Clinical and nutritional correlates of bacterial diarrhoea aetiology in young children: a secondary cross-sectional analysis of the ABCD trial.

OBJECTIVE: The objective was to assess the association between nutritional and clinical characteristics and quantitative PCR (qPCR)-diagnosis of bacterial diarrhoea in a multicentre cohort of children under 2 years of age with moderate to severe diarrhoea (MSD). DESIGN: A secondary cross-sectional analysis of baseline data collected from the AntiBiotics for Children with Diarrhoea trial (NCT03130114). PATIENTS: Children with MSD (defined as >3 loose stools within 24 hours and presenting with at least one of the following: some/severe dehydration, moderate acute malnutrition (MAM) or severe stunting) enrolled in the ABCD trial and collected stool sample. STUDY PERIOD: June 2017-July 2019. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Likely bacterial aetiology of diarrhoea. Secondary outcomes included specific diarrhoea aetiology. RESULTS: A total of 6692 children with MSD had qPCR results available and 28% had likely bacterial diarrhoea aetiology. Compared with children with severe stunting, children with MAM (adjusted OR (aOR) (95% CI) 1.56 (1.18 to 2.08)), some/severe dehydration (aOR (95% CI) 1.66 (1.25 to 2.22)) or both (aOR (95% CI) 2.21 (1.61 to 3.06)), had higher odds of having likely bacterial diarrhoea aetiology. Similar trends were noted for stable toxin-enterotoxigenic Escherichia coli aetiology. Clinical correlates including fever and prolonged duration of diarrhoea were not associated with likely bacterial aetiology; children with more than six stools in the previous 24 hours had higher odds of likely bacterial diarrhoea (aOR (95% CI) 1.20 (1.05 to 1.36)) compared with those with fewer stools. CONCLUSION: The presence of MAM, dehydration or high stool frequency may be helpful in identifying children with MSD who might benefit from antibiotics.

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.

Risk factors for moderate acute malnutrition among children with acute diarrhoea in India and Tanzania: a secondary analysis of data from a randomized trial.

BACKGROUND: Moderate acute malnutrition (MAM) affects over 30 million children aged < 5 years worldwide. MAM may confer a greater risk of developing severe malnutrition and even mortality in children. Assessing risk factors for MAM may allow for earlier recognition of children at risk of deleterious health outcomes. OBJECTIVE: To determine risk factors associated with the prevalence and development of MAM among children aged 6 to 59 months with acute diarrhoea who received treatment with oral rehydration solution and zinc supplementation. METHODS: We conducted a secondary analysis of data from a randomized, dose-finding trial of zinc among children with acute diarrhoea in India and Tanzania. We used regression models to assess risk factors for prevalent MAM at the start of diarrhoea treatment and to identify risk factors associated with the development of MAM at 60 days. MAM was defined as weight for length (or height) Z score ≤-2 and > -3 or mid-upper arm circumference < 12.5 and ≥ 11.5 cm. RESULTS: A total of 4,500 children were enrolled; 593 (13.2%) had MAM at the baseline. MAM at baseline was significantly less common among children in Tanzania than in India (adjusted risk ratio [aRR] 0.37, 95% confidence interval [CI]: 0.30, 0.44, P < 0.001), in children aged 24- < 60 months versus 6- < 12 months (aRR 0.46, 95% CI: 0.38, 0.56, P < 0.001), and in families with household wealth index higher than the median (aRR 0.79, 95% CI: 0.68, 0.92, P = 0.002). Sixty days after outpatient treatment and follow-up, 87 (2.5%) children developed MAM. When compared to children aged 6- < 12 months, children aged 24- < 60 months had a 52% lower risk of developing MAM. Every one unit increase in weight for length (or height) Z score at enrolment was associated with a 93% lower risk of developing MAM during follow-up. CONCLUSIONS: Among children with diarrhoea, younger children and those from households with lower wealth were at greater risk of MAM. These children may benefit from targeted interventions focusing on feeding (targeted nutrition support for at-risk households) and follow up in order to reduce the occurrence of MAM and its consequences.

Azithromycin for Bacterial Watery Diarrhea: A Reanalysis of the AntiBiotics for Children With Severe Diarrhea (ABCD) Trial Incorporating Molecular Diagnostics.

BACKGROUND: Bacterial pathogens cause substantial diarrhea morbidity and mortality among children living in endemic settings, yet antimicrobial treatment is only recommended for dysentery or suspected cholera. METHODS: AntiBiotics for Children with severe Diarrhea was a 7-country, placebo-controlled, double-blind efficacy trial of azithromycin in children 2-23 months of age with watery diarrhea accompanied by dehydration or malnutrition. We tested fecal samples for enteric pathogens utilizing quantitative polymerase chain reaction to identify likely and possible bacterial etiologies and employed pathogen-specific cutoffs based on genomic target quantity in previous case-control diarrhea etiology studies to identify likely and possible bacterial etiologies. RESULTS: Among 6692 children, the leading likely etiologies were rotavirus (21.1%), enterotoxigenic Escherichia coli encoding heat-stable toxin (13.3%), Shigella (12.6%), and Cryptosporidium (9.6%). More than one-quarter (1894 [28.3%]) had a likely and 1153 (17.3%) a possible bacterial etiology. Day 3 diarrhea was less common in those randomized to azithromycin versus placebo among children with a likely bacterial etiology (risk difference [RD]likely, -11.6 [95% confidence interval {CI}, -15.6 to -7.6]) and possible bacterial etiology (RDpossible, -8.7 [95% CI, -13.0 to -4.4]) but not in other children (RDunlikely, -0.3% [95% CI, -2.9% to 2.3%]). A similar association was observed for 90-day hospitalization or death (RDlikely, -3.1 [95% CI, -5.3 to -1.0]; RDpossible, -2.3 [95% CI, -4.5 to -.01]; RDunlikely, -0.6 [95% CI, -1.9 to .6]). The magnitude of risk differences was similar among specific likely bacterial etiologies, including Shigella. CONCLUSIONS: Acute watery diarrhea confirmed or presumed to be of bacterial etiology may benefit from azithromycin treatment. CLINICAL TRIALS REGISTRATION: NCT03130114.

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.

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.

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.

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.

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.

Performance of a validated spontaneous preterm delivery predictor in South Asian and Sub-Saharan African women: a nested case control study.

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.

Effect of 3 Days of Oral Azithromycin on Young Children With Acute Diarrhea in Low-Resource Settings: A Randomized Clinical Trial.

Importance: World Health Organization (WHO) guidelines do not recommend routine antibiotic use for children with acute watery diarrhea. However, recent studies suggest that a significant proportion of such episodes have a bacterial cause and are associated with mortality and growth impairment, especially among children at high risk of diarrhea-associated mortality. Expanding antibiotic use among dehydrated or undernourished children may reduce diarrhea-associated mortality and improve growth. Objective: To determine whether the addition of azithromycin to standard case management of acute nonbloody watery diarrhea for children aged 2 to 23 months who are dehydrated or undernourished could reduce mortality and improve linear growth. Design, Setting, and Participants: The Antibiotics for Children with Diarrhea (ABCD) trial was a multicountry, randomized, double-blind, clinical trial among 8266 high-risk children aged 2 to 23 months presenting with acute nonbloody diarrhea. Participants were recruited between July 1, 2017, and July 10, 2019, from 36 outpatient hospital departments or community health centers in a mixture of urban and rural settings in Bangladesh, India, Kenya, Malawi, Mali, Pakistan, and Tanzania. Each participant was followed up for 180 days. Primary analysis included all randomized participants by intention to treat. Interventions: Enrolled children were randomly assigned to receive either oral azithromycin, 10 mg/kg, or placebo once daily for 3 days in addition to standard WHO case management protocols for the management of acute watery diarrhea. Main Outcomes and Measures: Primary outcomes included all-cause mortality up to 180 days after enrollment and linear growth faltering 90 days after enrollment. Results: A total of 8266 children (4463 boys [54.0%]; mean [SD] age, 11.6 [5.3] months) were randomized. A total of 20 of 4133 children in the azithromycin group (0.5%) and 28 of 4135 children in the placebo group (0.7%) died (relative risk, 0.72; 95% CI, 0.40-1.27). The mean (SD) change in length-for-age z scores 90 days after enrollment was -0.16 (0.59) in the azithromycin group and -0.19 (0.60) in the placebo group (risk difference, 0.03; 95% CI, 0.01-0.06). Overall mortality was much lower than anticipated, and the trial was stopped for futility at the prespecified interim analysis. Conclusions and Relevance: The study did not detect a survival benefit for children from the addition of azithromycin to standard WHO case management of acute watery diarrhea in low-resource settings. There was a small reduction in linear growth faltering in the azithromycin group, although the magnitude of this effect was not likely to be clinically significant. In low-resource settings, expansion of antibiotic use is not warranted. Adherence to current WHO case management protocols for watery diarrhea remains appropriate and should be encouraged. Trial Registration: ClinicalTrials.gov Identifier: NCT03130114.

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.

Direct maternal morbidity and the risk of pregnancy-related deaths, stillbirths, and neonatal deaths in South Asia and sub-Saharan Africa: A population-based prospective cohort study in 8 countries.

BACKGROUND: Maternal morbidity occurs several times more frequently than mortality, yet data on morbidity burden and its effect on maternal, foetal, and newborn outcomes are limited in low- and middle-income countries. We aimed to generate prospective, reliable population-based data on the burden of major direct maternal morbidities in the antenatal, intrapartum, and postnatal periods and its association with maternal, foetal, and neonatal death in South Asia and sub-Saharan Africa. METHODS AND FINDINGS: This is a prospective cohort study, conducted in 9 research sites in 8 countries of South Asia and sub-Saharan Africa. We conducted population-based surveillance of women of reproductive age (15 to 49 years) to identify pregnancies. Pregnant women who gave consent were include in the study and followed up to birth and 42 days postpartum from 2012 to 2015. We used standard operating procedures, data collection tools, and training to harmonise study implementation across sites. Three home visits during pregnancy and 2 home visits after birth were conducted to collect maternal morbidity information and maternal, foetal, and newborn outcomes. We measured blood pressure and proteinuria to define hypertensive disorders of pregnancy and woman's self-report to identify obstetric haemorrhage, pregnancy-related infection, and prolonged or obstructed labour. Enrolled women whose pregnancy lasted at least 28 weeks or those who died during pregnancy were included in the analysis. We used meta-analysis to combine site-specific estimates of burden, and regression analysis combining all data from all sites to examine associations between the maternal morbidities and adverse outcomes. Among approximately 735,000 women of reproductive age in the study population, and 133,238 pregnancies during the study period, only 1.6% refused consent. Of these, 114,927 pregnancies had morbidity data collected at least once in both antenatal and in postnatal period, and 114,050 of them were included in the analysis. Overall, 32.7% of included pregnancies had at least one major direct maternal morbidity; South Asia had almost double the burden compared to sub-Saharan Africa (43.9%, 95% CI 27.8% to 60.0% in South Asia; 23.7%, 95% CI 19.8% to 27.6% in sub-Saharan Africa). Antepartum haemorrhage was reported in 2.2% (95% CI 1.5% to 2.9%) pregnancies and severe postpartum in 1.7% (95% CI 1.2% to 2.2%) pregnancies. Preeclampsia or eclampsia was reported in 1.4% (95% CI 0.9% to 2.0%) pregnancies, and gestational hypertension alone was reported in 7.4% (95% CI 4.6% to 10.1%) pregnancies. Prolonged or obstructed labour was reported in about 11.1% (95% CI 5.4% to 16.8%) pregnancies. Clinical features of late third trimester antepartum infection were present in 9.1% (95% CI 5.6% to 12.6%) pregnancies and those of postpartum infection in 8.6% (95% CI 4.4% to 12.8%) pregnancies. There were 187 pregnancy-related deaths per 100,000 births, 27 stillbirths per 1,000 births, and 28 neonatal deaths per 1,000 live births with variation by country and region. Direct maternal morbidities were associated with each of these outcomes. CONCLUSIONS: Our findings imply that health programmes in sub-Saharan Africa and South Asia must intensify their efforts to identify and treat maternal morbidities, which affected about one-third of all pregnancies and to prevent associated maternal and neonatal deaths and stillbirths. TRIAL REGISTRATION: The study is not a clinical trial.

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.

Lower-Dose Zinc for Childhood Diarrhea - A Randomized, Multicenter Trial.

BACKGROUND: The World Health Organization recommends 20 mg of zinc per day for 10 to 14 days for children with acute diarrhea; in previous trials, this dosage decreased diarrhea but increased vomiting. METHODS: We randomly assigned 4500 children in India and Tanzania who were 6 to 59 months of age and had acute diarrhea to receive 5 mg, 10 mg, or 20 mg of zinc sulfate for 14 days. The three primary outcomes were a diarrhea duration of more than 5 days and the number of stools (assessed in a noninferiority analysis) and the occurrence of vomiting (assessed in a superiority analysis) within 30 minutes after zinc administration. RESULTS: The percentage of children with diarrhea for more than 5 days was 6.5% in the 20-mg group, 7.7% in the 10-mg group, and 7.2% in the 5-mg group. The difference between the 20-mg and 10-mg groups was 1.2 percentage points (upper boundary of the 98.75% confidence interval [CI], 3.3), and that between the 20-mg and 5-mg groups was 0.7 percentage points (upper boundary of the 98.75% CI, 2.8), both of which were below the noninferiority margin of 4 percentage points. The mean number of diarrheal stools was 10.7 in the 20-mg group, 10.9 in the 10-mg group, and 10.8 in 5-mg group. The difference between the 20-mg and 10-mg groups was 0.3 stools (upper boundary of the 98.75% CI, 1.0), and that between the 20-mg and 5-mg groups was 0.1 stools (upper boundary of the 98.75% CI, 0.8), both of which were below the noninferiority margin (2 stools). Vomiting within 30 minutes after administration occurred in 19.3%, 15.6%, and 13.7% of the patients in the 20-mg, 10-mg, and 5-mg groups, respectively; the risk was significantly lower in the 10-mg group than in the 20-mg group (relative risk, 0.81; 97.5% CI, 0.67 to 0.96) and in the 5-mg group than in the 20-mg group (relative risk, 0.71; 97.5% CI, 0.59 to 0.86). Lower doses were also associated with less vomiting beyond 30 minutes after administration. CONCLUSIONS: Lower doses of zinc had noninferior efficacy for the treatment of diarrhea in children and were associated with less vomiting than the standard 20-mg dose. (Funded by the Bill and Melinda Gates Foundation; ZTDT ClinicalTrials.gov number, NCT03078842.).

A rapid triage test for active pulmonary tuberculosis in adult patients with persistent cough.

Improved tuberculosis (TB) prevention and control depend critically on the development of a simple, readily accessible rapid triage test to stratify TB risk. We hypothesized that a blood protein-based host response signature for active TB (ATB) could distinguish it from other TB-like disease (OTD) in adult patients with persistent cough, thereby providing a foundation for a point-of-care (POC) triage test for ATB. Three adult cohorts consisting of ATB suspects were recruited. A bead-based immunoassay and machine learning algorithms identified a panel of four host blood proteins, interleukin-6 (IL-6), IL-8, IL-18, and vascular endothelial growth factor (VEGF), that distinguished ATB from OTD. An ultrasensitive POC-amenable single-molecule array (Simoa) panel was configured, and the ATB diagnostic algorithm underwent blind validation in an independent, multinational cohort in which ATB was distinguished from OTD with receiver operator characteristic-area under the curve (ROC-AUC) of 0.80 [95% confidence interval (CI), 0.75 to 0.85], 80% sensitivity (95% CI, 73 to 85%), and 65% specificity (95% CI, 57 to 71%). When host antibodies against TB antigen Ag85B were added to the panel, performance improved to 86% sensitivity and 69% specificity. A blood-based host response panel consisting of four proteins and antibodies to one TB antigen can help to differentiate ATB from other causes of persistent cough in patients with and without HIV infection from Africa, Asia, and South America. Performance characteristics approach World Health Organization (WHO) target product profile accuracy requirements and may provide the foundation for an urgently needed blood-based POC TB triage test.