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




Background


Accurate gestational dating is a critical component of obstetric and newborn care. In the absence of early ultrasound, many clinicians rely on less accurate measures, such as last menstrual period or symphysis-fundal height during pregnancy, or Dubowitz scoring or the Ballard (or New Ballard) method at birth. These measures often underestimate or overestimate gestational age and can lead to misclassification of babies as born preterm, which has both short- and long-term clinical care and public health implications.


Objective


We sought to evaluate whether metabolic markers in newborns measured as part of routine screening for treatable inborn errors of metabolism can be used to develop a population-level metabolic gestational dating algorithm that is robust despite intrauterine growth restriction and can be used when fetal ultrasound dating is not available. We focused specifically on the ability of these markers to differentiate preterm births (PTBs) (<37 weeks) from term births and to assign a specific gestational age in the PTB group.


Study Design


We evaluated a cohort of 729,503 singleton newborns with a California birth in 2005 through 2011 who had routine newborn metabolic screening and fetal ultrasound dating at 11–20 weeks’ gestation. Using training and testing subsets (divided in a ratio of 3:1) we evaluated the association among PTB, target newborn characteristics, acylcarnitines, amino acids, thyroid-stimulating hormone, 17-hydroxyprogesterone, and galactose-1-phosphate-uridyl-transferase. We used multivariate backward stepwise regression to test for associations and linear discriminate analyses to create a linear function for PTB and to assign a specific week of gestation. We used sensitivity, specificity, and positive predictive value to evaluate the performance of linear functions.


Results


Along with birthweight and infant age at test, we included 35 of the 51 metabolic markers measured in the final multivariate model comparing PTBs and term births. Using a linear discriminate analyses-derived linear function, we were able to sort PTBs and term births accurately with sensitivities and specificities of ≥95% in both the training and testing subsets. Assignment of a specific week of gestation in those identified as PTBs resulted in the correct assignment of week ±2 weeks in 89.8% of all newborns in the training and 91.7% of those in the testing subset. When PTB rates were modeled using the metabolic dating algorithm compared to fetal ultrasound, PTB rates were 7.15% vs 6.11% in the training subset and 7.31% vs 6.25% in the testing subset.


Conclusion


When considered in combination with birthweight and hours of age at test, metabolic profile evaluated within 8 days of birth appears to be a useful measure of PTB and, among those born preterm, of specific week of gestation ±2 weeks. Dating by metabolic profile may be useful in instances where there is no fetal ultrasound due to lack of availability or late entry into care.


Introduction


Accurate gestational dating is a critical component of obstetric and newborn care. During pregnancy, gestational age informs the scheduling and management of clinical visits and laboratory testing, determination of the appropriateness of fetal growth, management and timing of delivery, evaluation of the pregnancy as being at risk for preterm and/or postterm delivery, and the application of interventions including, for example, the use of antenatal corticosteroids and magnesium for neuroprotection. In the newborn period, gestational dating informs critical decisions around resuscitation (particularly in newborns born around the limits of viability) and is essential for tracking growth and neurodevelopmental function.


Accurate gestational dating is also important for establishing population-level rates of preterm birth (PTB). Measuring and tracking this rate across time is essential for establishing baselines and for resource planning aimed at reducing these outcomes and their associated burden within and across populations. While first-trimester ultrasound is recognized as the best method to establish gestational age for most pregnancies, ultrasound dating becomes less reliable as gestation progresses. Prenatal ultrasound dating is also available only in sites with the resources to purchase the equipment and hire trained technicians. As such, prenatal ultrasound is not available in some rural and low- and middle-income country settings.


In the absence of early ultrasound, many clinicians rely on last menstrual period (LMP) for gestational dating. LMP can be unreliable given that a number of factors can influence dating including poor recall, irregular cycle length, and bleeding in early pregnancy. Symphysis-fundal height (SFH) >20 weeks is also used for pregnancy dating, but is often inaccurate due to factors such as multiple pregnancy, maternal size, polyhydramnios and oligohydramnios, and fetal growth restriction.


When no other measures of gestational dating are available (eg, fetal ultrasound, LMP, SFH), other measures used for gestational dating at birth include the Dubowitz method (which incorporates 34 physical and neurological assessments), the Ballard method (which uses 6 physical and neurological measures taken within 30 and 42 hours of age), and the New Ballard score (which uses an expanded list of features over the original Ballard method). While all of these measures provide useful information when no other data are available, like with use of LMP and SFH, these methods have been shown to be less accurate when there is intrauterine growth restriction.


The recognized need for a reliable alternative gestational dating method in instances where there is no first- or second-trimester ultrasound has led to an increased focus on identifying new dating methods and proxies that can be used during pregnancy or at the time of birth. Recent years have seen a punctuated effort in this area with the initiation of a number of high-risk, high-reward projects funded through the Bill and Melinda Gates Foundation and a recent call by the National Institutes of Health for more accurate tools to assess gestational age.


In response to this call for newer dating methods, in this study we hypothesized that metabolic markers measured as part of routine newborn screening for treatable inborn errors of metabolism could be used to build a population-level metabolic dating algorithm that is robust despite intrauterine growth restriction. Specifically, we hypothesized that when considered in combination with newborn characteristics, metabolic markers would be able to differentiate PTBs (<37 weeks) from term births and to assign a specific gestational age within a margin of 2 weeks. If a metabolic dating tool were developed, it could be used broadly in high-income countries with existing newborn screening programs where no fetal ultrasound scan was done and could also be used by researchers and policy makers in other settings where newborn specimens are available for testing either retrospectively or prospectively to establish population-level baseline rates of PTB. Such a tool could gain further acceptance in countries without newborn screening through the use of miniature or hand-held mass spectrometers or via the translation of findings into a clinical assay that does not require the use of a mass spectrometer.


While our hypothesis that markers used for newborn screening could also be used for gestational dating purposes is novel, the work is well supported by previous studies demonstrating that many of these routinely collected markers (eg, acylcarnitines, amino acids, thyroid-stimulating hormone [TSH]) are related to PTB and to week of gestation and have heritable components that are robust in small-for-gestational-age (SGA) infants. In the present study, we developed and evaluated a metabolic dating algorithm using a large and diverse sample of California newborns who had ultrasound dating between 11–20 weeks of gestation. We focused specifically on the capacity of markers to differentiate PTBs from term births and to assign a specific gestational age in the preterm group.




Materials and Methods


We evaluated whether biomarkers collected as part of routine newborn screening for treatable inborn errors of metabolism could be used to create a metabolic dating algorithm in a cohort of 729,503 singleton newborns born in California in 2005 through 2011. All of these newborns had routine metabolic screening performed through the California Newborn Screening program using a heel-stick blood draw between 12 hours and 8 days after birth. All babies had a mother who had ultrasound dating from 11–20 weeks of gestation, had a linked birth certificate and hospital discharge record, and were born at 22–44 completed weeks of gestation (sample selection included in Figure ). For study purposes, we randomly divided the final cohort into a training subset with 547,127 babies (75% of total) and a testing subset with 182,376 babies (25% of total). This division allowed for an unbiased estimate of model performance given that the testing set was not part of the sample in which the initial model was built.




Figure


Sample selection

a Results present for 51 target markers and ratios measured between 12 hours and 8 days after birth as part of routine newborn screening.

GA , gestational age; PNS , prenatal screening.

Jelliffe-Pawlowski et al. Dating by metabolic profile: California. Am J Obstet Gynecol 2016.


Date of birth, birthweight, gender, and race/ethnicity were derived from the birth cohort files, which are linked birth certificate and hospital discharge files obtained from the California Office of Statewide Health Planning and Development. Age at testing in hours after birth, blood-spot analyte measurements, and information about whether the infant had been on total parenteral nutrition (TPN) between birth and the time of testing was obtained from newborn screening records. We were able to identify newborns with fetal ultrasound dating from 11–20 weeks of gestation by examining linked newborn and prenatal screening records. We computed days of gestation at birth by comparing the estimated date of delivery based on ultrasound findings in the prenatal screening records to the birth date on the linked birth certificate and hospital discharge records. In California, the prenatal and newborn screening programs are nested within the same division of the California Department of Public Health (the Genetic Disease Screening Program). This nesting allows for routine linkage of prenatal and newborn screening records. The prenatal and newborn screening data used in this study were obtained from the California Biobank Program. Details regarding the California Newborn Screening Program and the California Prenatal Screening Program have been described in detail elsewhere.


All markers evaluated in the study were tested by the California Department of Public Health’s Genetic Disease Laboratory as part of routine screening for treatable inborn errors of metabolism using dried blood specimens collected by heel-stick at birth hospitals from 12 hours to 8 days after birth. Free carnitine, acylcarnitines (C-2, C-3, C-3DC, C-4, C-5, C-5:1, C-5DC, C-6, C-8, C-8:1, C-10, C-10:1, C-12, C-12:1, C-14, C-14:1, C-16, C-16:1, C-18, C-18:1, C-18:2, C-18:1OH), and amino acids (alanine, arginine, citrulline, glycine, methionine, ornithine, phenylalanine, proline, 5-oxoproline, tyrosine, valine) were measured by standardized tandem mass spectrometry. TSH and 17-hydroxyprogesterone (17-OHP) were measured using high-performance liquid chromatography. Galactose-1-phosphate-uridyl-transferase was measured using a fluorometric enzyme assay. The study also utilized a number of ratios commonly used in screening (C-14:1/C-12:1, C-1/C-2, C-8/C-10, free carnitine/(C-16 + C-18:1), arginine/ornithine, citrulline/arginine, leucine/alanine, leucine/isoleucine, ornithine/citrulline, and phenylalanine/tyrosine). We transformed biomarker values and ratios using natural logarithms to normalize distribution across all markers.


Analyses first focused on evaluating the association among maternal characteristics, markers, and PTB in the training subset using simple bivariate logistic regression and related odds ratios and 95% confidence intervals (CI). We used multivariate backward stepwise regression for final model building with entry at P < .40 based on bivariate analyses and removal at P < .05. TPN was included in multivariate models regardless of observed P values given the demonstrated relationship between TPN and some markers tested as part of routine screening (eg, some acylcarnitines and amino acids) and our desire to identify markers with a robust association with gestational age despite TPN status. Characteristics and markers remaining in the final TPN-adjusted multivariate model were used to create a linear discriminate analysis (LDA)-derived linear function that we used to sort PTBs and term births. Markers were also leveraged to create an LDA-derived linear function for specific week of gestation in those identified as preterm. We evaluated the performance of the linear functions in both the training and testing subsets using sensitivity, specificity, and positive predictive values (PPVs) and their 95% CIs. We evaluated performance in all births and in subsets grouped as SGA (<10th percentile weight for gestational age), appropriate for gestational age (10th–90th percentile weight for gestational age), and large for gestational age (>90th percentile weight for gestational age) based on published US norms. Week-specific LDA-derived linear functions were evaluated by their capacity to assign week of gestation in those identified as preterm compared to ultrasound-dated week. We further examined the modeled rates of PTB based on the metabolic dating algorithm compared to ultrasound-dated week.


We performed all analyses using software (SAS, Version 9.3; SAS Institute Inc, Cary, NC). Methods and protocols for the study were approved by the Committee for the Protection of Human Subjects within the Health and Human Services Agency of the State of California (protocol no. 12-09-0702).




Materials and Methods


We evaluated whether biomarkers collected as part of routine newborn screening for treatable inborn errors of metabolism could be used to create a metabolic dating algorithm in a cohort of 729,503 singleton newborns born in California in 2005 through 2011. All of these newborns had routine metabolic screening performed through the California Newborn Screening program using a heel-stick blood draw between 12 hours and 8 days after birth. All babies had a mother who had ultrasound dating from 11–20 weeks of gestation, had a linked birth certificate and hospital discharge record, and were born at 22–44 completed weeks of gestation (sample selection included in Figure ). For study purposes, we randomly divided the final cohort into a training subset with 547,127 babies (75% of total) and a testing subset with 182,376 babies (25% of total). This division allowed for an unbiased estimate of model performance given that the testing set was not part of the sample in which the initial model was built.




Figure


Sample selection

a Results present for 51 target markers and ratios measured between 12 hours and 8 days after birth as part of routine newborn screening.

GA , gestational age; PNS , prenatal screening.

Jelliffe-Pawlowski et al. Dating by metabolic profile: California. Am J Obstet Gynecol 2016.


Date of birth, birthweight, gender, and race/ethnicity were derived from the birth cohort files, which are linked birth certificate and hospital discharge files obtained from the California Office of Statewide Health Planning and Development. Age at testing in hours after birth, blood-spot analyte measurements, and information about whether the infant had been on total parenteral nutrition (TPN) between birth and the time of testing was obtained from newborn screening records. We were able to identify newborns with fetal ultrasound dating from 11–20 weeks of gestation by examining linked newborn and prenatal screening records. We computed days of gestation at birth by comparing the estimated date of delivery based on ultrasound findings in the prenatal screening records to the birth date on the linked birth certificate and hospital discharge records. In California, the prenatal and newborn screening programs are nested within the same division of the California Department of Public Health (the Genetic Disease Screening Program). This nesting allows for routine linkage of prenatal and newborn screening records. The prenatal and newborn screening data used in this study were obtained from the California Biobank Program. Details regarding the California Newborn Screening Program and the California Prenatal Screening Program have been described in detail elsewhere.


All markers evaluated in the study were tested by the California Department of Public Health’s Genetic Disease Laboratory as part of routine screening for treatable inborn errors of metabolism using dried blood specimens collected by heel-stick at birth hospitals from 12 hours to 8 days after birth. Free carnitine, acylcarnitines (C-2, C-3, C-3DC, C-4, C-5, C-5:1, C-5DC, C-6, C-8, C-8:1, C-10, C-10:1, C-12, C-12:1, C-14, C-14:1, C-16, C-16:1, C-18, C-18:1, C-18:2, C-18:1OH), and amino acids (alanine, arginine, citrulline, glycine, methionine, ornithine, phenylalanine, proline, 5-oxoproline, tyrosine, valine) were measured by standardized tandem mass spectrometry. TSH and 17-hydroxyprogesterone (17-OHP) were measured using high-performance liquid chromatography. Galactose-1-phosphate-uridyl-transferase was measured using a fluorometric enzyme assay. The study also utilized a number of ratios commonly used in screening (C-14:1/C-12:1, C-1/C-2, C-8/C-10, free carnitine/(C-16 + C-18:1), arginine/ornithine, citrulline/arginine, leucine/alanine, leucine/isoleucine, ornithine/citrulline, and phenylalanine/tyrosine). We transformed biomarker values and ratios using natural logarithms to normalize distribution across all markers.


Analyses first focused on evaluating the association among maternal characteristics, markers, and PTB in the training subset using simple bivariate logistic regression and related odds ratios and 95% confidence intervals (CI). We used multivariate backward stepwise regression for final model building with entry at P < .40 based on bivariate analyses and removal at P < .05. TPN was included in multivariate models regardless of observed P values given the demonstrated relationship between TPN and some markers tested as part of routine screening (eg, some acylcarnitines and amino acids) and our desire to identify markers with a robust association with gestational age despite TPN status. Characteristics and markers remaining in the final TPN-adjusted multivariate model were used to create a linear discriminate analysis (LDA)-derived linear function that we used to sort PTBs and term births. Markers were also leveraged to create an LDA-derived linear function for specific week of gestation in those identified as preterm. We evaluated the performance of the linear functions in both the training and testing subsets using sensitivity, specificity, and positive predictive values (PPVs) and their 95% CIs. We evaluated performance in all births and in subsets grouped as SGA (<10th percentile weight for gestational age), appropriate for gestational age (10th–90th percentile weight for gestational age), and large for gestational age (>90th percentile weight for gestational age) based on published US norms. Week-specific LDA-derived linear functions were evaluated by their capacity to assign week of gestation in those identified as preterm compared to ultrasound-dated week. We further examined the modeled rates of PTB based on the metabolic dating algorithm compared to ultrasound-dated week.


We performed all analyses using software (SAS, Version 9.3; SAS Institute Inc, Cary, NC). Methods and protocols for the study were approved by the Committee for the Protection of Human Subjects within the Health and Human Services Agency of the State of California (protocol no. 12-09-0702).




Results


The study cohort included 729,503 singleton infants (20.2% of the total population of singletons in these birth years [n = 3,617,727]). This number represented those with ultrasound results and linked birth and hospital discharge records who had gestational age from 22–44 weeks ( Figure ). Race/ethnicity and gender was similar to that of all births during this time period wherein most newborns were Hispanic (47.1%) or non-Hispanic white (32.0%) with more male than female births (51.1% vs 48.6%). Nine in 10 newborns had newborn screening conducted within 3 days ( Table 1 ).



Table 1

Newborn characteristics








































































































n = a %
Sample 729,503 100.0
Race/ethnicity
White, not Hispanic 233,729 32.0
Hispanic 343,388 47.1
Asian 90,219 12.4
Black 25,814 3.5
Other 36,307 5.0
Gender
Male 372,689 51.1
Female 354,189 48.6
Completed gestation, wk b
<32 3809 0.5
32–36 41,014 5.6
≥37 684,680 93.9
Weight for gestational age c
<10th (SGA) 51,469 7.1
10–90th (AGA) 617,147 84.6
>90th (LGA) 60,887 8.4
H/d at testing
12–24 h 262,323 36.0
2–3 d 397,301 54.5
4–5 d 51,076 7.0
7–8 d 18,803 2.6
Total parenteral nutrition 10,264 1.4

AGA , appropriate-for-gestational age; LGA , large-for-gestational age; SGA , small-for-gestational age.

Jelliffe-Pawlowski et al. Dating by metabolic profile: California. Am J Obstet Gynecol 2016 .

a Value missing where sum for category does not equal total sample size


b Based on 11–20 wk fetal ultrasound


c Using Alexander et al.



Along with a number of infant characteristics, 49 of the 51 metabolites and metabolite ratios measured were associated with PTB in crude logistic regression analyses (all except C-5DC and C-18) ( Supplementary Table 1 ). Male gender, race/ethnicity, and 14 metabolic markers were removed from final multivariate logistic models using stepwise methods given P values >.05. Hour of age at collection, birthweight, and 35 metabolic markers were included in the final multivariate logistic regression model ( Table 2 ). When a LDA-derived linear function was built based on these training set multivariate logistic results, we found that these characteristics and markers were able to identify PTBs with >95% accuracy in the training and testing subsets (sensitivity 99.5% [95% CI, 99.5–99.6%], specificity 98.8% [95% CI, 98.8–98.9%] in the training subset; sensitivity 99.5% [95% CI, 99.4–99.6%], specificity 98.9% [95% CI, 98.8–98.9%] in the testing subset). Findings were robust across SGA, appropriate-for-gestational-age, and large-for-gestational-age babies (sensitivity and specificity across all groups ≥94.9%). PPVs tended to be >85% across most groupings with the exception of the SGA group where PPVs were >66% (66.0% [95% CI, 64.6–67.2%] in the training set, 66.7% [95% CI, 64.4–68.9%] in the testing subset) ( Table 3 ). Assignment of a specific week of gestation in those identified as preterm resulted in the correct assignment ±2 weeks in 89.8% of all newborns in the training set and 91.7% of newborns in the testing subset ( Table 4 ).



Table 2

Markers and characteristics in final <37 weeks’ model a








































































































































































Adj OR 95% CI
Acylcarnitines
C-3 3.526 3.344–3.719
C-3DC 0.750 0.706–0.798
C-4 1.054 1.017–1.093
C-5 1.918 1.845–1.995
C-5:1 1.032 1.014–1.051
C-5DC 1.972 1.868–2.082
C-6 0.930 0.911–0.950
C-8:1 1.083 1.053–1.115
C-10 0.860 0.829–0.893
C-12 0.835 0.802–0.870
C-12:1 0.589 0.524–0.662
C-14 1.394 1.312–1.481
C-14:1 1.427 1.245–1.635
C16:1 1.160 1.097–1.226
C-18 1.383 1.279–1.496
C-18:1 1.692 1.527–1.876
C-18:2 1.192 1.143–1.243
Free carnitine 0.146 0.127–0.168
C-14:1/C-12:1 0.805 0.722–0.897
Free carnitine/(C-16 + C-18:1) 0.146 0.127–0.168
Amino acids
Alanine 0.135 0.126–0.144
Glycine 1.399 1.299–1.508
Methionine 1.291 1.205–1.384
Ornithine 0.263 0.243–0.284
Proline 0.889 0.832–0.949
Tyrosine 7.940 7.128–8.845
Valine 0.566 0.525–0.611
5-Oxoproline 1.144 1.108–1.181
Leucine/isoleucine 3.020 2.729–3.342
Citrulline/arginine 0.849 0.827–0.873
Phenylalanine/tyrosine 2.235 2.022–2.470
Ornithine/citrulline 2.137 2.002–2.470
Other
Thyroid-stimulating hormone 0.810 0.788–0.832
17-OHP 2.963 2.879–3.048
GALT 0.552 0.513–0.593
Birthweight b 0.997 0.997–0.997
Hour at test b 1.025 1.024–1.027

Adj OR , adjusted odds ratio; CI , confidence interval; FC , free carnitine; GALT , galactose-1-phosphate-uridyl-transferase; 17-OHP , 17-hydroxyprogesterone.

Jelliffe-Pawlowski et al. Dating by metabolic profile: California. Am J Obstet Gynecol 2016 .

a All variables natural log-transformed, associations P < .01 after adjustment for all other markers in model and for total parenteral nutrition


b Included as continuous variable.



Table 3

Performance of linear discriminate for <37 completed weeks’ gestation











































































Training Testing
Sensitivity (95% CI) Specificity (95% CI) PPV (95% CI) Sensitivity (95% CI) Specificity (95% CI) PPV (95% CI)
All 99.5% 98.9% 85.1% 99.5% 98.8% 85.2%
(99.5–99.6) (98.8–98.9) (84.7–85.4) (99.4–99.6) (98.8–98.9) (84.6–85.8)
SGA a 99.9 95.1 66.0 100.0 95.0 66.7
(99.8–100.0) (94.8–95.3) (64.6–67.2) (99.6–100.0) (94.6–95.4) (64.4–68.9)
AGA a 99.6 99.1 87.9 99.6 99.1 87.9
(99.5–99.7) (99.1–99.1) (87.5–88.2) (99.5–99.7) (99.0–99.1) (87.2–88.5)
LGA a 95.4 99.7 89.3 94.9 99.7 89.4
(94.1–96.6) (99.7–99.8) (87.4–90.9) (92.0–96.9) (99.6–99.8) (85.8–92.2)

AGA , appropriate (birthweight 10–90th percentile) for gestational age; CI , confidence interval; LGA , large (birthweight >90th percentile) for gestational age; PPV , positive predictive value; SGA , small (birthweight <10th percentile) for gestational age.

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May 4, 2017 | Posted by in GYNECOLOGY | Comments Off on Gestational dating by metabolic profile at birth: a California cohort study
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