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

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

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

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 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

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\u27s 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\u27s age was significantly associated with MDD (p value\u3c 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\u27s 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

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

Gender wise distribution of DBS biomarkers in cohort of neonates from AMANHI biorepository.

a: Gender wise distribution of DBS amino acid and succinylacetone concentrations in cohort of neonates from AMANHI biorepository in Pakistan (n = 610). b: Gender wise distribution of DBS acylcarnitine in cohort of neonates from AMANHI biorepository in Pakistan (n = 610).</p