Metagenomic next-generation sequencing (mNGS) experiments involving small amounts of nucleic acid input are highly susceptible to erroneous conclusions resulting from unintentional sequencing of occult contaminants, especially those derived from molecular biology reagents. Recent work suggests that, for any given microbe detected by mNGS, an inverse linear relationship between microbial sequencing reads and sample mass implicates that microbe as a contaminant. By associating sequencing read output with the mass of a spike-in control, we demonstrate that contaminant nucleic acid can be quantified in order to identify the mass contributions of each constituent. In an experiment using a high-resolution (n = 96) dilution series of HeLa RNA spanning 3-logs of RNA mass input, we identified a complex set of contaminants totaling 9.1 ± 2.0 attograms. Given the competition between contamination and the true microbiome in ultra-low biomass samples such as respiratory fluid, quantification of the contamination within a given batch of biological samples can be used to determine a minimum mass input below which sequencing results may be distorted. Rather than completely censoring contaminant taxa from downstream analyses, we propose here a statistical approach that allows separation of the true microbial components from the actual contribution due to contamination. We demonstrate this approach using a batch of n = 97 human serum samples and note that despite E. coli contamination throughout the dataset, we are able to identify a patient sample with significantly more E. coli than expected from contamination alone. Importantly, our method assumes no prior understanding of possible contaminants, does not rely on any prior collection of environmental or reagent-only sequencing samples, and does not censor potentially clinically relevant taxa, thus making it a generalized approach to any kind of metagenomic sequencing, for any purpose, clinical or otherwise.

Objective: To examine the relationship between maternal characteristics, serum biomarkers and preterm birth (PTB) by spontaneous and medically indicated subtypes.Design: Population‐based cohort.Setting: California, United States of America.Population: From a total population of 1 004 039 live singleton births in 2009 and 2010, 841 665 pregnancies with linked birth certificate and hospital discharge records were included.Methods: Characteristics were compared for term and preterm deliveries by PTB subtype using logistic regression and odds ratios adjusted for maternal characteristics and obstetric factors present in final stepwise models and 95% confidence intervals. First‐trimester and second‐trimester serum marker levels were analysed in a subset of 125 202 pregnancies with available first‐trimester and second‐trimester serum biomarker results.Main outcome measure: PTB by subtype.Results: In fully adjusted models, ten characteristics and three serum biomarkers were associated with increased risk in each PTB subtype (Black race/ethnicity, pre‐existing hypertension with and without pre‐eclampsia, gestational hypertension with pre‐eclampsia, pre‐existing diabetes, anaemia, previous PTB, one or two or more previous caesarean section(s), interpregnancy interval ≥ 60 months, low first‐trimester pregnancy‐associated plasma protein A, high second‐trimester α‐fetoprotein, and high second‐trimester dimeric inhibin A). These risks occurred in 51.6–86.2% of all pregnancies ending in PTB depending on subtype. The highest risk observed was for medically indicated PTB <32 weeks in women with pre‐existing hypertension and pre‐eclampsia (adjusted odds ratio 89.7, 95% CI 27.3–111.2).Conclusions: Our findings suggest a shared aetiology across PTB subtypes. These commonalities point to targets for further study and exploration of risk reduction strategies.Tweetable Findings suggest a shared aetiology across preterm birth subtypes. Patterns may inform risk reduction efforts.

Accurate gestational dating is a critical component of obstetric and newborn care. In the absence of early ultrasound, many clinicians rely on less accurate measures, such as last menstrual period or symphysis-fundal height during pregnancy, or Dubowitz scoring or the Ballard (or New Ballard) method at birth. These measures often underestimate or overestimate gestational age and can lead to misclassification of babies as born preterm, which has both short- and long-term clinical care and public health implications. We sought to evaluate whether metabolic markers in newborns measured as part of routine screening for treatable inborn errors of metabolism can be used to develop a population-level metabolic gestational dating algorithm that is robust despite intrauterine growth restriction and can be used when fetal ultrasound dating is not available. We focused specifically on the ability of these markers to differentiate preterm births (PTBs) (<37 weeks) from term births and to assign a specific gestational age in the PTB group. We evaluated a cohort of 729,503 singleton newborns with a California birth in 2005 through 2011 who had routine newborn metabolic screening and fetal ultrasound dating at 11-20 weeks' gestation. Using training and testing subsets (divided in a ratio of 3:1) we evaluated the association among PTB, target newborn characteristics, acylcarnitines, amino acids, thyroid-stimulating hormone, 17-hydroxyprogesterone, and galactose-1-phosphate-uridyl-transferase. We used multivariate backward stepwise regression to test for associations and linear discriminate analyses to create a linear function for PTB and to assign a specific week of gestation. We used sensitivity, specificity, and positive predictive value to evaluate the performance of linear functions. Along with birthweight and infant age at test, we included 35 of the 51 metabolic markers measured in the final multivariate model comparing PTBs and term births. Using a linear discriminate analyses-derived linear function, we were able to sort PTBs and term births accurately with sensitivities and specificities of ≥95% in both the training and testing subsets. Assignment of a specific week of gestation in those identified as PTBs resulted in the correct assignment of week ±2 weeks in 89.8% of all newborns in the training and 91.7% of those in the testing subset. When PTB rates were modeled using the metabolic dating algorithm compared to fetal ultrasound, PTB rates were 7.15% vs 6.11% in the training subset and 7.31% vs 6.25% in the testing subset. When considered in combination with birthweight and hours of age at test, metabolic profile evaluated within 8 days of birth appears to be a useful measure of PTB and, among those born preterm, of specific week of gestation ±2 weeks. Dating by metabolic profile may be useful in instances where there is no fetal ultrasound due to lack of availability or late entry into care.

Environmental pollution exposure during pregnancy has been identified as a risk factor for preterm birth. Most studies have evaluated exposures individually and in limited study populations. We examined the associations between several environmental exposures, both individually and cumulatively, and risk of preterm birth in Fresno County, California. We also evaluated early (< 34 weeks) and spontaneous preterm birth. We used the Communities Environmental Health Screening Tool and linked hospital discharge records by census tract from 2009 to 2012. The environmental factors included air pollution, drinking water contaminants, pesticides, hazardous waste, traffic exposure and others. Social factors, including area-level socioeconomic status (SES) and race/ethnicity were also evaluated as potential modifiers of the relationship between pollution and preterm birth. In our study of 53,843 births, risk of preterm birth was associated with higher exposure to cumulative pollution scores and drinking water contaminants. Risk of preterm birth was twice as likely for those exposed to high versus low levels of pollution. An exposure-response relationship was observed across the quintiles of the pollution burden score. The associations were stronger among early preterm births in areas of low SES. In Fresno County, we found multiple pollution exposures associated with increased risk for preterm birth, with higher associations among the most disadvantaged. This supports other evidence finding environmental exposures are important risk factors for preterm birth, and furthermore the burden is higher in areas of low SES. This data supports efforts to reduce the environmental burden on pregnant women.

Objective: To evaluate if mid-pregnancy immune and growth-related molecular factors predict preterm birth (PTB) with and without (±) preeclampsia. Study design: Included were 400 women with singleton deliveries in California in 2009–2010 (200 PTB and 200 term) divided into training and testing samples at a 2:1 ratio. Sixty-three markers were tested in 15–20 serum samples using multiplex technology. Linear discriminate analysis was used to create a discriminate function. Model performance was assessed using area under the receiver operating characteristic curve (AUC). Results: Twenty-five serum biomarkers along with maternal age 80% of women with PTB ± preeclampsia with best performance in women with preterm preeclampsia (AUC = 0.889, 95% confidence interval (0.822–0.959) training; 0.883 (0.804–0.963) testing). Conclusion: Together with maternal age and poverty status, mid-pregnancy immune and growth factors reliably identified most women who went on to have a PTB ± preeclampsia.

Importance: Preterm birth (PTB) (gestational age <37 weeks) is a major cause of infant mortality and morbidity in the US and is marked by racial and ethnic and socioeconomic inequities. Further research is needed to elucidate the association of risk and protective factors with trends in PTB rates and with related inequities.Objective: To describe the association of PTB rates with inequities as well as related risk and protective factors over the past decade in a US population–based cohort.Design, Setting, and Participants: This retrospective cohort study of singleton live births in California from January 1, 2011, to December 31, 2022, was conducted using vital statistics records and hospital records. The cohort included births with a gestational age of 22 to 44 weeks.Main Outcomes and Measures: Preterm birth rates by racial and ethnic group and by public and nonpublic insurance (considered as a proxy for socioeconomic status) were studied across years. Log-linear regression (relative risks with 95% CIs) was used to evaluate risk and protective factors within groups. Associations of PTB rates with risk and protective factors were assessed.Results: This study included 5 431 018 singleton live births to individuals who identified as American Indian or Alaska Native (0.3%), Asian (14.2%), Black (4.9%), Hispanic (47.8%), or White (27.0%). A total of 43.1% of births were to individuals with public health insurance. From 2011 to 2022, the overall PTB rate increased from 6.8% to 7.5% (change [SE], 10.6% [0.6%]; z score of 18.5; P < .001). Differences in PTB rates and associated changes were observed for racial and ethnic groups and insurance groups. For example, 2022 PTB rates ranged from 5.8% among White individuals with nonpublic insurance to 11.3% among Black individuals with public health insurance. From 2011 to 2022, PTB rates decreased from 9.1% to 8.8% (change [SE], −3.5% [4.2]; z score of −0.8; P = .42) among Black individuals with nonpublic insurance, whereas they increased from 6.4% to 9.5% (change [SE], 49.8% [16.0%]; z score of 3.1; P = .002) among American Indian or Alaska Native individuals with nonpublic insurance. Increases in some risk factors (eg, preexisting diabetes, sexually transmitted infections, mental health conditions) were observed in most groups, and decreases in some protective factors (eg, participation in the California Women, Infants, and Children program) (P for trend < .001 from 2011 to 2021) were observed mostly in low-income groups.Conclusions and Relevance: In this cohort study of singleton live births in California, PTB rates increased in many groups. Persistent racial and ethnic and socioeconomic inequities were also observed. Changes in risk and protective factors provided clues to patterns of PTB. These data point to an urgent need to address factors associated with PTB at both the individual and population levels.