Young Infant Mortality Associated with Preterm and Small-for-Gestational-Age Births in Rural Bangladesh: A Prospective Cohort Study.

OBJECTIVE: To assess the relative risk of mortality in infants born preterm and small for gestational age (SGA) during the first and second months of life in rural Bangladesh. STUDY DESIGN: We analyzed data from a cohort of pregnant women and their babies in Sylhet, Bangladesh, assembled between 2011 and 2014. Community health workers visited enrolled babies up to 10 times from birth to age 59 days. Survival status was recorded at each visit. Gestational age was estimated from mother's reported last menstrual period. Birth weights were measured within 72 hours of delivery. SGA was defined using the INTERGROWTH-21st standard. We estimated unadjusted and adjusted hazard ratios (HRs) and corresponding 95% CIs for babies born preterm and SGA separately for the first and second month of life using bivariate and multivariable weighted Cox regression models. RESULTS: The analysis included 17 643 singleton live birth babies. Compared with infants born at term-appropriate for gestational age, in both unadjusted and adjusted analyses, infants born preterm-SGA had the greatest risk of death in the first (HR 13.25, 95% CI 8.65-20.31; adjusted HR 12.05, 95% CI 7.82-18.57) and second month of life (HR 4.65, 95% CI 1.93-11.23; adjusted HR 4.1, 95% CI 1.66-10.15), followed by infants born preterm-appropriate for gestational age and term-SGA. CONCLUSIONS: The risk of mortality in infants born preterm and/or SGA is increased and extends through the second month of life. Appropriate interventions to prevent and manage complications caused by prematurity and SGA could improve survival during and beyond the neonatal period.

Early to mid-pregnancy HbA1c levels and its association with adverse pregnancy outcomes in three low middle-income countries in Asia and Sub-Saharan Africa.

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.

Association of maternal prenatal copper concentration with gestational duration and preterm birth: a multicountry meta-analysis.

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.

Vitamin D deficiency in pregnancy and the risk of preterm birth: a nested case-control study.

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.

Developing a video expert panel as a reference standard to evaluate respiratory rate counting in paediatric pneumonia diagnosis: protocol for a cross-sectional study.

INTRODUCTION: Manual counting of respiratory rate (RR) in children is challenging for health workers and can result in misdiagnosis of pneumonia. Some novel RR counting devices automate the counting of RR and classification of fast breathing. The absence of an appropriate reference standard to evaluate the performance of these devices is a challenge. If good quality videos could be captured, with RR interpretation from these videos systematically conducted by an expert panel, it could act as a reference standard. This study is designed to develop a video expert panel (VEP) as a reference standard to evaluate RR counting for identifying pneumonia in children. METHODS AND ANALYSIS: Using a cross-sectional design, we will enrol children aged 0-59 months presenting with suspected pneumonia at different levels of health facilities in Dhaka and Sylhet, Bangladesh. We will videorecord a physician/health worker counting RR manually and also using an automated RR counter (Children's Automated Respiration Monitor) from each child. We will establish a standard operating procedure for capturing quality videos, make a set of reference videos, and train and standardise the VEP members using the reference videos. After that, we will assess the performance of the VEP as a reference standard to evaluate RR counting. We will calculate the mean difference and proportions of agreement within±2 breaths per minute and create Bland-Altman plots with limits of agreement between VEP members. ETHICS AND DISSEMINATION: The study protocol was approved by the National Research Ethics Committee of Bangladesh Medical Research Council, Bangladesh (registration number: 39315022021) and Edinburgh Medical School Research Ethics Committee (EMREC), Edinburgh, UK (REC Reference: 21-EMREC-040). Dissemination of the study findings will be through conference presentations and publications in peer-reviewed scientific journals.

Sero-prevalence and risk factors for Severe Acute Respiratory Syndrome Coronavirus 2 infection in women and children in a rural district of Bangladesh: A cohort study.

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.

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.

Digital auscultation as a novel childhood pneumonia diagnostic tool for community clinics in Sylhet, Bangladesh: protocol for a cross-sectional study.

INTRODUCTION: The WHO's Integrated Management of Childhood Illnesses (IMCI) algorithm for diagnosis of child pneumonia relies on counting respiratory rate and observing respiratory distress to diagnose childhood pneumonia. IMCI case defination for pneumonia performs with high sensitivity but low specificity, leading to overdiagnosis of child pneumonia and unnecessary antibiotic use. Including lung auscultation in IMCI could improve specificity of pneumonia diagnosis. Our objectives are: (1) assess lung sound recording quality by primary healthcare workers (HCWs) from under-5 children with the Feelix Smart Stethoscope and (2) determine the reliability and performance of recorded lung sound interpretations by an automated algorithm compared with reference paediatrician interpretations. METHODS AND ANALYSIS: In a cross-sectional design, community HCWs will record lung sounds of ~1000 under-5-year-old children with suspected pneumonia at first-level facilities in Zakiganj subdistrict, Sylhet, Bangladesh. Enrolled children will be evaluated for pneumonia, including oxygen saturation, and have their lung sounds recorded by the Feelix Smart stethoscope at four sequential chest locations: two back and two front positions. A novel sound-filtering algorithm will be applied to recordings to address ambient noise and optimise recording quality. Recorded sounds will be assessed against a predefined quality threshold. A trained paediatric listening panel will classify recordings into one of the following categories: normal, crackles, wheeze, crackles and wheeze or uninterpretable. All sound files will be classified into the same categories by the automated algorithm and compared with panel classifications. Sensitivity, specificity and predictive values, of the automated algorithm will be assessed considering the panel's final interpretation as gold standard. ETHICS AND DISSEMINATION: The study protocol was approved by the National Research Ethics Committee of Bangladesh Medical Research Council, Bangladesh (registration number: 09630012018) and Academic and Clinical Central Office for Research and Development Medical Research Ethics Committee, Edinburgh, UK (REC Reference: 18-HV-051). Dissemination will be through conference presentations, peer-reviewed journals and stakeholder engagement meetings in Bangladesh. TRIAL REGISTRATION NUMBER: NCT03959956.

Burden and risk factors for antenatal depression and its effect on preterm birth in South Asia: A population-based cohort study.

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.

Maternal Diet, Infection, and Risk of Cord Blood Inflammation in the Bangladesh Projahnmo Pregnancy Cohort.

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.

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.

Association of maternal prenatal selenium concentration and preterm birth: a multicountry meta-analysis.

BACKGROUND: Selenium (Se), an essential trace mineral, has been implicated in preterm birth (PTB). We aimed to determine the association of maternal Se concentrations during pregnancy with PTB risk and gestational duration in a large number of samples collected from diverse populations. METHODS: Gestational duration data and maternal plasma or serum samples of 9946 singleton live births were obtained from 17 geographically diverse study cohorts. Maternal Se concentrations were determined by inductively coupled plasma mass spectrometry analysis. The associations between maternal Se with PTB and gestational duration were analysed using logistic and linear regressions. The results were then combined using fixed-effect and random-effect meta-analysis. FINDINGS: In all study samples, the Se concentrations followed a normal distribution with a mean of 93.8 ng/mL (SD: 28.5 ng/mL) but varied substantially across different sites. The fixed-effect meta-analysis across the 17 cohorts showed that Se was significantly associated with PTB and gestational duration with effect size estimates of an OR=0.95 (95% CI: 0.9 to 1.00) for PTB and 0.66 days (95% CI: 0.38 to 0.94) longer gestation per 15 ng/mL increase in Se concentration. However, there was a substantial heterogeneity among study cohorts and the random-effect meta-analysis did not achieve statistical significance. The largest effect sizes were observed in UK (Liverpool) cohort, and most significant associations were observed in samples from Malawi. INTERPRETATION: While our study observed statistically significant associations between maternal Se concentration and PTB at some sites, this did not generalise across the entire cohort. Whether population-specific factors explain the heterogeneity of our findings warrants further investigation. Further evidence is needed to understand the biologic pathways, clinical efficacy and safety, before changes to antenatal nutritional recommendations for Se supplementation are considered.

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.

Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries.

Importance: Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies. Objective: To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. Design, Setting, and Participants: This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019. Exposures: Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites. Main Outcomes and Measures: The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. Results: Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways. Conclusions and Relevance: This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.