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BACKGROUND: The Multi-Omics for Mothers and Infants consortium aims to improve birth outcomes. Preterm birth is a major obstetrical complication globally and causes significant infant and childhood morbidity and mortality. OBJECTIVE: We analyzed placental samples (basal plate, placenta or chorionic villi, and the chorionic plate) collected by the 5 Multi-Omics for Mothers and Infants sites, namely The Alliance for Maternal and Newborn Health Improvement Bangladesh, The Alliance for Maternal and Newborn Health Improvement Pakistan, The Alliance for Maternal and Newborn Health Improvement Tanzania, The Global Alliance to Prevent Prematurity and Stillbirth Bangladesh, and The Global Alliance to Prevent Prematurity and Stillbirth Zambia. The goal was to analyze the morphology and gene expression of samples collected from preterm and uncomplicated term births. STUDY DESIGN: The teams provided biopsies from 166 singleton preterm (<37 weeks' gestation) and 175 term (≥37 weeks' gestation) deliveries. The samples were fixed in formalin and paraffin embedded. Tissue sections from these samples were stained with hematoxylin and eosin and subjected to morphologic analyses. Other placental biopsies (n=35 preterm, 21 term) were flash frozen, which enabled RNA purification for bulk transcriptomics. RESULTS: The morphologic analyses revealed a surprisingly high rate of inflammation that involved the basal plate, placenta or chorionic villi, and the chorionic plate. The rate of inflammation in chorionic villus samples, likely attributable to chronic villitis, ranged from 25% (Pakistan site) to 60% (Zambia site) of cases. Leukocyte infiltration in this location vs in the basal plate or chorionic plate correlated with preterm birth. Our transcriptomic analyses identified 267 genes that were differentially expressed between placentas from preterm vs those from term births (123 upregulated, 144 downregulated). Mapping the differentially expressed genes onto single-cell RNA sequencing data from human placentas suggested that all the component cell types, either singly or in subsets, contributed to the observed dysregulation. Consistent with the histopathologic findings, gene ontology analyses highlighted the presence of leukocyte infiltration or activation and inflammatory responses in both the fetal and maternal compartments. CONCLUSION: The relationship between placental inflammation and preterm birth is appreciated in developed countries. In this study, we showed that this link also exists in developing geographies. In addition, among the participating sites, we found geographic- and population-based differences in placental inflammation and preterm birth, suggesting the importance of local factors.
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BACKGROUND: There are limited data on the impact of perinatal inflammation on child neurodevelopment in low-middle income countries and among growth-restricted infants. METHODS: Population-based, prospective birth cohort study of 288 infants from July 2016-March 2017 in Sylhet, Bangladesh. Umbilical cord blood was analyzed for interleukin(IL)-1α, IL-1ß, IL-6, IL-8, and C-reactive protein(CRP). Child neurodevelopment was assessed at 24 months with Bayley-III Scales of Infant Development. We determined associations between cord blood inflammation and neurodevelopmental outcomes, controlling for potential confounders. RESULTS: 248/288 (86%) live born infants were followed until 24 months, among whom 8.9% were preterm and 45.0% small-for-gestational-age(SGA) at birth. Among all infants, elevated concentrations (>75%) of CRP and IL-6 at birth were associated with increased odds of fine motor delay at 24 months; elevated CRP was also associated with lower receptive communication z-scores. Among SGA infants, elevated IL-1α was associated with cognitive delay, IL-8 with language delay, CRP with lower receptive communication z-scores, and IL-1ß with lower expressive communication and motor z-scores. CONCLUSIONS: In rural Bangladesh, perinatal inflammation was associated with impaired neurodevelopment at 24 months. The associations were strongest among SGA infants and noted across several biomarkers and domains, supporting the neurobiological role of inflammation in adverse fetal development, particularly in the setting of fetal growth restriction. IMPACT: Cord blood inflammation was associated with fine motor and language delays at 24 months of age in a community-based cohort in rural Bangladesh. 23.4 million infants are born small-for-gestational-age (SGA) globally each year. Among SGA infants, the associations between cord blood inflammation and adverse outcomes were strong and consistent across several biomarkers and neurodevelopmental domains (cognitive, motor, language), supporting the neurobiological impact of inflammation prominent in growth-restricted infants. Prenatal interventions to prevent intrauterine growth restriction are needed in low- and middle-income countries and may also result in long-term benefits on child development.
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BACKGROUND: Small-for-gestational-age (SGA), commonly caused by poor placentation, is a major contributor to global perinatal mortality and morbidity. Maternal serum levels of placental protein and angiogenic factors are changed in SGA. Using data from a population-based pregnancy cohort, we estimated the relationships between levels of second-trimester pregnancy-associated plasma protein-A (PAPP-A), placental growth factor (PlGF), and serum soluble fms-like tyrosine kinase-1 (sFlt-1) with SGA. METHODS: Three thousand pregnant women were enrolled. Trained health workers prospectively collected data at home visits. Maternal blood samples were collected, serum aliquots were prepared and stored at -80â. Included in the analysis were 1,718 women who delivered a singleton live birth baby and provided a blood sample at 24-28 weeks of gestation. We used Mann-Whitney U test to examine differences of the median biomarker concentrations between SGA (< 10th centile birthweight for gestational age) and appropriate-for-gestational-age (AGA). We created biomarker concentration quartiles and estimated the risk ratios (RRs) and 95% confidence intervals (CIs) for SGA by quartiles separately for each biomarker. A modified Poisson regression was used to determine the association of the placental biomarkers with SGA, adjusting for potential confounders. RESULTS: The median PlGF level was lower in SGA pregnancies (934 pg/mL, IQR 613-1411 pg/mL) than in the AGA (1050 pg/mL, IQR 679-1642 pg/mL; p < 0.001). The median sFlt-1/PlGF ratio was higher in SGA pregnancies (2.00, IQR 1.18-3.24) compared to AGA pregnancies (1.77, IQR 1.06-2.90; p = 0.006). In multivariate regression analysis, women in the lowest quartile of PAPP-A showed 25% higher risk of SGA (95% CI 1.09-1.44; p = 0.002). For PlGF, SGA risk was higher in women in the lowest (aRR 1.40, 95% CI 1.21-1.62; p < 0.001) and 2nd quartiles (aRR 1.30, 95% CI 1.12-1.51; p = 0.001). Women in the highest and 3rd quartiles of sFlt-1 were at reduced risk of SGA delivery (aRR 0.80, 95% CI 0.70-0.92; p = 0.002, and aRR 0.86, 95% CI 0.75-0.98; p = 0.028, respectively). Women in the highest quartile of sFlt-1/PlGF ratio showed 18% higher risk of SGA delivery (95% CI 1.02-1.36; p = 0.025). CONCLUSIONS: This study provides evidence that PAPP-A, PlGF, and sFlt-1/PlGF ratio measurements may be useful second-trimester biomarkers for SGA.
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Biomarcadores , Recién Nacido Pequeño para la Edad Gestacional , Factor de Crecimiento Placentario , Insuficiencia Placentaria , Segundo Trimestre del Embarazo , Proteína Plasmática A Asociada al Embarazo , Receptor 1 de Factores de Crecimiento Endotelial Vascular , Humanos , Femenino , Embarazo , Factor de Crecimiento Placentario/sangre , Biomarcadores/sangre , Estudios Prospectivos , Adulto , Receptor 1 de Factores de Crecimiento Endotelial Vascular/sangre , Proteína Plasmática A Asociada al Embarazo/análisis , Proteína Plasmática A Asociada al Embarazo/metabolismo , Insuficiencia Placentaria/sangre , Recién Nacido , Segundo Trimestre del Embarazo/sangre , Bangladesh/epidemiología , Adulto Joven , Edad Gestacional , Factores de RiesgoRESUMEN
BACKGROUND: Hyperglycemia during pregnancy leads to adverse maternal and fetal outcomes. Thus, strict monitoring of blood glucose levels is warranted. This study aims to determine the association of early to mid-pregnancy HbA1c levels with the development of pregnancy complications in women from three countries in South Asia and Sub-Saharan Africa. METHODS: We performed a secondary analysis of the AMANHI (Alliance for Maternal and Newborn Health Improvement) cohort, which enrolled 10,001 pregnant women between May 2014 and June 2018 across Sylhet-Bangladesh, Karachi-Pakistan, and Pemba Island-Tanzania. HbA1c assays were performed at enrollment (8 to < 20 gestational weeks), and epidemiological data were collected during 2-3 monthly household visits. The women were followed-up till the postpartum period to determine the pregnancy outcomes. Multivariable logistic regression models assessed the association between elevated HbA1c levels and adverse events while controlling for potential confounders. RESULTS: A total of 9,510 pregnant women were included in the analysis. The mean HbA1c level at enrollment was found to be the highest in Bangladesh (5.31 ± 0.37), followed by Tanzania (5.22 ± 0.49) and then Pakistan (5.07 ± 0.58). We report 339 stillbirths and 9,039 live births. Among the live births were 892 preterm births, 892 deliveries via cesarean section, and 532 LGA babies. In the multivariate pooled analysis, maternal HbA1c levels of ≥ 6.5 were associated with increased risks of stillbirths (aRR = 6.3, 95% CI = 3.4,11.6); preterm births (aRR = 3.5, 95% CI = 1.8-6.7); and Large for Gestational Age (aRR = 5.5, 95% CI = 2.9-10.6). CONCLUSION: Maternal HbA1c level is an independent risk factor for predicting adverse pregnancy outcomes such as stillbirth, preterm birth, and LGA among women in South Asia and Sub-Saharan Africa. These groups may benefit from early interventional strategies.
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Resultado del Embarazo , Nacimiento Prematuro , Embarazo , Femenino , Recién Nacido , Humanos , Resultado del Embarazo/epidemiología , Mortinato/epidemiología , Nacimiento Prematuro/epidemiología , Hemoglobina Glucada , Cesárea , Países en Desarrollo , Bangladesh , Pakistán , TanzaníaRESUMEN
BACKGROUND: Hypertensive disorders of pregnancy (HDP) are a significant cause of maternal mortality worldwide. The classification and treatment of hypertension in pregnancy remain debated. We aim to compare the effectiveness of the revised 2017 ACC/AHA blood pressure threshold in predicting adverse pregnancy outcomes. METHODS: We conducted a secondary data analysis of the Alliance for Maternal and Newborn Health Improvement (AMANHI) biorepository study, including 10,001 pregnant women from Bangladesh, Pakistan, and Tanzania. Blood pressure was measured using validated devices at different antenatal care visits. The blood pressure readings were categorized as: normal blood pressure (systolic blood pressure (sBP) < 120 mm Hg and diastolic blood pressure (dBP) < 80 mm Hg), elevated blood pressure (sBP 120-129 and dBP < 80), stage 1 hypertension (sBP 130-139 or dBP 80-89, or both), and stage 2 hypertension (sBP ≥ 140 or dBP ≥ 90, or both). We estimated risk ratios for stillbirths and preterm births, as well as diagnostic test properties of both the pre-existing JNC7 (≥ 140/90) and revised ACC/AHA (≥ 130/80) thresholds using normal blood pressure as reference group. RESULTS: From May 2014 to June 2018, blood pressure readings were available for 9,448 women (2,894 in Bangladesh, 2,303 in Pakistan, and 4,251 in Tanzania). We observed normal blood pressure in 70%, elevated blood pressure in 12.4%, stage 1 hypertension in 15.2%, and stage 2 hypertension in 2.5% of the pregnant women respectively. Out of these, 310 stillbirths and 9,109 live births were recorded, with 887 preterm births. Using the ACC/AHA criteria, the stage 1 hypertension cut-off revealed 15.3% additional hypertension diagnoses as compared to JNC7 criteria. ACC/AHA defined hypertension was significantly associated with stillbirths (RR 1.8, 95% CI 1.4, 2.3). The JNC 7 hypertension cut-off of ≥ 140/90 was significantly associated with a higher risk of preterm births (RR 1.6, 95% CI 1.2, 2.2) and stillbirths (RR 3.6, 95% CI 2.5, 5.3). Both criteria demonstrated low sensitivities (8.4 for JNC-7 and 28.1 for ACC/AHA) and positive predictive values (11.0 for JNC7 and 5.2 for ACC/AHA) in predicting adverse outcomes. CONCLUSION: The ACC/AHA criteria (≥ 130/80) identified additional cases of hypertension but had limited predictive accuracy for stillbirths and preterm births, highlighting the ongoing need for improved criteria in managing pregnancy-related hypertension.
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Hipertensión Inducida en el Embarazo , Guías de Práctica Clínica como Asunto , Nacimiento Prematuro , Mortinato , Humanos , Femenino , Embarazo , Nacimiento Prematuro/epidemiología , Mortinato/epidemiología , Adulto , Hipertensión Inducida en el Embarazo/diagnóstico , Hipertensión Inducida en el Embarazo/epidemiología , Estados Unidos/epidemiología , Pakistán/epidemiología , Estudios de Cohortes , American Heart Association , Bangladesh/epidemiología , Tanzanía/epidemiología , Adulto Joven , Presión Sanguínea , Recién Nacido , Sur de AsiaRESUMEN
BACKGROUND: Each year, an estimated 15 million babies are born preterm. Micronutrient deficiencies, including vitamin D deficiency (VDD), are common in many low- and middle-income countries (LMICs), and these conditions are often associated with adverse pregnancy outcomes. Bangladesh experiences a high prevalence of VDD. The country also has a high preterm birth (PTB) rate. Using data from a population-based pregnancy cohort, we estimated the burden of VDD during pregnancy and its association with PTB. METHODS: Pregnant women (N = 3,000) were enrolled after ultrasound confirmation of gestational age at 8-19 weeks of gestation. Trained health workers prospectively collected phenotypic and epidemiological data at scheduled home visits. Trained phlebotomists collected maternal blood samples at enrollment and 24 -28 weeks of gestation. Aliquots of serum were stored at -800 C. We conducted a nested case-control study with all PTB (n = 262) and a random sample of term births (n = 668). The outcome, PTB, was defined as live births < 37 weeks of gestation, based on ultrasound. The main exposure was vitamin D concentrations of 24-28 weeks maternal blood samples. The analysis was adjusted for other PTB risk factors. Women were categorized as VDD (lowest quartile of 25(OH)D; < = 30.25 nmol/L) or not deficient (upper-three quartiles of 25(OH)D; > 30.25 nmol/L). We used logistic regression to determine the association of VDD with PTB, adjusting for potential confounders. RESULTS: The median and interquartile range of serum 25(OH)D was 38.0 nmol/L; 30.18 to 48.52 (nmol/L). After adjusting for co-variates, VDD was significantly associated with PTB [adjusted odds ratio (aOR) = 1.53, 95% confidence interval (CI) = 1.10 - 2.12]. The risk of PTB was also higher among women who were shorter (aOR = 1.81, 95% CI: 1.27-2.57), primiparous (aOR = 1.55, 95% CI = 1.12 - 2.12), passive smokers (aOR = 1.60, 95% CI = 1.09 - 2.34), and those who received iron supplementation during pregnancy (aOR = 1.66, 95% CI: 1.17, 2.37). CONCLUSION: VDD is common in Bangladeshi pregnant women and is associated with an increased risk of PTB.
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Nacimiento Prematuro , Deficiencia de Vitamina D , Femenino , Embarazo , Recién Nacido , Humanos , Lactante , Nacimiento Prematuro/epidemiología , Nacimiento Prematuro/etiología , Estudios de Casos y Controles , Deficiencia de Vitamina D/complicaciones , Deficiencia de Vitamina D/epidemiología , Resultado del Embarazo/epidemiología , Vitamina DRESUMEN
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.
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Algoritmos , Edad Gestacional , Aprendizaje Automático , Tamizaje Neonatal/métodos , Nacimiento Prematuro/epidemiología , África del Sur del Sahara/epidemiología , Asia/epidemiología , Estudios de Cohortes , Países en Desarrollo , Femenino , Humanos , Recién Nacido , Masculino , Metabolómica , Embarazo , Estudios Prospectivos , Curva ROC , Ultrasonografía PrenatalRESUMEN
BACKGROUND: In low-income settings, community health workers (CHWs) are frequently the first point of contact for newborns. Mobile technology may aid health workers in classifying illness and providing referral and management guidance for newborn care. This study evaluates the potential for mobile health technology to improve diagnosis and case management of newborns in Bangladesh. METHODS: A mobile application based on Bangladesh's Comprehensive Newborn Care Package national guidelines (mCNCP) was developed to aid CHWs in identifying and managing small and sick infants. After a 2-day training, CHWs assessed newborns at Sylhet Osmani Medical College Hospital and in the Projahnmo research site (Sylhet, Bangladesh) using either mCNCP or a comparable paper form (pCNCP), similar to standard IMCI-formatted paper forms. CHWs were randomized to conduct a block of ~ 6 newborn assessments starting with either mCNCP or pCNCP, then switched to the alternate method. Physicians using mCNCP served as gold standard assessors. CHW performance with mCNCP and pCNCP were compared using chi-squared tests of independence for equality of proportions, and logistic regressions clustered by CHW. RESULTS: Two hundred seven total CHW assessments were completed on 101 enrolled infants. mCNCP assessments were more often fully completed and completed faster than pCNCP assessments (100% vs 23.8%, p < 0.001; 17.5 vs 23.6 min; p < 0.001). mCNCP facilitated calculations of respiratory rate, temperature, and gestational age. CHWs using mCNCP were more likely to identify small newborns (Odds Ratio (OR): 20.8, Confidence Interval (CI): (7.1, 60.8), p < 0.001), and to correctly classify 7 out of 16 newborn conditions evaluated, including severe weight loss (OR: 13.1, CI: (4.6, 37.5), p < 0.001), poor movement (OR: 6.6, CI: (2.3, 19.3), p = 0.001), hypothermia (OR: 14.9, CI: (2.7, 82.2), p = 0.002), and feeding intolerance (OR: 2.1, CI: (1.3, 3.3), p = 0.003). CHWs with mCNCP were more likely to provide counseling as needed on 4 out of 7 case management recommendations evaluated, including kangaroo mother care. CONCLUSIONS: CHWs in rural Bangladesh with limited experience using tablets successfully used a mobile application for neonatal assessment after a two-day training. mCNCP may aid frontline health workers in Bangladesh to improve completion of neonatal assessment, classification of illnesses, and adherence to neonatal management guidelines.
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Manejo de Caso , Enfermedades del Recién Nacido/diagnóstico , Enfermedades del Recién Nacido/terapia , Aplicaciones Móviles , Bangladesh , Agentes Comunitarios de Salud , Femenino , Humanos , Recién Nacido , Método Madre-Canguro , Masculino , Tamizaje Neonatal , Servicios de Salud RuralRESUMEN
Background: Preterm birth (PTB) and its complications are important public health problems. Its aetiology is multifactorial and involves both modifiable and non-modifiable factors. Among the modifiable risk factors, micronutrient deficiencies, including maternal folate deficiency, are increasingly being studied in PTB. In this study, we estimated the prevalence of folate deficiency during pregnancy and examined its association with PTB among rural Bangladeshi women. Methods: We conducted a nested case-control study using data from a population-based cohort of 3000 pregnant women who were enrolled between 8 and 19 weeks of gestation following ultrasound confirmation of gestational age. Sociodemographic, epidemiologic, clinical, and pregnancy outcomes data were collected through home visits, while blood samples were collected at enrolment and 24-28 weeks of gestation during pregnancy. We included all women who delivered preterm (defined as live births <37 weeks of gestation) as cases (n = 235) and a random sample of women having a term birth as controls (n = 658). The main exposure was folate concentrations in maternal serum during 24-28 weeks of pregnancy. We categorised women into folate deficient (<3 ng/mL) and not deficient (≥3 ng/mL). We then performed multivariable logistic regression analysis to examine the association between maternal folate levels and PTB, adjusting for relevant covariates. Results: Thirty-eight per cent of the enrolled pregnant women were folate deficient. Maternal serum folate deficiency was significantly associated with PTB (adjusted OR (aOR) = 1.73; 95% confidence interval (CI) = 1.27-2.36). The risk of PTB was also higher among women who were of short stature (aOR = 1.83; 95% CI = 1.27-2.63), primiparous (aOR = 1.60; 95% CI = 1.15-2.22), and had exposure to passive smoking (aOR = 1.54; 95% CI = 1.02-2.31). Conclusions: The prevalence of folate deficiency was high among pregnant women in rural Bangladesh, and folate deficiency was significantly associated with an increased risk of preterm birth.
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Deficiencia de Ácido Fólico , Nacimiento Prematuro , Humanos , Femenino , Embarazo , Estudios de Casos y Controles , Nacimiento Prematuro/epidemiología , Adulto , Deficiencia de Ácido Fólico/epidemiología , Bangladesh/epidemiología , Factores de Riesgo , Adulto Joven , Complicaciones del Embarazo/epidemiología , Prevalencia , Ácido Fólico/sangre , Población Rural/estadística & datos numéricosRESUMEN
BACKGROUND: Copper (Cu), an essential trace mineral regulating multiple actions of inflammation and oxidative stress, has been implicated in risk for preterm birth (PTB). OBJECTIVES: This study aimed to determine the association of maternal Cu concentration during pregnancy with PTB risk and gestational duration in a large multicohort study including diverse populations. METHODS: Maternal plasma or serum samples of 10,449 singleton live births were obtained from 18 geographically diverse study cohorts. Maternal Cu concentrations were determined using inductively coupled plasma mass spectrometry. The associations of maternal Cu with PTB and gestational duration were analyzed using logistic and linear regressions for each cohort. The estimates were then combined using meta-analysis. Associations between maternal Cu and acute-phase reactants (APRs) and infection status were analyzed in 1239 samples from the Malawi cohort. RESULTS: The maternal prenatal Cu concentration in our study samples followed normal distribution with mean of 1.92 µg/mL and standard deviation of 0.43 µg/mL, and Cu concentrations increased with gestational age up to 20 wk. The random-effect meta-analysis across 18 cohorts revealed that 1 µg/mL increase in maternal Cu concentration was associated with higher risk of PTB with odds ratio of 1.30 (95% confidence interval [CI]: 1.08, 1.57) and shorter gestational duration of 1.64 d (95% CI: 0.56, 2.73). In the Malawi cohort, higher maternal Cu concentration, concentrations of multiple APRs, and infections (malaria and HIV) were correlated and associated with greater risk of PTB and shorter gestational duration. CONCLUSIONS: Our study supports robust negative association between maternal Cu and gestational duration and positive association with risk for PTB. Cu concentration was strongly correlated with APRs and infection status suggesting its potential role in inflammation, a pathway implicated in the mechanisms of PTB. Therefore, maternal Cu could be used as potential marker of integrated inflammatory pathways during pregnancy and risk for PTB.
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Nacimiento Prematuro , Embarazo , Femenino , Humanos , Recién Nacido , Cobre , Edad Gestacional , Nacimiento Vivo , Inflamación , Factores de RiesgoRESUMEN
BACKGROUND: Blood proteins are frequently measured in serum or plasma, because they provide a wealth of information. Differences in the ex vivo processing of serum and plasma raise concerns that proteomic health and disease signatures derived from serum or plasma differ in content and quality. However, little is known about their respective power to predict feto-maternal health outcomes. Predictive power is a sentinel characteristic to determine the clinical use of biosignatures. OBJECTIVE: This study aimed to compare the power of serum and plasma proteomic signatures to predict a physiological pregnancy outcome. STUDY DESIGN: Paired serum and plasma samples from 73 women were obtained from biorepositories of a multinational prospective cohort study on pregnancy outcomes. Gestational age at the time of sampling was the predicted outcome, because the proteomic signatures have been validated for such a prediction. Multivariate and cross-validated models were independently derived for serum and plasma proteins. RESULTS: A total of 1116 proteins were measured in 88 paired samples from 73 women with a highly multiplexed platform using proximity extension technology (Olink Proteomics Inc, Watertown, MA). The plasma proteomic signature showed a higher predictive power (R=0.64; confidence interval, 0.42-0.79; P=3.5×10-6) than the serum signature (R=0.45; confidence interval, 0.18-0.66; P=2.2×10-3). The serum signature was validated in plasma with a similar predictive power (R=0.58; confidence interval, 0.34-0.75; P=4.8×10-5), whereas the plasma signature was validated in serum with reduced predictive power (R=0.53; confidence interval, 0.27-0.72; P=2.6×10-4). Signature proteins largely overlapped in the serum and plasma, but the strength of association with gestational age was weaker for serum proteins. CONCLUSION: Findings suggest that serum proteomics are less informative than plasma proteomics. They are compatible with the view that the partial ex-vivo degradation and modification of serum proteins during sample processing are an underlying reason. The rationale for collecting and analyzing serum and plasma samples should be carefully considered when deriving proteomic biosignatures to ascertain that specimens of the highest scientific and clinical yield are processed. Findings suggest that plasma is the preferred matrix.
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Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.
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Nacimiento Prematuro , Recién Nacido , Embarazo , Niño , Humanos , Femenino , Nacimiento Prematuro/epidemiología , Países en Desarrollo , Multiómica , Proteómica , Quimiocinas CCRESUMEN
Background: Bangladesh reported its first COVID-19 case on March 8, 2020. Despite lockdowns and promoting behavioural interventions, as of December 31, 2021, Bangladesh reported 1.5 million confirmed cases and 27 904 COVID-19-related deaths. To understand the course of the pandemic and identify risk factors for SARs-Cov-2 infection, we conducted a cohort study from November 2020 to December 2021 in rural Bangladesh. Methods: After obtaining informed consent and collecting baseline data on COVID-19 knowledge, comorbidities, socioeconomic status, and lifestyle, we collected data on COVID-like illness and care-seeking weekly for 54 weeks for women (n = 2683) and their children (n = 2433). Between March and July 2021, we tested all participants for SARS-CoV-2 antibodies using ROCHE's Elecsys® test kit. We calculated seropositivity rates and 95% confidence intervals (95% CI) separately for women and children. In addition, we calculated unadjusted and adjusted relative risk (RR) and 95% CI of seropositivity for different age and risk groups using log-binomial regression models. Results: Overall, about one-third of women (35.8%, 95% CI = 33.7-37.9) and one-fifth of children (21.3%, 95% CI = 19.2-23.6) were seropositive for SARS-CoV-2 antibodies. The seroprevalence rate doubled for women and tripled for children between March 2021 and July 2021. Compared to women and children with the highest household wealth (HHW) tertile, both women and children from poorer households had a lower risk of infection (RR, 95% CI for lowest HHW tertile women (0.83 (0.71-0.97)) and children (0.75 (0.57-0.98)). Most infections were asymptomatic or mild. In addition, the risk of infection among women was higher if she reported chewing tobacco (RR = 1.19,95% CI = 1.03-1.38) and if her husband had an occupation requiring him to work indoors (RR = 1.16, 95% CI = 1.02-1.32). The risk of infection was higher among children if paternal education was >5 years (RR = 1.37, 95% CI = 1.10-1.71) than in children with a paternal education of ≤5 years. Conclusions: We provided prospectively collected population-based data, which could contribute to designing feasible strategies against COVID-19 tailored to high-risk groups. The most feasible strategy may be promoting preventive care practices; however, collecting data on reported practices is inadequate. More in-depth understanding of the factors related to adoption and adherence to the practices is essential.
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COVID-19 , Anticuerpos Antivirales , Bangladesh/epidemiología , COVID-19/epidemiología , Niño , Estudios de Cohortes , Control de Enfermedades Transmisibles , Femenino , Humanos , Masculino , Prevalencia , Factores de Riesgo , SARS-CoV-2 , Estudios SeroepidemiológicosRESUMEN
INTRODUCTION: Women experience high rates of depression, particularly during pregnancy and the postpartum periods. Using population-based data from Bangladesh and Pakistan, we estimated the burden of antenatal depression, its risk factors, and its effect on preterm birth. METHODS: The study uses the following data: maternal depression measured between 24 and 28 weeks of gestation using the 9-question Patient Health Questionnaire (PHQ-9); data on pregnancy including an ultrasound before 19 weeks of gestation; data on pregnancy outcomes; and data on woman's age, education, parity, weight, height, history of previous illness, prior miscarriage, stillbirth, husband's education, and household socioeconomic data collected during early pregnancy. Using PHQ-9 cutoff score of ≥12, women were categorized into none to mild depression or moderate to moderately severe depression. Using ultrasound data, preterm birth was defined as babies born <37 weeks of gestation. To identify risk ratios (RR) for antenatal depression, unadjusted and adjusted RR and 95% confidence intervals (CI) were calculated using log- binomial model. Log-binomial models were also used for determining the effect of antenatal depression on preterm birth adjusting for potential confounders. Data were analyzed using Stata version 16 (StataCorp LP). RESULTS: About 6% of the women reported moderate to moderately severe depressive symptoms during the antenatal period. A parity of ≥2 and the highest household wealth status were associated with an increased risk of depression. The overall incidence of preterm birth was 13.4%. Maternal antenatal depression was significantly associated with the risk of preterm birth (ARR, 95% CI: 1.34, 1.02-1.74). CONCLUSION: The increased risk of preterm birth in women with antenatal depression in conjunction with other significant risk factors suggests that depression likely occurs within a constellation of other risk factors. Thus, to effectively address the burden of preterm birth, programs require developing and providing integrated care addressing multiple risk factors.
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Depresión/epidemiología , Resultado del Embarazo/epidemiología , Nacimiento Prematuro/epidemiología , Adulto , Asia/epidemiología , Bangladesh/epidemiología , Estudios de Cohortes , Depresión/complicaciones , Femenino , Humanos , Recién Nacido , Pakistán/epidemiología , Embarazo , Complicaciones del Embarazo/epidemiología , Complicaciones del Embarazo/psicología , Resultado del Embarazo/psicología , Atención Prenatal/estadística & datos numéricos , Factores de Riesgo , Adulto JovenRESUMEN
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.
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Hielo Seco , Aprendizaje Automático , Femenino , Edad Gestacional , Humanos , Lactante , Recién Nacido , Pakistán , Embarazo , Tanzanía , Tecnología , TemperaturaRESUMEN
OBJECTIVES: To address the disproportionate burden of preterm birth (PTB) in low- and middle-income countries, this study aimed to (1) verify the performance of the United States-validated spontaneous PTB (sPTB) predictor, comprised of the IBP4/SHBG protein ratio, in subjects from Bangladesh, Pakistan and Tanzania enrolled in the Alliance for Maternal and Newborn Health Improvement (AMANHI) biorepository study, and (2) discover biomarkers that improve performance of IBP4/SHBG in the AMANHI cohort. STUDY DESIGN: The performance of the IBP4/SHBG biomarker was first evaluated in a nested case control validation study, then utilized in a follow-on discovery study performed on the same samples. Levels of serum proteins were measured by targeted mass spectrometry. Differences between the AMANHI and U.S. cohorts were adjusted using body mass index (BMI) and gestational age (GA) at blood draw as covariates. Prediction of sPTB < 37 weeks and < 34 weeks was assessed by area under the receiver operator curve (AUC). In the discovery phase, an artificial intelligence method selected additional protein biomarkers complementary to IBP4/SHBG in the AMANHI cohort. RESULTS: The IBP4/SHBG biomarker significantly predicted sPTB < 37 weeks (n = 88 vs. 171 terms ≥ 37 weeks) after adjusting for BMI and GA at blood draw (AUC= 0.64, 95% CI: 0.57-0.71, p < .001). Performance was similar for sPTB < 34 weeks (n = 17 vs. 184 ≥ 34 weeks): AUC = 0.66, 95% CI: 0.51-0.82, p = .012. The discovery phase of the study showed that the addition of endoglin, prolactin, and tetranectin to the above model resulted in the prediction of sPTB < 37 with an AUC= 0.72 (95% CI: 0.66-0.79, p-value < .001) and prediction of sPTB < 34 with an AUC of 0.78 (95% CI: 0.67-0.90, p < .001). CONCLUSION: A protein biomarker pair developed in the U.S. may have broader application in diverse non-U.S. populations.
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Nacimiento Prematuro , Recién Nacido , Femenino , Humanos , Nacimiento Prematuro/diagnóstico , Estudios de Casos y Controles , Inteligencia Artificial , Estudios Prospectivos , Biomarcadores , África del Sur del SaharaRESUMEN
Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC-MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value.
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Metabolómica , Ultrasonografía Prenatal , Cromatografía Liquida , Estudios de Cohortes , Femenino , Edad Gestacional , Humanos , Recién Nacido , EmbarazoRESUMEN
To discover lipidomic alterations during pregnancy in mothers who subsequently delivered small for gestational age (SGA) neonates and identify predictive lipid markers that can help recognize and manage these mothers, we carried out untargeted lipidomics on maternal serum samples collected between 24-28 weeks of gestation. We used a nested case-control study design and serum from mothers who delivered SGA and appropriate for gestational age babies. We applied untargeted lipidomics using mass spectrometry to characterize lipids and discover changes associated with SGA births during pregnancy. Multivariate pattern recognition software Collaborative Laboratory Integrated Reports (CLIR) was used for the post-analytical recognition of range differences in lipid ratios that could differentiate between SGA and control mothers and their integration for complete separation between the two groups. Here, we report changes in lipids from serum collected during pregnancy in mothers who delivered SGA neonates. In contrast to normal pregnancies where lysophosphatidic acid increased over the course of the pregnancy owing to increased activity of lysophospholipase D, we observed a decrease (32%; P = 0.05) of 20:4-lysophosphatidic acid in SGA mothers, which could potentially compromise fetal growth and development. Integration of lipid ratios in an interpretive tool (CLIR) could completely separate SGA mothers from controls demonstrating the power of untargeted lipidomic analyses for identifying novel predictive biomarkers. Additional studies are required for further assessment of the lipid biomarkers identified in this report.
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Recién Nacido Pequeño para la Edad Gestacional , Lipidómica , Estudios de Casos y Controles , Femenino , Edad Gestacional , Humanos , Lactante , Recién Nacido , Lisofosfolípidos , EmbarazoRESUMEN
Inflammation may adversely affect early human brain development. We aimed to assess the role of maternal nutrition and infections on cord blood inflammation. In a pregnancy cohort in Sylhet, Bangladesh, we enrolled 251 consecutive pregnancies resulting in a term livebirth from July 2016-March 2017. Stillbirths, preterm births, and cases of neonatal encephalopathy were excluded. We prospectively collected data on maternal diet (food frequency questionnaire) and morbidity, and analyzed umbilical cord blood for interleukin (IL)-1α, IL-1ß, IL-6, IL-8 and C-reactive protein. We determined associations between nutrition and infection exposures and cord cytokine elevation (≥75% vs. <75%) using logistic regression, adjusting for confounders. One-third of mothers were underweight (BMI < 18.5 kg/m2) at enrollment. Antenatal and intrapartum infections were observed among 4.8% and 15.9% of the sample, respectively. Low pregnancy intakes of B vitamins (B1, B2, B3, B6, B9 (folate)), fat-soluble vitamins (D, E), iron, zinc, and linoleic acid (lowest vs. middle tertile) were associated with higher risk of inflammation, particularly IL-8. There was a non-significant trend of increased risk of IL-8 and IL-6 elevation with history of ante-and intrapartum infections, respectively. In Bangladesh, improving micronutrient intake and preventing pregnancy infections are targets to reduce fetal systemic inflammation and associated adverse neurodevelopmental outcomes.
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Dieta/efectos adversos , Sangre Fetal/química , Inflamación/embriología , Fenómenos Fisiologicos Nutricionales Maternos , Complicaciones Infecciosas del Embarazo/sangre , Adulto , Bangladesh , Proteína C-Reactiva/análisis , Dieta/estadística & datos numéricos , Encuestas sobre Dietas , Femenino , Desarrollo Fetal , Humanos , Recién Nacido , Inflamación/etiología , Interleucinas/sangre , Modelos Logísticos , Estado Nutricional , Embarazo , Complicaciones Infecciosas del Embarazo/etiología , Estudios ProspectivosRESUMEN
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.