Published reports examining lipid levels during pregnancy and preeclampsia have been inconsistent. The objective of this meta-analysis was to test the association between preeclampsia and maternal total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), non-HDL-C, and triglyceride levels measured during pregnancy. We conducted a systematic search for studies published between the index date until July 2013 reporting maternal lipid levels in women with preeclampsia and normotensive pregnant women. Seventy-four studies met all eligibility criteria and were included in the meta-analysis. Weighted mean differences in lipid levels were calculated using a random-effects model. Statistical heterogeneity was investigated using the I² statistic. Meta-regression was used to identify sources of heterogeneity. Preeclampsia was associated with elevated total cholesterol, non-HDL-C, and triglyceride levels, regardless of gestational age at the time of blood sampling, and with lower levels of HDL-C in the third trimester. A marginal association was found with LDL-C levels. Statistical heterogeneity was detected in all analyses. Meta-regression analyses suggested that differences in body mass index (weight (kg)/height (m)²) across studies may be partially responsible for the heterogeneity in the triglyceride and LDL-C analyses. This systematic review and meta-analysis demonstrates that women who develop preeclampsia have elevated levels of total cholesterol, non-HDL-C, and triglycerides during all trimesters of pregnancy, as well as lower levels of HDL-C during the third trimester.

Most of the women who smoke before pregnancy continue smoking during pregnancy, and some start to quit smoking after being pregnant, although existing guidelines for pregnancy recommend that women who smoke should quit smoking before pregnancy. Findings about the timing and intensity of maternal smoking, especially low-intensity smoking (1–9 cigarettes per day), and preterm birth are still inconsistent and ambiguous. This study aimed to examine the association of the timing of smoking and doses of smoking before pregnancy and during the first or second trimester of pregnancy with preterm birth in a large-scale population-based retrospective cohort study. We used nationwide birth certificate data from singleton mother–infant pairs in the United States National Vital Statistics System, 2011–2018. All adult women with live singleton births, without preexisting hypertension or diabetes, and with complete data on smoking and gestational age at delivery were included. Participants reported their smoking status (yes or no) and daily number of cigarettes consumed before and during each trimester of pregnancy. The outcome of interest was preterm birth, defined as a birth before 37 weeks of gestation. Logistic regression models were used to estimate the odds ratio (OR) with 95% confidence intervals (CIs) of preterm birth associated with smoking status and the number of cigarettes consumed, adjusting for maternal age, race/ethnicity, parity, education levels, prepregnancy BMI, previous history of preterm birth, marital status, infant sex, and initiation of prenatal care. This study included 25,623,479 women, with a mean age of 29 years (range 20–50 years); 13,742,486 (53.6%) participants were of non-Hispanic white ancestry, 5,971,598 (23.3%) of Hispanic ancestry, and 3,417,456 (13.34%) of non-Hispanic black ancestry. The prevalence of preterm birth was 9.3% (n = 2,378,398). We found that maternal smoking during pregnancy, even at a very low level of intensity, was associated with an increased risk of preterm delivery. The adjusted ORs (95% CI) of preterm birth for mothers who smoked 1–2, 3–5, 6–9, 10–19, and ≥20 cigarettes per day during the first trimester compared with mothers who did not smoke were 1.31 (1.29–1.33), 1.31 (1.30–1.32), 1.33 (1.31–1.35), 1.44 (1.43–1.45), and 1.53 (1.52–1.55), respectively (all P values < 0.001), whereas for those who smoked during the second trimester, the corresponding ORs were 1.37 (1.35–1.39), 1.36 (1.35–1.38), 1.36 (1.34–1.38), 1.48 (1.47–1.49), and 1.59 (1.58–1.61), respectively (all P values < 0.001). Furthermore, smokers who quit before pregnancy, regardless of smoking intensity, had a comparable risk of preterm birth with nonsmokers, although this was not the case when cessation occurred in the first or second trimester of pregnancy. The major limitation of this study is the self-reported information about smoking, which may be subject to information bias. In addition, we cannot rule out the possibility of residual confounding caused by unmeasured factors in an observational research design. In this study, we observed that low-intensity cigarette consumption during either the first or second trimester of pregnancy, even as low as 1–2 cigarettes per day, was associated with an increased risk of preterm birth. These findings suggest that there is no safe level or safe trimester for maternal smoking during pregnancy. Women of reproductive age who smoke should be strongly encouraged and supported to quit smoking before pregnancy.

Metabolic syndrome is a growing cause of morbidity and mortality worldwide. Metabolic syndrome is characterized by the presence of a variety of metabolic disturbances including obesity, hyperlipidemia, hypertension, and elevated fasting blood sugar. Although the risk for metabolic syndrome has largely been attributed to adult lifestyle factors such as poor nutrition, lack of exercise, and smoking, there is now strong evidence suggesting that predisposition to the development of metabolic syndrome begins in utero. First posited by Hales and Barker in 1992, the “thrifty phenotype” hypothesis proposes that susceptibility to adult chronic diseases can occur in response to exposures in the prenatal and perinatal periods. This hypothesis has been continually supported by epidemiologic studies and studies involving animal models. In this review, we describe the structural, metabolic and epigenetic changes that occur in response to adverse intrauterine environments including prenatal and postnatal diet, maternal obesity, and pregnancy complications. Given the increasing prevalence of metabolic syndrome in both the developed and developing worlds, a greater understanding and appreciation for the role of the intrauterine environment in adult chronic disease etiology is imperative.

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.