3 and 9 of life. Urine metabolites were evaluated using targeted liquid chromatography-tandem mass spectrometry. Demographic and perinatal-clinical characteristics were recorded. Univariate and multivariate statistical analyses were performed. Orthogonal Partial Least Squares-Discriminant Analysis showed that the study groups differed significantly due to alternation in 20 out of the 40 metabolites detected in the urine. Elevated differentiated metabolites included energy intermediates and other important compounds, whereas reduced ones various amino acids, hypoxanthine and nicotinamide. A set of metabolites showed high performance as indicators of IVH, especially during day 1. As evidenced by metabolomics, preterm neonates with IVH demonstrate significant metabolism perturbations. Potentially, a selected panel of metabolites could be used as urine biomarkers of IVH development and/or progression in high-risk preterm infants.