High rates of placental inflammation among samples collected by the Multi-Omics for Mothers and Infants consortium.

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.

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.

Does the implementation of revised American College of Cardiology and American Heart Association (ACC/AHA) guidelines improve the identification of stillbirths and preterm births in hypertensive pregnancies: a population-based cohort study from South Asia and sub-Saharan Africa.

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.

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.

Metabolomics of a neonatal cohort from the Alliance for Maternal and Newborn Health Improvement biorepository: Effect of preanalytical variables on reference intervals.

BACKGROUND: The study was conducted to determine reference interval (RI) and evaluate the effect of preanalytical variables on Dried blood spot (DBS)-amino acids, acylcarnitines and succinylacetone of neonates. METHODOLOGY: DBS samples were collected within 48-72 hours of life. Samples were analyzed for biochemical markers on tandem mass spectrometer at the University of Iowa. Comparison of RI across various categorical variables were performed. RESULTS: A total of 610 reference samples were selected based on exclusion criteria; 53.2% being females. Mean gestational age (GA) of mothers at the time of delivery was 38.7±1.6 weeks; 24.5% neonates were of low birth weight and 14.3% were preterm. Out of the total 610 neonates, 23.1% were small for GA. Reference intervals were generated for eleven amino acids, thirty-two acylcarnitines and succinylacetone concentrations. Markers were evaluated with respect to the influence of gender, GA, weight and time of sampling and statistically significant minimal differences were observed for some biomarkers. CONCLUSION: RI for amino acids, succinylacetone and acylcarnitine on DBS has been established for healthy neonates, which could be of use in the clinical practice. Clinically significant effect of GA, weight, gender and time of sampling on these markers were not identified.

Clinical and epidemiological features of pediatric population hospitalized with COVID-19: a multicenter longitudinal study (March 2020-December 2021) from Pakistan.

Background: We aimed to explore the epidemiological, clinical, and phenotypic parameters of pediatric patients hospitalized with COVID-19 in Pakistan. Methods: This longitudinal cohort study was conducted in five tertiary care hospitals in Pakistan from March 2020 to December 2021. Data on various epidemiological and clinical variables were collected using Case Report Forms (CRFs) adapted from the WHO COVID-19 clinical data platform at baseline and at monthly follow-ups for 3 months. Findings: A total of 1090 children were included. The median age was 5 years (Interquartile range 1-10), and the majority presented due to new signs/symptoms associated with COVID-19 (57.8%; n = 631), the most common being general and respiratory symptoms. Comorbidities were present in 417 (38.3%) children. Acute COVID-19 alone was found in 932 (85.5%) children, 81 (7.4%) had multisystem inflammatory syndrome (MIS-C), 77 (7.0%) had overlapping features of acute COVID-19 and MIS-C, and severe disease was found in 775/1086 (71.4%). Steroids were given to 351 (32.2%) patients while 77 (7.1%) children received intravenous immunoglobulins. Intensive care unit (ICU) care was required in 334 (31.6%) patients, and 203 (18.3%) deaths were reported during the study period. The largest spike in cases and mortality was from July to September 2021 when the Delta variant first emerged. During the first and second follow-ups, 37 and 10 children expired respectively, and medical care after discharge was required in 204 (25.4%), 94 (16.6%), and 70 (13.7%) children respectively during each monthly follow-up. Interpretation: Our study highlights that acute COVID-19 was the major phenotype associated with high severity and mortality in children in Pakistan in contrast to what has been observed globally. Funding: The study was supported by the World Health Organization (WHO), which was involved in the study design but played no role in its analysis, writeup, or publication.

Comparative predictive power of serum vs plasma proteomic signatures in feto-maternal medicine.

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.

Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries.

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.

Prediction of gestational age using urinary metabolites in term and preterm pregnancies.

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.

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.

Determinants of infant and young complementary feeding practices among children 6-23 months of age in urban Pakistan: a multicenter longitudinal study.

BACKGROUND: Suboptimal feeding practices have a negative impact on children's health and growth in the first 2 years of life and increase their risk of undernutrition, morbidity, and mortality. The aim of the study was to assess the factors that influence infant and young child feeding practices among urban mothers in a hospital setting at Karachi, Pakistan. METHODS: A longitudinal multi-center cohort study was conducted in four countries, MULTICENTER BODY COMPOSITION REFERENCE STUDY (MBCRS) to produce normal body composition reference data in healthy infants from 3 months to 24 months of age. Repeated anthropometric (weight, length and head circumference) and body composition measurements using "deuterium dilution method" along with 24-h dietary recall questionnaires were performed on 250 healthy term infants at 3, 6, 9, 12, 18, and 24 months of age. The 24-h dietary recall data from this study was used to assess the breastfeeding and complementary feeding practices in children aged 6-24 months. RESULTS: A total of 250 healthy infants were enrolled in the study. A majority of newborns (75.4%) were exclusively breastfed till 3 months of age; however, by 6 months of age, only 30.2% of infants were exclusively breastfed. Only 44.1% of children aged 6-24 months achieved minimum dietary diversity (MDD), 84.7% achieved minimum meal frequency (MMF), and 44.1% achieved a minimum acceptable diet (MAD). 71.4% achieved MDD and MAD and 100% achieved MMF at 24 months. The bivariate analysis found that breastfed children (OR 3.93, 95% CI 2.72-5.68), with employed mothers (OR 1.55, 95% CI 1.06-2.27) who had graduated from secondary school (OR 1.45, 95% CI 1.08-1.94) were more likely to meet minimum dietary diversity. The multivariable analysis showed that only the child's age was significantly associated with MDD (p value< 0.0001), with the likelihood of meeting MDD increasing as the children aged; 9 months (OR 18.96, 95% CI 6.63-54.19), 12 months (OR 40.25, 95% CI 14.14-114.58), 18 months (OR 90.02, 95% CI 30.84-262.77) and 24 months (OR 82.14, 95% CI 27.23-247.83). CONCLUSION: Our study revealed that Infant and young child feeding practices are significantly associated with maternal education, employment, and the child's age. Therefore, it is essential that investments be made towards protective breastfeeding and complementary feeding policies and legislations, emphasis on female education and ensuring the availability of affordable nutritious and diverse foods.

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.