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

Important Lessons on Long-Term Stability of Amino Acids in Stored Dried Blood Spots

Residual heel prick Dried Blood Spots (DBS) are valuable samples for retrospective investigation of inborn metabolic diseases (IMD) and biomarker analyses. Because many metabolites suffer time-dependent decay, we investigated the five-year stability of amino acids (AA) in residual heel prick DBS. In 2019/2020, we analyzed 23 AAs in 2170 residual heel prick DBS from the Dutch neonatal screening program, stored from 2013-2017 (one year at +4 °C and four years at room temperature), using liquid chromatography mass-spectrometry. Stability was assessed by AA changes over the five years. Hydroxyproline could not be measured accurately and was not further assessed. Concentrations of 19 out of the remaining 22 AAs degraded significantly, ranked from most to least stable: aspartate, isoleucine, proline, valine, leucine, tyrosine, alanine, phenylalanine, threonine, citrulline, glutamate, serine, ornithine, glycine, asparagine, lysine, taurine, tryptophan and glutamine. Arginine, histidine and methionine concentrations were below the limit of detection and were likely to have been degraded within the first year of storage. AAs in residual heel prick DBS stored at room temperature are subject to substantial degradation, which may cause incorrect interpretation of test results for retrospective biomarker studies and IMD diagnostics. Therefore, retrospective analysis of heel prick blood should be done in comparison to similarly stored heel prick blood from controls.

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

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.

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.

Storage stability of five steroids and in dried blood spots for newborn screening and retrospective diagnosis of congenital adrenal hyperplasia

Congenital adrenal hyperplasia (CAH) is a severe inherited disorder of cortisol biosynthesis that is potentially lethal or can seriously affect quality of life. For the first time, we aimed to assess the stability of 21-deoxycortisol (21Deox), 11-deoxycortisol (11Deox), 4-androstenedione (4AD), 17-hydroxyprogesterone (17OHP) and cortisol (Cort), diagnostic for CAH, in dried blood spots (DBSs) during a 1 year storage at different temperatures. Spiked DBS samples were stored at room temperature, 4 °C, -20 °C or -70 °C, respectively and analyzed in triplicates using liquid chromatography–tandem mass spectrometry at Weeks 0, 1, 2, 3 and 4, Month 6 and Year 1. Analyte levels within ±15% vs the baseline were considered stable. Our observations show that 21Deox, 4AD and 17OHP were not significantly changed for 1 year even at room temperature at either analyte levels. In contrast, Cort required storage at 4 °C, -20 °C or -70 °C for long-term stability, being significantly decreased at room temperature from Month 6 (p<0.01) in both the 30(60) nM and the 90(180) nM samples. 11Deox was significantly decreased at room temperature at Year 1 (p<0.01) and only in the 30(60) nM samples. Thus, all biomarkers were stable for up to 1 year at 4 °C, -20 °C or -70 °C and at least for 4 weeks at room temperature. These findings have implications for analyses of stored DBS samples in 2^nd-tier assays in newborn screening and for retrospective CAH studies.