Gestational dating by metabolic profile at birth: a California cohort study

Longitudinal urine metabolic profiling and gestational age prediction in human pregnancy

Pregnancy is a vital period affecting both maternal and fetal health, with impacts on maternal metabolism, fetal growth, and long-term development. While the maternal metabolome undergoes significant changes during pregnancy, longitudinal shifts in maternal urine have been largely unexplored. In this study, we applied liquid chromatography-mass spectrometry-based untargeted metabolomics to analyze 346 maternal urine samples collected throughout pregnancy from 36 women with diverse backgrounds and clinical profiles. Key metabolite changes included glucocorticoids, lipids, and amino acid derivatives, indicating systematic pathway alterations. We also developed a machine learning model to accurately predict gestational age using urine metabolites, offering a non-invasive pregnancy dating method. Additionally, we demonstrated the ability of the urine metabolome to predict time-to-delivery, providing a complementary tool for prenatal care and delivery planning. This study highlights the clinical potential of urine untargeted metabolomics in obstetric care.

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.

Overview of the Global and US Burden of Preterm Birth

Preterm birth (PTB) is the leading cause of morbidity and mortality in children globally, yet its prevalence has been difficult to accurately estimate due to unreliable methods of gestational age dating, heterogeneity in counting, and insufficient data. The estimated global PTB rate in 2020 was 9.9% (95% confidence interval: 9.1, 11.2), which reflects no significant change from 2010, and 81% of prematurity-related deaths occurred in Africa and Asia. PTB prevalence in the United States in 2021 was 10.5%, yet with concerning racial disparities. Few effective solutions for prematurity prevention have been identified, highlighting the importance of further research.

Estimating Gestational Age and Prediction of Preterm Birth Using Metabolomics Biomarkers

Preterm birth (PTB) is a leading cause of morbidity and mortality in children aged under 5 years globally, especially in low-resource settings. It remains a challenge in many low-income and middle-income countries to accurately measure the true burden of PTB due to limited availability of accurate measures of gestational age (GA), first trimester ultrasound dating being the gold standard. Metabolomics biomarkers are a promising area of research that could provide tools for both early identification of high-risk pregnancies and for the estimation of GA and preterm status of newborns postnatally.

Accuracy of prenatal and postnatal biomarkers for estimating gestational age: a systematic review and meta-analysis

Background

Knowledge of gestational age (GA) is key in clinical management of individual obstetric patients, and critical to be able to calculate rates of preterm birth and small for GA at a population level. Currently, the gold standard for pregnancy dating is measurement of the fetal crown rump length at 11-14 weeks of gestation. However, this is not possible for women first presenting in later pregnancy, or in settings where routine ultrasound is not available. A reliable, cheap and easy to measure GA-dependent biomarker would provide an important breakthrough in estimating the age of pregnancy. Therefore, the aim of this study was to determine the accuracy of prenatal and postnatal biomarkers for estimating gestational age (GA).

Methods

Systematic review prospectively registered with PROSPERO (CRD42020167727) and reported in accordance with the PRISMA-DTA. Medline, Embase, CINAHL, LILACS, and other databases were searched from inception until September 2023 for cohort or cross-sectional studies that reported on the accuracy of prenatal and postnatal biomarkers for estimating GA. In addition, we searched Google Scholar and screened proceedings of relevant conferences and reference lists of identified studies and relevant reviews. There were no language or date restrictions. Pooled coefficients of correlation and root mean square error (RMSE, average deviation in weeks between the GA estimated by the biomarker and that estimated by the gold standard method) were calculated. The risk of bias in each included study was also assessed.

Findings

Thirty-nine studies fulfilled the inclusion criteria: 20 studies (2,050 women) assessed prenatal biomarkers (placental hormones, metabolomic profiles, proteomics, cell-free RNA transcripts, and exon-level gene expression), and 19 (1,738,652 newborns) assessed postnatal biomarkers (metabolomic profiles, DNA methylation profiles, and fetal haematological components). Among the prenatal biomarkers assessed, human chorionic gonadotrophin measured in maternal serum between 4 and 9 weeks of gestation showed the highest correlation with the reference standard GA, with a pooled coefficient of correlation of 0.88. Among the postnatal biomarkers assessed, metabolomic profiling from newborn blood spots provided the most accurate estimate of GA, with a pooled RMSE of 1.03 weeks across all GAs. It performed best for term infants with a slightly reduced accuracy for preterm or small for GA infants. The pooled RMSEs for metabolomic profiling and DNA methylation profile from cord blood samples were 1.57 and 1.60 weeks, respectively.

Interpretation

We identified no antenatal biomarkers that accurately predict GA over a wide window of pregnancy. Postnatally, metabolomic profiling from newborn blood spot provides an accurate estimate of GA, however, as this is known only after birth it is not useful to guide antenatal care. Further prenatal studies are needed to identify biomarkers that can be used in isolation, as part of a biomarker panel, or in combination with other clinical methods to narrow prediction intervals of GA estimation.

Funding

The research was funded by the Bill and Melinda Gates Foundation (INV-000368). ATP is supported by the Oxford Partnership Comprehensive Biomedical Research Centre with funding from the NIHR Biomedical Research Centre funding scheme. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, the Department of Health, or the Department of Biotechnology. The funders of this study had no role in study design, data collection, analysis or interpretation of the data, in writing the paper or the decision to submit for publication.

Developing a resiliency model for survival without major morbidity in preterm infants

OBJECTIVE

Develop and validate a resiliency score to predict survival and survival without neonatal morbidity in preterm neonates <32 weeks of gestation using machine learning.

STUDY DESIGN

Models using maternal, perinatal, and neonatal variables were developed using LASSO method in a population based Californian administrative dataset. Outcomes were survival and survival without severe neonatal morbidity. Discrimination was assessed in the derivation and an external dataset from a tertiary care center.

RESULTS

Discrimination in the internal validation dataset was excellent with a c-statistic of 0.895 (95% CI 0.882-0.908) for survival and 0.867 (95% CI 0.857-0.877) for survival without severe neonatal morbidity, respectively. Discrimination remained high in the external validation dataset (c-statistic 0.817, CI 0.741-0.893 and 0.804, CI 0.770-0.837, respectively).

CONCLUSION

Our successfully predicts survival and survival without major morbidity in preterm babies born at <32 weeks. This score can be used to adjust for multiple variables across administrative datasets.

Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers

BACKGROUND

Accurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical and metabolomic data.

METHODS

We derived three GA estimation models using ELASTIC NET multivariable linear regression using metabolomic markers from heel-prick blood samples and clinical data from a retrospective cohort of newborns from Ontario, Canada. We conducted internal model validation in an independent cohort of Ontario newborns, and external validation in heel prick and cord blood sample data collected from newborns from prospective birth cohorts in Lusaka, Zambia and Matlab, Bangladesh. Model performance was measured by comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound.

RESULTS

Samples were collected from 311 newborns from Zambia and 1176 from Bangladesh. The best-performing model accurately estimated GA within about 6 days of ultrasound estimates in both cohorts when applied to heel prick data (MAE 0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for Bangladesh), and within about 7 days when applied to cord blood data (1.02 weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh).

CONCLUSIONS

Algorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data.

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.

Machine learning prediction of gestational age from metabolic screening markers resistant to ambient temperature transportation: Facilitating use of this technology in low resource settings of South Asia and East Africa

Background

Knowledge of gestational age is critical for guiding preterm neonatal care. In the last decade, metabolic gestational dating approaches emerged in response to a global health need; because in most of the developing world, accurate antenatal gestational age estimates are not feasible. These methods initially developed in North America have now been externally validated in two studies in developing countries, however, require shipment of samples at sub-zero temperature.

Methods

A subset of 330 pairs of heel prick dried blood spot samples were shipped on dry ice and in ambient temperature from field sites in Tanzania, Bangladesh and Pakistan to laboratory in Iowa (USA). We evaluated impact on recovery of analytes of shipment temperature, developed and evaluated models for predicting gestational age using a limited set of metabolic screening analytes after excluding 17 analytes that were impacted by shipment conditions of a total of 44 analytes.

Results

With the machine learning model using all the analytes, samples shipped in dry ice yielded a Root Mean Square Error (RMSE) of 1.19 weeks compared to 1.58 weeks for samples shipped in ambient temperature. Out of the 44 screening analytes, recovery of 17 analytes was significantly different between the two shipment methods and these were excluded from further machine learning model development. The final model, restricted to stable analytes provided a RMSE of 1.24 (95% confidence interval (CI) = 1.10-1.37) weeks for samples shipped on dry ice and RMSE of 1.28 (95% CI = 1.15-1.39) for samples shipped at ambient temperature. Analysis for discriminating preterm births (gestational age <37 weeks), yielded an area under curve (AUC) of 0.76 (95% CI = 0.71-0.81) for samples shipped on dry ice and AUC of 0.73 (95% CI = 0.67-0.78) for samples shipped in ambient temperature.

Conclusions

In this study, we demonstrate that machine learning algorithms developed using a sub-set of newborn screening analytes which are not sensitive to shipment at ambient temperature, can accurately provide estimates of gestational age comparable to those from published regression models from North America using all analytes. If validated in larger samples especially with more newborns <34 weeks, this technology could substantially facilitate implementation in LMICs.

Targeted newborn metabolomics: prediction of gestational age from cord blood

OBJECTIVE

Our study sought to determine whether metabolites from a retrospective collection of banked cord blood specimens could accurately estimate gestational age and to validate these findings in cord blood samples from Busia, Uganda.

STUDY DESIGN

Forty-seven metabolites were measured by tandem mass spectrometry or enzymatic assays from 942 banked cord blood samples. Multiple linear regression was performed, and the best model was used to predict gestational age, in weeks, for 150 newborns from Busia, Uganda.

RESULTS

The model including metabolites and birthweight, predicted the gestational ages within 2 weeks for 76.7% of the Ugandan cohort. Importantly, this model estimated the prevalence of preterm birth <34 weeks closer to the actual prevalence (4.67% and 4.00%, respectively) than a model with only birthweight which overestimates the prevalence by 283%.

CONCLUSION

Models that include cord blood metabolites and birth weight appear to offer improvement in gestational age estimation over birth weight alone.

Gestational Age, Newborn Metabolic Markers and Academic Achievement

BACKGROUND

Gestational age is associated with greater school achievement and variation in newborn metabolic markers. Whether metabolic markers are related to gestational age differences in achievement is unknown. This study examines whether newborn metabolic markers are associated with gestational age differences in performance on standardized school tests.

METHODS

This retrospective cohort study linked birth certificates of children born in Iowa between 2002 and 2010 to newborn screening records and school tests between 2009 and 2018. The analytical sample includes up to 229,679 children and 973,247 child-grade observations. Regression models estimate the associations between gestational age and 37 newborn metabolic markers with national percentile ranking (NPR) scores on math, reading comprehension, and science tests.

RESULTS

An additional gestational week is associated with 0.6 (95% CI: 0.6, 0.7), 0.5 (95% CI: 0.4, 0.5), and 0.4 (95% CI: 0.4, 0.5) higher NPRs on math, reading, and science, respectively. Compared to full term children (37-44 weeks), preterm children (32-36 weeks) have 2.2 (95% CI: -2.6, -1.8), 1.5 (95% CI: -1.9, -1.1), and 1.0 (95% CI: -1.4, -0.7) lower NPRs on math, reading comprehension, and science. Very preterm children (20-31 weeks) have 8.3 (95% CI: -9.4, -7.2), 5.2 (95% CI: -6.2, -4.0), and 4.7 (95% CI: -5.6, -3.8) lower NPRs than full term children on math, reading, and science. Metabolic markers are associated with 27%, 36%, and 45% of gestational age differences in math, reading, and science scores, respectively, and over half of the difference in test scores between preterm or very preterm and full term children.

CONCLUSIONS

Newborn metabolic markers are strongly related to gestational age differences in school test scores, suggesting that early metabolic differences are important markers of long-term child development.

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.

Real world external validation of metabolic gestational age assessment in Kenya

Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick samples, our best-performing model estimated GA within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35 (95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was 2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI 1.36, 1.53)). Accuracy was not impacted by maternal HIV status and improved when the dating ultrasound occurred between 9 and 13 weeks of gestation, in both heel prick and cord blood data (overall MAE 1.04 (95% CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). The accuracy of metabolic model based GA estimates in the Kenya cohort was lower compared to our previously published validation studies, however inconsistency in the timing of reference dating ultrasounds appears to have been a contributing factor to diminished model performance.

Immunoreactive Trypsinogen and Free Carnitine Changes on Newborn Screening after Birth in Patients Who Develop Type 1 Diabetes

Are free carnitine concentrations on newborn screening (NBS) 48–72 h after birth lower in patients who develop type 1 diabetes than in controls? A retrospective case-control study of patients with type 1 diabetes was conducted. NBS results of patients from a Sydney hospital were compared against matched controls from the same hospital (1:5). Multiple imputation was performed for estimating missing data (gestational age) using gender and birthweight. Conditional logistic regression was used to control for confounding and to generate parameter estimates (α = 0.05). The Hommel approach was used for post-hoc analyses. Results are reported as medians and interquartile ranges. A total of 159 patients were eligible (80 females). Antibodies were detectable in 86. Median age at diagnosis was 8 years. Free carnitine concentrations were lower in patients than controls (25.50 µmol/L;18.98–33.61 vs. 27.26; 21.22–34.86 respectively) (p = 0.018). Immunoreactive trypsinogen was higher in this group (20.24 µg/L;16.15–29–52 vs. 18.71; 13.96–26.92) (p = 0.045), which did not persist in the post-hoc analysis. Carnitine levels are lower and immunoreactive trypsinogen might be higher, within 2–3 days of birth and years before development of type 1 diabetes as compared to controls, although the differences were well within reference ranges and provide insight into the pathogenesis into neonatal onset of type 1 diabetes development rather than use as a diagnostic tool. Given trypsinogen’s use for evaluation of new-onset type 1 diabetes, larger studies are warranted.

Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa

BACKGROUND

Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound examination is not available in these settings, gestational age (GA) is estimated using new-born assessment, last menstrual period (LMP) recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1-2 weeks of ultrasonography-based GA. We sought to leverage machine learning algorithms to improve accuracy and applicability of this approach to LMICs settings.

METHODS

This study uses data from AMANHI-ACT, a prospective pregnancy cohorts in Asia and Africa where early pregnancy ultrasonography estimated GA and birth weight are available and metabolite screening data in a subset of 1318 new-borns were also available. We utilized this opportunity to develop machine learning (ML) algorithms. Random Forest Regressor was used where data was randomly split into model-building and model-testing dataset. Mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate performance. Bootstrap procedures were used to estimate confidence intervals (CI) for RMSE and MAE. For pre-term birth identification ROC analysis with bootstrap and exact estimation of CI for area under curve (AUC) were performed.

RESULTS

Overall model estimated GA had MAE of 5.2 days (95% CI 4.6-6.8), which was similar to performance in SGA, MAE 5.3 days (95% CI 4.6-6.2). GA was correctly estimated to within 1 week for 85.21% (95% CI 72.31-94.65). For preterm birth classification, AUC in ROC analysis was 98.1% (95% CI 96.0-99.0; p < 0.001). This model performed better than Iowa regression, AUC Difference 14.4% (95% CI 5-23.7; p = 0.002).

CONCLUSIONS

Machine learning algorithms and models applied to metabolomic gestational age dating offer a ladder of opportunity for providing accurate population-level gestational age estimates in LMICs settings. These findings also point to an opportunity for investigation of region-specific models, more focused feasible analyte models, and broad untargeted metabolome investigation.

Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal gestational age estimation

BACKGROUND

Globally, 15 million infants are born preterm and another 23.2 million infants are born small for gestational age (SGA). Determining burden of preterm and SGA births, is essential for effective planning, modification of health policies and targeting interventions for reducing these outcomes for which accurate estimation of gestational age (GA) is crucial. Early pregnancy ultrasound measurements, last menstrual period and post-natal neonatal examinations have proven to be not feasible or inaccurate. Proposed algorithms for GA estimation in western populations, based on routine new-born screening, though promising, lack validation in developing country settings. We evaluated the hypothesis that models developed in USA, also predicted GA in cohorts of South Asia (575) and Sub-Saharan Africa (736) with same precision.

METHODS

Dried heel prick blood spots collected 24-72 hours after birth from 1311 new-borns, were analysed for standard metabolic screen. Regression algorithm based, GA estimates were computed from metabolic data and compared to first trimester ultrasound validated, GA estimates (gold standard).

RESULTS

Overall Algorithm (metabolites + birthweight) estimated GA to within an average deviation of 1.5 weeks. The estimated GA was within the gold standard estimate by 1 and 2 weeks for 70.5% and 90.1% new-borns respectively. Inclusion of birthweight in the metabolites model improved discriminatory ability of this method, and showed promise in identifying preterm births. Receiver operating characteristic (ROC) curve analysis estimated an area under curve of 0.86 (conservative bootstrap 95% confidence interval (CI) = 0.83 to 0.89); P < 0.001) and Youden Index of 0.58 (95% CI = 0.51 to 0.64) with a corresponding sensitivity of 80.7% and specificity of 77.6%.

CONCLUSION

Metabolic gestational age dating offers a novel means for accurate population-level gestational age estimates in LMIC settings and help preterm birth surveillance initiatives. Further research should focus on use of machine learning and newer analytic methods broader than conventional metabolic screen analytes, enabling incorporation of region-specific analytes and cord blood metabolic profiles models predicting gestational age accurately.

External validation of machine learning models including newborn metabolomic markers for postnatal gestational age estimation in East and South-East Asian infants

Background: Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted.  

Methods: This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent sample of Canadian infants, and externally validated in infant cohorts from the Philippines and China. 

Results: Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China.  For the full model including sex, birth weight and metabolomic markers, GA estimates were within ±5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within ±6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model.

Conclusions: Accuracy of metabolic GA algorithms was attenuated when applied in external settings.  Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility

External validation of machine learning models including newborn metabolomic markers for postnatal gestational age estimation in East and South-East Asian infants

Background: Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted.   Methods: This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent sample of Canadian infants, and externally validated in infant cohorts from the Philippines and China.  Results: Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China.  For the full model including sex, birth weight and metabolomic markers, GA estimates were within ±5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within ±6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model. Conclusions: Accuracy of metabolic GA algorithms was attenuated when applied in external settings.  Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility.

Metabolites from midtrimester plasma of pregnant patients at high risk for preterm birth

BACKGROUND

There is an increased awareness regarding the association between exposure to environmental contaminants and adverse pregnancy outcomes including preterm birth. Whether an individual's metabolic profile can be utilized during pregnancy to differentiate the subset of patients who are ultimately destined to delivered preterm remains uncertain but could have MEANINGFUL clinical implications.

OBJECTIVE

We sought to objectively quantify metabolomic profiles of patients at high risk of preterm birth by evaluating midtrimester maternal plasma and to measure whether endogenous metabolites and exogenous environmental substances differ among those who ultimately deliver preterm compared with those who deliver at term.

STUDY DESIGN

This was a case-control analysis from a prospective cohort of patients carrying a singleton, nonanomalous gestation who were at high risk of spontaneous preterm birth. Subjects with a plasma blood sample drawn at <28 weeks' gestation and no evidence of preterm labor at the time of enrollment were included. Metabolites were extracted from frozen samples, and metabolomic analysis was performed using liquid chromatography/mass spectrometry. The primary outcome was preterm birth at 16.0 to 36.9 weeks' gestation.

RESULTS

A total of 42 patients met the inclusion criteria. Of these, 25 (59.5%) delivered preterm at <37 weeks' gestation, at a median of 30.14 weeks' gestation (interquartile range, 28.14-34.14). A total of 812 molecular features differed between preterm birth cases and term controls with a minimum fold change of 1.2 and P<.05. Of these, 570 of 812 (70.1%) were found in higher abundances in preterm birth cases; the other 242 of 812 (29.9%) were in higher abundance in term birth controls. The identity of the small molecule/compound represented by the molecular features differing statistically between preterm birth cases and term controls was identified as ranging from those involved with endogenous metabolic pathways (including lipid catabolism, steroids, and steroid-related molecules) to exogenous exposures (including avocadyne, diosgenin, polycyclic aromatic hydrocarbons, acetaminophen metabolites, aspartame, and caffeine). Random forest analyses evaluating the relative contribution of each of the top 30 compounds in differentiating preterm birth and term controls accurately classified 21 of 25 preterm birth cases (84%).

CONCLUSION

Both endogenous metabolites and exogenous exposures differ in maternal plasma in the midtrimester among patients who ultimately delivered preterm compared with those who deliver at term.

Gestational age-dependent development of the neonatal metabolome

BACKGROUND

Prematurity is a severe pathophysiological condition, however, little is known about the gestational age-dependent development of the neonatal metabolome.

METHODS

Using an untargeted liquid chromatography-tandem mass spectrometry metabolomics protocol, we measured over 9000 metabolites in 298 neonatal residual heel prick dried blood spots retrieved from the Danish Neonatal Screening Biobank. By combining multiple state-of-the-art metabolome mining tools, we retrieved chemical structural information at a broad level for over 5000 (60%) metabolites and assessed their relation to gestational age.

RESULTS

A total of 1459 (~16%) metabolites were significantly correlated with gestational age (false discovery rate-adjusted P < 0.05), whereas 83 metabolites explained on average 48% of the variance in gestational age. Using a custom algorithm based on hypergeometric testing, we identified compound classes (617 metabolites) overrepresented with metabolites correlating with gestational age (P < 0.05). Metabolites significantly related to gestational age included bile acids, carnitines, polyamines, amino acid-derived compounds, nucleotides, phosphatidylcholines and dipeptides, as well as treatment-related metabolites, such as antibiotics and caffeine.

CONCLUSIONS

Our findings elucidate the gestational age-dependent development of the neonatal blood metabolome and suggest that the application of metabolomics tools has great potential to reveal novel biochemical underpinnings of disease and improve our understanding of complex pathophysiological mechanisms underlying prematurity-associated disorders.

IMPACT

A large variation in the neonatal dried blood spot metabolome from residual heel pricks stored at the Danish Neonatal Screening Biobank can be explained by gestational age. While previous studies have assessed the relation of selected metabolic markers to gestational age, this study assesses metabolome-wide changes related to prematurity. Using a combination of recently developed metabolome mining tools, we assess the relation of over 9000 metabolic features to gestational age. The ability to assess metabolome-wide changes related to prematurity in neonates could pave the way to finding novel biochemical underpinnings of health complications related to preterm birth.

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.

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.

Investigating the Metabolic Model in Preterm Neonates by Tandem Mass Spectrometry: A Cohort Study

The changes of metabolite profiles in preterm birth have been demonstrated using newborn screening data. However, little is known about the holistic metabolic model in preterm neonates. The aim was to investigate the holistic metabolic model in preterm neonates. All metabolite values were obtained from a cohort data of routine newborn screening. A total of 261 758 newborns were recruited and randomly divided into a training subset and a testing subset. Using the training subset, 949 variates were considered to establish a logistic regression model for identifying preterm birth (<37 weeks) from term birth (≥37 weeks). Sventy-two variates (age at collection, TSH, 17α-OHP, proline, tyrosine, C16:1-OH, C18:2, and 65 ratios) entered into the final metabolic model for identifying preterm birth from term birth. Among the variates entering into the final model of PTB [Leucine+Isoleucine+Proline-OH)/Valine (OR=38.36], (C3DC+C4-OH)/C12 (OR=15.58), Valine/C5 (OR=6.32), [Leucine+isoleucine+Proline-OH)/Ornithine (OR=2.509)], and Proline/C18:1 (OR=2.465) have the top five OR values, and [Leucine+Isoleucine+Proline-OH)/C5 (OR=0.05)], [Leucine+Isoleucine+Proline-OH)/Phenylalanine (OR=0.214)], proline/valine (OR=0.230), C16/C18 (OR=0.259), and Alanine/free carnitine (OR=0.279) have the five lowest OR values. The final metabolic model had a capacity of identifying preterm infants with >80% accuracy in both the training and testing subsets. When identifying neonates ≤32 weeks from those >32 weeks, it had a robust performance with nearly 95% accuracy in both subsets. In summary, we have established an excellent metabolic model in preterm neonates. These findings could provide new insights for more efficient nutrient supplements and etiology of preterm birth.

Metabolic gestational age assessment in low resource settings: a validation protocol

Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children's Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario's newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable.

Maternal metabolic profiling to assess fetal gestational age and predict preterm delivery: a two-centre retrospective cohort study in the US

OBJECTIVES

The aim of this study was to develop a single blood test that could determine gestational age and estimate the risk of preterm birth by measuring serum metabolites. We hypothesised that serial metabolic modelling of serum analytes throughout pregnancy could be used to describe fetal gestational age and project preterm birth with a high degree of precision.

STUDY DESIGN

A retrospective cohort study.

SETTING

Two medical centres from the USA.

PARTICIPANTS

Thirty-six patients (20 full-term, 16 preterm) enrolled at Stanford University were used to develop gestational age and preterm birth risk algorithms, 22 patients (9 full-term, 13 preterm) enrolled at the University of Alabama were used to validate the algorithms.

OUTCOME MEASURES

Maternal blood was collected serially throughout pregnancy. Metabolic datasets were generated using mass spectrometry.

RESULTS

A model to determine gestational age was developed (R²=0.98) and validated (R²=0.81). 66.7% of the estimates fell within ±1 week of ultrasound results during model validation. Significant disruptions from full-term pregnancy metabolic patterns were observed in preterm pregnancies (R²=-0.68). A separate algorithm to predict preterm birth was developed using a set of 10 metabolic pathways that resulted in an area under the curve of 0.96 and 0.92, a sensitivity of 0.88 and 0.86, and a specificity of 0.96 and 0.92 during development and validation testing, respectively.

CONCLUSIONS

In this study, metabolic profiling was used to develop and test a model for determining gestational age during full-term pregnancy progression, and to determine risk of preterm birth. With additional patient validation studies, these algorithms may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights into the pathophysiology of preterm birth. Metabolic pathway-based pregnancy modelling is a novel modality for investigation and clinical application development.

Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification

Background

Preterm birth complications are the leading cause of death among children under 5 years of age, and this imposes a heavy burden on healthcare and social systems, particularly in low- and middle-income countries where reliable estimates of gestational age may be difficult to obtain. Metabolic analyte data can aid in accurately estimating gestational age. However, important costs are associated with this approach, which are related to the collection and analysis of newborn samples, and its cost-effectiveness has yet to be determined.

Objective

This study aimed to evaluate the cost-effectiveness of an internationally validated gestational age estimation algorithm based on neonatal blood spot metabolite data in combination with clinical and demographic variables (birthweight, sex, and multiple birth status) compared with a basic algorithm that uses only clinical and demographic variables in classifying infants as preterm or term (using a 37-week dichotomous preterm or term classification) and determining gestational age.

Study Design

The cost per correctly classified preterm infant and per correctly classified small-for-gestational-age infant for the metabolic algorithm vs the basic algorithm were estimated with data from an implementation study in Bangladesh.

Results

Over 1 year, the metabolic algorithm correctly classified an average of 8.7 (95% confidence interval, 1.3–14.7) additional preterm infants and 145.3 (95% confidence interval, 128.0–164.7) additional small-for-gestational-age infants per 1323 infants screened compared with the basic algorithm using only clinical and demographic variables. The incremental annual cost of adopting the metabolic algorithm was $100,031 (95% confidence interval, $86,354–$115,725). If setup costs were included, the cost was $120,496 (95% confidence interval, $106,322–$136,656). Compared with the basic algorithm, the incremental cost per preterm infant correctly classified by the metabolic algorithm is $11,542 ($13,903 with setup), and the incremental cost per small-for-gestational-age infant is $688 ($829 with setup).

Conclusion

This research quantifies the cost per detection of preterm or small-for-gestational-age infant in the implementation of a newborn screening program to aid in improved classification of preterm and, in particular, small-for-gestational-age infants in low- and middle-income countries.

Newborn Screening Samples for Diabetes Research: An Underused Resource

Inborn errors of metabolism and diabetes share common derangements in analytes of metabolic networks that are tested for in newborn screening, usually performed 48-72 h after birth. There is limited research examining the metabolic imprint of diabetes on newborn screening results. This paper aims to demonstrate the links between diabetes, biochemical genetics and newborn screening in investigating disease pathophysiology in diabetes, provide possible reasons for the lack of research in diabetes in newborn screening and offer recommendations on potential research areas. We performed a systematic search of the available literature from 1 April 1998 to 31 December 2018 involving newborn screening and diabetes using OVID, MEDLINE, Cochrane and the PROSPERO register, utilizing a modified extraction tool adapted from Cochrane. Eight studies were included after screening 1312 records. Five studies reanalyzed dried blood spots (DBS) on filter paper cards, and three studies utilized pre-existing results. The results of these studies and how they relate to cord blood studies, the use of cord blood versus newborn screening dried blood spots as a sample and considerations on newborn screening and diabetes research is further discussed. The timing of sampling of newborn screening allows insight into neonatal physiology in a catabolic state with minimal maternal and placental influence. This, combined with the wide coverage of newborn screening worldwide, may aid in our understanding of the origins of diabetes.

Metabolic gestational age assessment in low resource settings: a validation protocol

Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children’s Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario’s newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Outcomes of pulmonary vascular disease in infants conceived with non-IVF fertility treatment and assisted reproductive technologies at 1 year of age

BACKGROUND

Assisted reproductive technologies (ARTs) have been associated with the development of endothelial dysfunction.

OBJECTIVE

To determine potential differences in outcomes associated with pulmonary vascular disease in infants born to mothers receiving any infertility treatment including ART and non-IVF fertility treatments (NIFTs).

DESIGN/METHODS

The sample was derived from an administrative database containing detailed information on infant and maternal characteristics for live-born infants in California (2007-2012) with gestational age (GA) 22 to 44 weeks. Cases were defined as infants with ICD-9 code for pulmonary vascular disease (PVD) and records for ART/NIFT. Controls were randomly selected at a 1:4 ratio. The primary outcome was 1-year mortality. Crude and adjusted odds ratio (OR) with 95% confidence interval (CI) were calculated.

RESULTS

We identified 159 cases and 636 controls. Mothers that utilized ART/NIFT were older, to be of the Caucasian race, to have pre-eclampsia, private insurance, and education >12 years (P < .001). Cases compared to controls were more premature, had lower birth weights, and were more often the product of a multiple gestation pregnancy (P < .001). Cases had a higher 1-year mortality (18.2% vs 9.1%; OR: 2.2; 95% CI: 1.4, 3.6), more severe PVD (86.2% vs 72.3%; OR: 2.4; 95% CI: 1.5, 3.9), and a longer hospital stay (66.7 ± 73.0 vs 32.5 ± 47.2 days; P < .001) than controls. However, when adjusting for GA these differences become statistically insignificant.

CONCLUSION

Children born following ART/NIFT with PVD had increased mortality compared to infants with PVD but without ART/NIFT. The primary driver of this relationship is prematurity.

Morbidity of Persistent Pulmonary Hypertension of the Newborn in the First Year of Life

OBJECTIVE

To assess postdischarge mortality and morbidity in infants diagnosed with different etiologies and severities of persistent pulmonary hypertension of the newborn (PPHN), and to identify risk factors for these adverse clinical outcomes.

STUDY DESIGN

This was a population-based study using an administrative dataset linking birth and death certificates, hospital discharge and readmissions records from 2005 to 2012 in California. Cases were infants ≥34 weeks' gestational age with International Classification of Diseases,9th edition, codes consistent with PPHN. The primary outcome was defined as postdischarge mortality or hospital readmission during the first year of life. Crude and adjusted risk ratio (aRR) with 95% CIs were calculated to quantify the risk for the primary outcome and to identify risk factors.

RESULTS

Infants with PPHN (n = 7847) had an aRR of 3.5 (95% CI, 3.3-3.7) for the primary outcome compared with infants without PPHN (n = 3 974 536), and infants with only mild PPHN (n = 2477) had an aRR of 2.2 (95% CI, 2.0-2.5). Infants with congenital diaphragmatic hernia as the etiology for PPHN had an aRR of 8.2 (95% CI, 6.7-10.2) and infants with meconium aspiration syndrome had an aRR of 4.2 (95% CI, 3.7-4.6) compared with infants without PPHN. Hispanic ethnicity, small for gestational age, severe PPHN, and etiology of PPHN were risk factors for the primary outcome.

CONCLUSIONS

The postdischarge morbidity burden of infants with PPHN is large. These findings extend to infants with mild PPHN and etiologies with pulmonary vascular changes that are thought to be short term and recoverable. These data could inform counseling of parents.

Postnatal gestational age estimation via newborn screening analysis: application and potential

Introduction: Preterm birth is a major global health concern, contributing to 35% of all neonatal deaths in 2016. Given the importance of accurately ascertaining estimates of preterm birth and in light of current limitations in postnatal gestational age (GA) estimation, novel methods of estimating GA postnatally in the absence of prenatal ultrasound are needed. Previous work has demonstrated the potential for metabolomics to estimate GA by analyzing data captured through routine newborn screening. Areas covered: Circulating analytes found in newborn blood samples vary by GA. Leveraging newborn screening and demographic data, our group developed an algorithm capable of estimating GA postnatally to within approximately 1 week of ultrasound-validated GA. Since then, we have built on the model by including additional analytes and validating the model's performance through internal and external validation studies, and through implementation of the model internationally. Expert opinion: Currently, using metabolomics to estimate GA postnatally holds considerable promise but is limited by issues of cost-effectiveness and resource access in low-income settings. Future work will focus on enhancing the precision of this approach while prioritizing point-of-care testing that is both accessible and acceptable to individuals in low-resource settings.

[Serum metabolomics of preterm and full-term infants based on gas chromatography-mass spectrometry]

OBJECTIVE

To study the features of serum metabolites in preterm infants based on gas chromatography-mass spectrometry (GC-MS), and to find differentially expressed metabolites in the serum of preterm infants.

METHODS

Serum samples were collected from 19 preterm infants and 20 full-term infants before feeding. GC-MS was used to measure metabolic profiles, and the metabolic features of 397 serum metabolites in preterm infants were analyzed.

RESULTS

There was a significant difference in serum metabolic features between the preterm and full-term infants before feeding. There were significant differences between the full-term and preterm infants in the levels of metabolites such as O-phosphonothreonine, digicitrin, tannic acid, and fructose-1,6-diphosphate (P<0.01), suggesting that the above differentially expressed metabolites were highly differentiated between the preterm and full-term infants. Most differentially expressed metabolites were involved in the metabolic pathways such as ABC transporters, β-alanine and pyrimidines and were correlated with some clinical parameters (albumin and total bilirubin) (P<0.05).

CONCLUSIONS

There is a significant difference in serum metabolites between preterm and full-term infants before feeding. Metabolomics plays an important role in improving metabolic disorders and exploring metabolism-related diseases in preterm infants.

External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh

This study sought to evaluate the performance of metabolic gestational age estimation models developed in Ontario, Canada in infants born in Bangladesh. Cord and heel prick blood spots were collected in Bangladesh and analyzed at a newborn screening facility in Ottawa, Canada. Algorithm-derived estimates of gestational age and preterm birth were compared to ultrasound-validated estimates. 1036 cord blood and 487 heel prick samples were collected from 1069 unique newborns. The majority of samples (93.2% of heel prick and 89.9% of cord blood) were collected from term infants. When applied to heel prick data, algorithms correctly estimated gestational age to within an average deviation of 1 week overall (root mean square error = 1.07 weeks). Metabolic gestational age estimation provides accurate population-level estimates of gestational age in this data set. Models were effective on data obtained from both heel prick and cord blood, the latter being a more feasible option in low-resource settings.

Newborn Metabolic Profile Associated with Hyperbilirubinemia With and Without Kernicterus

Our objective was to assess the relationship between hyperbilirubinemia with and without kernicterus and metabolic profile at newborn screening. Included were 1,693,658 infants divided into a training or testing subset in a ratio of 3:1. Forty‐two metabolites were analyzed using logistic regression (odds ratios (ORs), area under the receiver operating characteristic curve (AUC), 95% confidence intervals (CIs)). Several metabolite patterns remained consistent across gestational age groups for hyperbilirubinemia without kernicterus. Thyroid stimulating hormone (TSH) and C‐18:2 were decreased, whereas tyrosine and C‐3 were increased in infants across groupings. Increased C‐3 was also observed for kernicterus (OR: 3.17; 95% CI: 1.18–8.53). Thirty‐one metabolites were associated with hyperbilirubinemia without kernicterus in the training set. Phenylalanine (OR: 1.91; 95% CI: 1.85–1.97), ornithine (OR: 0.76; 95% 0.74–0.77), and isoleucine + leucine (OR: 0.63; 95% CI: 0.61–0.65) were the most strongly associated. This study showed that newborn metabolic function is associated with hyperbilirubinemia with and without kernicterus.

Combining newborn metabolic and DNA analysis for second-tier testing of methylmalonic acidemia

Purpose

Improved second-tier tools are needed to reduce false-positive outcomes in newborn screening (NBS) for inborn metabolic disorders on the Recommended Universal Screening Panel (RUSP).

Methods

We designed an assay for multiplex sequencing of 72 metabolic genes (RUSPseq) from newborn dried blood spots. Analytical and clinical performance was evaluated in 60 screen-positive newborns for methylmalonic acidemia (MMA) reported by the California Department of Public Health NBS program. Additionally, we trained a Random Forest machine learning classifier on NBS data to improve prediction of true and false-positive MMA cases.

Results

Of 28 MMA patients sequenced, we found two pathogenic or likely pathogenic (P/LP) variants in a MMA-related gene in 24 patients, and one pathogenic variant and a variant of unknown significance (VUS) in 1 patient. No such variant combinations were detected in MMA false positives and healthy controls. Random Forest–based analysis of the entire NBS metabolic profile correctly identified the MMA patients and reduced MMA false-positive cases by 51%. MMA screen-positive newborns were more likely of Hispanic ethnicity.

Conclusion

Our two-pronged approach reduced false positives by half and provided a reportable molecular finding for 89% of MMA patients. Challenges remain in newborn metabolic screening and DNA variant interpretation in diverse multiethnic populations.

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

OBJECTIVE

To evaluate the association between early metabolic profiles combined with infant characteristics and survival past 7 days of age in infants born at 22-25 weeks of gestation.

STUDY DESIGN

This nested case-control consisted of 465 singleton live births in California from 2005 to 2011 at 22-25 weeks of gestation. All infants had newborn metabolic screening data available. Data included linked birth certificate and mother and infant hospital discharge records. Mortality was derived from linked death certificates and death discharge information. Each death within 7 days was matched to 4 surviving controls by gestational age and birth weight z score category, leaving 93 cases and 372 controls. The association between explanatory variables and 7-day survival was modeled via stepwise logistic regression. Infant characteristics, 42 metabolites, and 12 metabolite ratios were considered for model inclusion. Model performance was assessed via area under the curve.

RESULTS

The final model included 1 characteristic and 11 metabolites. The model demonstrated a strong association between metabolic patterns and infant survival (area under the curve [AUC] 0.885, 95% CI 0.851-0.920). Furthermore, a model with just the selected metabolites performed better (AUC 0.879, 95% CI 0.841-0.916) than a model with multiple clinical characteristics (AUC 0.685, 95% CI 0.627-0.742).

CONCLUSIONS

Use of metabolomics significantly strengthens the association with 7-day survival in infants born extremely premature. Physicians may be able to use metabolic profiles at birth to refine mortality risks and inform postnatal counseling for infants born at <26 weeks of gestation.

Metabolic profiles derived from residual blood spot samples: A longitudinal analysis

Background: Secondary use of newborn screening dried blood spot samples include use for biomedical or epidemiological research. However, the effects of storage conditions on archival samples requires further examination. The objective of this study was to determine the utility of residual newborn samples for deriving reliable metabolic gestational age estimates. Methods: Residual newborn dried blood spot samples that had been stored for 2-, 4-, 6-, or 12-months in temperature controlled (21°C) conditions were re-analyzed for the full panel of newborn screening analytes offered by a provincial newborn screening lab in Ottawa, Canada. Data from re-analyzed samples were compared to corresponding baseline newborn screening values for absolute agreement, and Pearson and intraclass correlation. Performance of a gestational age estimation algorithm originally developed from baseline newborn screening values was then validated on data derived from stored samples. Results: A total of 307 samples were used for this study. 17-hydroxyprogesterone and newborn hemoglobin profiles measured by immunoassay and high-performance liquid chromatography, respectively, were among the most stable markers across all time points of analysis. Acylcarnitines exhibited the greatest degree of variation in stability upon repeat measurement. The largest shifts in newborn analyte profiles and the poorest performance of metabolic gestational age algorithms were observed when samples were analyzed 12-months after sample collection. Conclusions: Duration of sample storage, independent of temperature and humidity, affects newborn screening profiles and gestational age estimates derived from metabolic gestational dating algorithms. When considering use of dried blood spot samples either for clinical or research purposes, care should be taken when interpreting data stemming from secondary use.

Biofluid Metabolomics in Preterm Birth Research

This article presents an account of the research carried out so far in the use of metabolomics to find biomarkers of preterm birth (PTB) in fetal, maternal, and newborn biofluids. Metabolomic studies have employed mainly nuclear magnetic resonance spectroscopy or mass spectrometry-based methodologies to analyze, on one hand, prenatal biofluids (amniotic fluid, maternal urine/maternal blood, cervicovaginal fluid) to identify predictive biomarkers of PTB, and on the other hand, biofluids collected at or after birth (amniotic fluid, umbilical cord blood, newborn urine, and newborn blood, maternal blood, or breast milk) to assess and follow up the health status of PTB babies. Besides advancing on the biochemical knowledge of PTB metabolism mainly during the in utero period and at birth, the work carried out has also helped to identify important requirements related to experimental design and analytical protocol that need to be addressed, if translation of these biomarkers to the clinic is to be envisaged. An outlook of possible future developments for the translation of laboratory results to the clinic is presented.

Altered metabolites in newborns with persistent pulmonary hypertension

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.

Racial and Ethnic Disparities in Preterm Infant Mortality and Severe Morbidity: A Population-Based Study

BACKGROUND

Disparities exist in the rates of preterm birth and infant mortality across different racial/ethnic groups. However, only a few studies have examined the impact of race/ethnicity on the outcomes of premature infants.

OBJECTIVE

To report the rates of mortality and severe neonatal morbidity among multiple gestational age (GA) groups stratified by race/ethnicity.

METHODS

A retrospective cohort study utilizing linked birth certificate, hospital discharge, readmission, and death records up to 1 year of life. Live-born infants ≤36 weeks born in the period 2007-2012 were included. Maternal self-identified race/ethnicity, as recorded on the birth certificate, was used. ICD-9 diagnostic and procedure codes captured neonatal morbidities (intraventricular hemorrhage, retinopathy of prematurity, periventricular leukomalacia, bronchopulmonary dysplasia, and necrotizing enterocolitis). Multiple logistic regression was performed to evaluate the impact of race/ethnicity on mortality and morbidity, adjusting for GA, birth weight, sex, and multiple gestation.

RESULTS

Our cohort totaled 245,242 preterm infants; 26% were white, 46% Hispanic, 8% black, and 12% Asian. At 22-25 weeks, black infants were less likely to die than white infants (odds ratio [OR] 0.76; 95% confidence interval [CI] 0.62-0.94). However, black infants born at 32-34 weeks (OR 1.64; 95% CI 1.15-2.32) or 35-36 weeks (OR 1.57; 95% CI 1.00-2.24) were more likely to die. Hispanic infants born at 35-36 weeks were less likely to die than white infants (OR 0.66; 95% CI 0.50-0.87). Racial disparities at different GAs were also detected for severe morbidities.

CONCLUSIONS

The impact of race/ethnicity on mortality and severe morbidity varied across GA categories in preterm infants. Disparities persisted even after adjusting for important potential confounders.

Using newborn screening analytes to identify cases of neonatal sepsis

Neonatal sepsis is associated with high mortality and morbidity, yet challenges with available diagnostic approaches can lead to delays in therapy. Our study assessed whether newborn screening analytes could be utilized to identify associations with neonatal sepsis. We linked a newborn screening registry with health databases to identify cases of sepsis among infants born in Ontario from 2010-2015. Correlations between sepsis and screening analytes were examined within three gestational age groups (early preterm: <34 weeks; late preterm: 34-36 weeks; term: ≥37 weeks), using multivariable logistic regression models. We started with a model containing only clinical factors, then added groups of screening analytes. Among 793,128 infants, 4,794 were diagnosed with sepsis during the neonatal period. Clinical variables alone or in combination with hemoglobin values were not strongly predictive of neonatal sepsis among infants born at term or late preterm. However, model fit improved considerably after adding markers of thyroid and adrenal function, acyl-carnitines, and amino acids. Among infants born at early preterm gestation, neither clinical variables alone nor models incorporating screening analytes adequately predicted neonatal sepsis. The combination of clinical variables and newborn screening analytes may have utility in identifying term or late preterm infants at risk for neonatal sepsis.

Gestational Age and Outcomes in Critical Congenital Heart Disease

BACKGROUND AND OBJECTIVES

It is unknown how gestational age (GA) impacts neonatal morbidities in infants with critical congenital heart disease (CCHD). We aim to quantify GA-specific mortality and neonatal morbidity in infants with CCHD.

METHODS

Cohort study using a database linking birth certificate, infant hospital discharge, readmission, and death records, including infants 22 to 42 weeks' GA without chromosomal anomalies (2005-2012, 2 988 925 live births). The International Classification of Diseases, Ninth Revision diagnostic and procedure codes were used to define CCHD and neonatal morbidities (intraventricular hemorrhage, retinopathy, periventricular leukomalacia, chronic lung disease, necrotizing enterocolitis). Adjusted absolute risk differences (ARDs) with 95% confidence intervals (CIs) were calculated.

RESULTS

We identified 6903 out of 2 968 566 (0.23%) infants with CCHD. The incidence of CCHD was highest at 29 to 31 weeks' GA (0.9%) and lowest at 39 to 42 weeks (0.2%). Combined neonatal morbidity or mortality in infants with and without CCHD was 82.8% and 57.9% at <29 weeks and declined to 10.9% and 0.1% at 39 to 42 weeks' GA. In infants with CCHD, being born at 34 to 36 weeks was associated with a higher risk of death or morbidity than being born at 37 to 38 weeks (adjusted ARD 9.1%, 95% CI 5.5% to 12.7%), and being born at 37 to 38 weeks was associated with a higher risk of death or morbidity than 39 to 42 weeks (adjusted ARD 3.2%, 95% CI 1.6% to 4.9%).

CONCLUSIONS

Infants born with CCHD are at high risk of neonatal morbidity. Morbidity remains increased across all GA groups in comparison with infants born at 39 to 42 weeks. This substantial risk of neonatal morbidity is important to consider when caring for this patient population.

Performance of a postnatal metabolic gestational age algorithm: a retrospective validation study among ethnic subgroups in Canada

OBJECTIVES

Biological modelling of routinely collected newborn screening data has emerged as a novel method for deriving postnatal gestational age estimates. Validation of published models has previously been limited to cohorts largely consisting of infants of white Caucasian ethnicity. In this study, we sought to determine the validity of a published gestational age estimation algorithm among recent immigrants to Canada, where maternal landed immigrant status was used as a surrogate measure of infant ethnicity.

DESIGN

We conducted a retrospective validation study in infants born in Ontario between April 2009 and September 2011.

SETTING

Provincial data from Ontario, Canada were obtained from the Institute for Clinical Evaluative Sciences.

PARTICIPANTS

The dataset included 230 034 infants born to non-landed immigrants and 70 098 infants born to immigrant mothers. The five most common countries of maternal origin were India (n=10 038), China (n=7468), Pakistan (n=5824), The Philippines (n=5441) and Vietnam (n=1408). Maternal country of origin was obtained from Citizenship and Immigration Canada's Landed Immigrant Database.

PRIMARY AND SECONDARY OUTCOME MEASURES

Performance of a postnatal gestational age algorithm was evaluated across non-immigrant and immigrant populations.

RESULTS

Root mean squared error (RMSE) of 1.05 weeks was observed for infants born to non-immigrant mothers, whereas RMSE ranged from 0.98 to 1.15 weeks among infants born to immigrant mothers. Area under the receiver operating characteristic curve for distinguishing term versus preterm infants (≥37 vs <37 weeks gestational age or >34 vs ≤34 weeks gestational age) was 0.958 and 0.986, respectively, in the non-immigrant subgroup and ranged from 0.927 to 0.964 and 0.966 to 0.99 in the immigrant subgroups.

CONCLUSIONS

Algorithms for postnatal determination of gestational age may be further refined by development and validation of region or ethnicity-specific models. However, our results provide reassurance that an algorithm developed from Ontario-born infant cohorts performs well across a range of ethnicities and maternal countries of origin without modification.

Epidemiology of Live Born Infants with Nonimmune Hydrops Fetalis-Insights from a Population-Based Dataset

OBJECTIVE

To evaluate the incidence, etiology, and 1-year mortality of nonimmune hydrops fetalis (NIHF) and to identify risk factors for mortality in a contemporary population-based dataset.

STUDY DESIGN

The California Office of Statewide Health Planning and Development maintains a database linking maternal and infant hospital discharge, readmissions, and birth and death certificate date from 1 year before to 1 year after birth. We searched the database (2005-2012) for infants with NIHF (identified by the International Classification of Diseases, 9th Revision, Clinical Modification code). Hazard models were used to identify risk factors for mortality in infants with NIHF; results are presented as hazard ratios (HRs, 95% CI).

RESULTS

The incidence of NIHF was 2.5 out of 10 000 among live born infants. Neonatal mortality was 35.1% (364 out of 1037) and overall mortality was 43.2% (448 out of 1037) at 1 year of age. Gestational age (GA) was predictive of mortality with a HR of 2.4 (95% CI 1.9-3.2) for preterm compared with term infants. The GA-adjusted HR for mortality was 1.3 (95% CI 1.1-1.6) for polyhydramnios and 1.5 (95% CI 1.2-2.0) for large for gestational age infants compared with appropriate for GA infants. Aneuploid infants with critical congenital heart disease had an adjusted HR of 2.3 (95% CI 1.5-3.6) compared with euploid infants without a structural birth defect.

CONCLUSIONS

In this large, population-based study, prematurity, polyhydramnios, and large for gestational age were predictors of increased mortality. Mortality is highly variable among euploid and aneuploid infants with and without structural birth defects and critical congenital heart disease.

Postnatal gestational age estimation using newborn screening blood spots: a proposed validation protocol

Background

Knowledge of gestational age (GA) is critical for guiding neonatal care and quantifying regional burdens of preterm birth. In settings where access to ultrasound dating is limited, postnatal estimates are frequently used despite the issues of accuracy associated with postnatal approaches. Newborn metabolic profiles are known to vary by severity of preterm birth. Recent work by our group and others has highlighted the accuracy of postnatal GA estimation algorithms derived from routinely collected newborn screening profiles. This protocol outlines the validation of a GA model originally developed in a North American cohort among international newborn cohorts.

Methods

Our primary objective is to use blood spot samples collected from infants born in Zambia and Bangladesh to evaluate our algorithm’s capacity to correctly classify GA within 1, 2, 3 and 4 weeks. Secondary objectives are to 1) determine the algorithm's accuracy in small-for-gestational-age and large-for-gestational-age infants, 2) determine its ability to correctly discriminate GA of newborns across dichotomous thresholds of preterm birth (≤34 weeks, <37 weeks GA) and 3) compare the relative performance of algorithms derived from newborn screening panels including all available analytes and those restricted to analyte subsets. The study population will consist of infants born to mothers already enrolled in one of two preterm birth cohorts in Lusaka, Zambia, and Matlab, Bangladesh. Dried blood spot samples will be collected and sent for analysis in Ontario, Canada, for model validation.

Discussion

This study will determine the validity of a GA estimation algorithm across ethnically diverse infant populations and assess population specific variations in newborn metabolic profiles.

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

OBJECTIVE

Pregnancy is accompanied by dramatic physiological changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age and determine biological processes that are perturbed in normal pregnancy.

STUDY DESIGN

A longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median, 5) were profiled to measure the abundance of 1125 proteins using multiplex assays. Linear mixed-effects models with polynomial splines were used to model protein abundance as a function of gestational age, and the significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having the following: (1) >1.5-fold change between 8 and 40 weeks of gestation; and (2) a false discovery rate-adjusted value of P < .1. Gene ontology enrichment analysis was used to identify biological processes overrepresented among the proteins that changed with advancing gestation.

RESULTS

The following results were found: (1) Ten percent (112 of 1125) of the profiled proteins changed in abundance as a function of gestational age; (2) of the 1125 proteins analyzed, glypican-3, sialic acid-binding immunoglobulin-type lectin-6, placental growth factor, C-C motif-28, carbonic anhydrase 6, prolactin, interleukin-1 receptor 4, dual-specificity mitogen-activated protein kinase 4, and pregnancy-associated plasma protein-A had more than a 5-fold change in abundance across gestation (these 9 proteins are known to be involved in a wide range of both physiological and pathological processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc); and (3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis, and leukocyte migration (false discovery rate, 10%).

CONCLUSION

The plasma proteome of normal pregnancy demonstrates dramatic changes in both the magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy and the development of biomarkers to differentiate normal vs abnormal pregnancy and determine the response to interventions.