Elevated Micro- and Nanoplastics Detected in Preterm Human Placentae

Elevated Micro- and Nanoplastics Detected in Preterm Human Placentae | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Elevated Micro- and Nanoplastics Detected in Preterm Human Placentae Michael Jochum#, Marcus Garcia#, Alexandra Hammerquist, Jacquelyne Howell, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5903715/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Recent analytical advancements have uncovered increasing micro- and nanoplastics (MNPs) in environmental, dietary, and biological domains, raising concerns about their health impacts. Preterm birth (PTB), a leading cause of maternal and neonatal morbidity and mortality, may be influenced by MNP exposure, yet this relationship remains unexplored. This study quantified 12 MNP polymers in placentae from term (n=87) and preterm (n=71) deliveries using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Cumulative MNP concentrations were 28% higher in PTB placentae (mean ±SD: 224.7 ± 180.7 µg/g vs. 175.5 ± 137.9 µg/g; p=0.038). Polyvinyl chloride (PVC), polyethylene terephthalate (PET), polyurethane (PU), and polycarbonate (PC) were significantly elevated in PTB, and PET, PU, and PC inversely correlated with gestational age and birth weight. Logistic regression identified PVC and PC as independent predictors of PTB. These findings suggest total and specific MNPs are associated with PTB, providing actionable insights and emphasizing the importance of minimizing exposure during pregnancy. Health sciences/Medical research/Outcomes research Biological sciences/Developmental biology microplastic preterm birth obstetrics outcomes developmental toxicology environmental pollutants Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Micro- and nanoplastics (MNPs) have emerged as significant environmental contaminants due to their widespread use and persistence in the environment. Humans are estimated to ingest and absorb growing quantities of MNPs through food, water, and air. 1 , 2 This pervasive exposure raises concerns regarding the potential health impacts of MNPs on vulnerable populations, such as pregnant women and developing fetuses. While MNPs have been detected in human tissues and fluids, 3 – 14 their long-term effects, especially during pregnancy, remain poorly understood. Preterm birth (PTB), a leading cause of maternal and early-life mortality and morbidity 15 , is a major global health challenge. Despite multiple decades of research, there are no effective interventions, and the spontaneous PTB rate remains effectively unchanged, affecting approximately 10% of pregnancies worldwide and incurring an estimated $ 25 billion in healthcare costs in the United States. 16 , 17 Despite decades of research, most cases of spontaneous (non-medically indicated) PTB remain idiopathic, with growing evidence implicating both vascular and inflammatory processes as potential drivers. 18 – 21 Given their putative causal role in modulating atherosclerotic disease, 22 a potential role for MNPs exposure in driving aberrant placental pathophysiology and triggering PTB remains a critical and understudied area of maternal-fetal health research. Environmental toxicants such as air pollution, heavy metals (e.g., lead and cadmium), endocrine-disrupting chemicals (e.g., phthalates and bisphenol A), and polycyclic aromatic hydrocarbons (PAHs) have been implicated in PTB. 23 – 31 Many of these toxicants can accumulate and cross the placenta, disrupting placental function, inducing oxidative stress, and contributing to adverse pregnancy outcomes. Despite these insights, MNPs, which share many characteristics with these toxicants, have yet to be thoroughly investigated for their role in PTB. In 2021, MNPs were first identified in human placentae, providing critical evidence that they can reach the maternal-fetal barrier. 12 Subsequent studies have shown that MNPs are ubiquitous in placental tissues at varying concentrations 11 , 32 – 41 and have been detected in amniotic fluid, cord blood, and meconium 11 , 34 , 35 , 37 , 41 , 42 . However, there currently are limited studies that have explored the relationship between MNP exposure and health outcomes. Recently, significantly higher concentrations of MNPs were reported in placentae from fetal growth-restricted pregnancies compared to controls, with inverse associations noted between MNP levels and birth weight. 32 Emerging evidence also implicates MNPs in recurrent pregnancy loss, with a study using pyrolysis gas chromatography-mass spectrometry (Py-GC/MS) reporting significantly higher polystyrene (PS) concentrations in placentae from miscarriage cases compared to controls (odds ratio of 34, 95% CI: 3.61–320). 40 Despite this growing body of preclinical evidence, significant gaps remain in understanding if and how MNPs contribute to adverse gestational outcomes such as PTB. In this study, we hypothesized that placental MNP concentrations and polymer profiles differ between term and PTBs. Using Py-GC/MS, we quantified 12 types of MNPs in N = 158 human placentae, focusing on their associations with gestational age at delivery, maternal characteristics, and perinatal outcomes. Our findings reveal novel insights into the accelerated bioaccumulation of MNPs in preterm placentae, their correlations with gestational age, and their potential associations with maternal conditions known to exacerbate pathways critical to labor initiation. Results Clinical and demographic characteristics of the cohort. The study comprised 158 subjects stratified by gestational age at delivery: Term (≥ 37 weeks; n = 87) and Preterm (< 37 weeks; n = 71). Nesting of the cohort and study design assured an equal distribution of preterm and term deliveries, equal representation across ethnicity and race (non-Hispanic White, Hispanic White, non-Hispanic Black, and non-Hispanic Asian), an equal fetal sex ratio, cesarean delivery, and live birth. Table 1 summarizes the clinical and demographic characteristics of the cohort (relevant clinical metadata available in Table S1 a). Maternal race and ethnicity differed significantly between groups ( p < 0.001), with a higher proportion of Hispanic White participants in the PTB group (70.8%) compared to the Term group (28.7%) and a lower proportion of non-Hispanic Black participants in the PTB group (12.5%) compared to the Term group (20.7%; p = 0.019). As anticipated, hypertension was more prevalent in the PTB group (28.2%) than in the Term group (8.0%; p = 0.002). Similarly, the Social Deprivation Index (SDI) was higher in the PTB group (79.62 ± 23.29) compared to the Term group (70.32 ± 28.72; p = 0.04). Prior PTBs were more frequent in the PTB group (23.9%) than in the Term group (9.2%; p = 0.021). Similarly and as anticipated, the type of labor also differed significantly ( p < 0.001 ), with more spontaneous labor in the PTB group (21.1%) than in the Term group (8.0%; p = 0.0 33), higher rates of preeclampsia (35.2% vs. 4.6%; p < 0.001) and PPROM (14.1% vs. 0%; p = 0.001) in the PTB group. Medical co-morbidities accompanying cesarean delivery were significantly different ( p < 0.001), while other medical indications were more common in the Term group (10.3% vs. 1.4%; p = 0.049). As expected, gestational age at delivery (34.72 ± 2.11 weeks vs. 39.32 ± 1.39 weeks; p < 0.001) and birth weight (2499.87 ± 674.09 g vs. 3406.91 ± 470.70 g; p < 0.001 ) were significantly different between preterm and term deliveries, respectively. APGAR scores at 1 minute were lower in PTB deliveries (7.34 ± 1.93) compared to Term deliveries (7.89 ± 1.63; p = 0.04), which is not considered a clinically meaningful difference, despite the statistical difference. Potential confounders of PTB, such as prior PTBs, preeclampsia, and racial health disparities, were controlled for in subsequent univariate and multivariate analyses, ensuring robust comparisons. Table 1 Clinical and Demographic Characteristics of the Study Cohort by Delivery Term Classification . Summary of relevant maternal demographics, clinical characteristics, obstetric outcomes, and neonatal parameters across delivery term classifications: Term (≥ 37 weeks) and Preterm (< 37 weeks). Continuous variables are presented as mean ± standard deviation (SD), with comparisons assessed using the Kruskal-Wallis test. Categorical variables are displayed as counts (n) and percentages (%), with comparisons evaluated using the Chi-Square test. Statistically significant differences are denoted in bold ( p < 0.05). Abbreviations: PPROM = Preterm premature rupture of the membranes, SGA = Small for gestational age, LGA = Large for gestational age, APGAR = Scoring for appearance, pulse, grimace, activity, and respiration. # Note: All deliveries in this study were Cesarean. Variable Overall Cohort ( N = 158) Preterm Group ( n = 71) Term Group ( n = 87) p -value Maternal Age, years 31.71 ± 6.44 32.14 ± 6.37 31.35 ± 6.51 0.441 Ethnicity/Race: < 0.001 Hispanic White 76 (47.8%) 51 (70.8%) 25 (28.7%) < 0.001 Non-Hispanic Black 27 (17.0%) 9 (12.5%) 18 (20.7%) 0.019 Non-Hispanic Asian 24 (15.1%) 5 (6.9%) 19 (21.8%) 0.263 Non-Hispanic White 23 (14.5%) 5 (6.9%) 18 (20.7%) 0.028 Hispanic Black 4 (2.5%) 1 (1.4%) 3 (3.4%) 1.000 Hispanic/Race Not Reported 5 (3.1%) 1 (1.4%) 4 (4.6%) 1.000 Hypertension 27 (17.1%) 20 (28.2%) 7 (8.0%) 0.002 Social Deprivation Index (SDI) 74.45 ± 26.77 79.62 ± 23.29 70.32 ± 28.72 0.040 Maternal Smoking, never 147 (93.0%) 65 (91.5%) 82 (94.3%) 0.726 Gestational Diabetes, yes 31 (19.6%) 18 (25.4%) 13 (14.9%) 0.151 Gravida 2.95 ± 1.80 2.86 ± 1.56 3.02 ± 1.98 0.165 Prior PTB, yes 25 (15.8%) 17 (23.9%) 8 (9.19%) 0.021 Preeclampsia 29 (18.4%) 25 (35.2%) 4 (4.6%) < 0.001 PPROM 10 (6.3%) 10 (14.1%) 0 (0.0%) 0.001 Type of Labor: 0.001 No Labor 89 (56.3%) 46 (64.8%) 43 (49.4%) 0.076 Spontaneous 22 (13.9%) 15 (21.1%) 7 (8.0%) 0.033 Spontaneous Augmented 9 (5.7%) 2 (2.8%) 7 (8.0%) 0.287 Indication for Delivery # : < 0.001 Indicated Prelabor Caesarean 3 (1.9%) 3 (4.2%) 0 (0.0%) 0.177 Placental Abnormality 4 (2.5%) 4 (5.6%) 0 (0.0%) 0.083 Fetal Indication 7 (4.4%) 6 (8.5%) 1 (1.1%) 0.067 Other Medically Indicated 10 (6.3%) 1 (1.4%) 9 (10.3%) 0.049 Infant Gender, male 83 (52.5%) 41 (57.7%) 42 (48.3%) 0.305 Gestational Age, weeks 37.25 ± 2.89 34.72 ± 2.11 39.32 ± 1.39 < 0.001 Birth Weight, grams 2999.32 ± 727.20 2499.87 ± 674.09 3406.91 ± 470.70 < 0.001 Birth Weight Percentile 54.84 ± 28.38 50.93 ± 25.67 58.03 ± 30.19 0.129 SGA 6 (3.8%) 2 (2.8%) 4 (4.6%) 0.555 LGA 28 (17.7%) 9 (12.7%) 19 (21.8%) 0.197 APGAR at 1 Minute 7.64 ± 1.79 7.34 ± 1.93 7.89 ± 1.63 0.040 APGAR at 5 Minutes 8.63 ± 1.09 8.45 ± 1.50 8.77 ± 0.54 0.616 Despite shorter gestations, PTB placentae have higher concentrations of MNPs than those delivered at term. Placental specimens were subjected to Py-GC/MS to quantify twelve types of MNPs: polyethylene (PE), styrene-butadiene rubber (SBR), polyvinyl chloride (PVC), polypropylene (PP), nylon 66 (N66), polyethylene terephthalate (PET), nylon 6 (N6), polymethyl methacrylate (PMMA), acrylonitrile butadiene styrene (ABS), polyurethane (PU), polycarbonate (PC), and PS. Samples were run in duplicate and quantified relative to standards (Py-GC/MS parameters, settings, and standards available in Table S1 b). The cumulative concentrations of MNPs were log1p transformed 43 and assessed comparing preterm versus term delivery status, revealing MNP concentrations in PTB placentae were 28% higher (Fig. 1 a, mean ± SD: 224.7 ± 180.7 µg/g vs. 175.5 ± 137.9 µg/g; p = 0.038, Wilcoxon test). Individual MNP concentrations were analyzed in Table 2 and Fig. 1 b, identifying significantly higher MNP types in PTB placentae, including PVC ( p = 0.045; 17% higher), PET ( p < 0.001; 113% higher), PU ( p < 0.001; 157% higher), and PC ( p = 0.007; 46% higher). Only ABS was higher in term placentae ( p = 0.02; 42% higher). Table 2 Comparisons of MNP concentrations in placentae across the overall cohort, preterm, and term groups. Values are expressed as mean ± SD. P -values were calculated using Wilcoxon tests to compare preterm and term groups, with statistical significance indicated in bold ( p < 0.05). # Cumulative MNPs were analyzed using log1p-normalized data in the Wilcoxon tests. Micro- and Nanoplastics (MNPs) in µg/g placental tissue Overall Cohort ( N = 158) Preterm Group ( n = 71) Term Group ( n = 87) p -value Polyethylene (PE) 102.6 ± 117.6 105.3 ± 135.0 88.11 ± 96.12 0.44 Styrene-butadiene rubber (SBR) 33.85 ± 29.18 28.49 ± 30.33 34.99 ± 28.34 0.17 Polyvinyl chloride (PVC) 15.18 ± 12.49 15.54 ± 14.44 13.27 ± 10.70 0.045 Polypropylene (PP) 13.63 ± 12.49 14.63 ± 14.36 11.78 ± 9.87 0.087 Nylon 66 (N66) 10.06 ± 8.41 9.03 ± 8.35 10.15 ± 8.69 0.43 Polyethylene terephthalate (PET) 8.62 ± 10.77 11.68 ± 12.66 5.49 ± 8.18 < 0.001 Nylon 6 (N6) 6.22 ± 15.05 7.48 ± 20.72 4.72 ± 7.55 0.22 Polymethyl methacrylate (PMMA) 2.81 ± 2.81 2.77 ± 3.28 2.63 ± 2.36 0.35 Acrylonitrile butadiene styrene (ABS) 1.73 ± 3.83 1.35 ± 3.68 1.92 ± 3.93 0.02 Polyurethane (PU) 2.63 ± 4.49 3.86 ± 5.42 1.50 ± 3.27 < 0.001 Polycarbonate (PC) 1.20 ± 1.74 1.39 ± 2.54 0.95 ± 0.51 0.007 Polystyrene (PS) 0.534 ± 3.25 0.534 ± 3.25 0.04 ± 0.22 0.11 Cumulative 197.62 ± 159.91 224.67 ± 180.69 175.54 ± 137.88 0.038 # Placental MNP concentrations are associated with maternal comorbid conditions and environmental factors. Spearman’s correlation analyses were performed to examine associations between placental MNP concentrations and clinical metadata, including maternal comorbidities (Fig. 2 a; Table S1 c). Cumulative MNPs showed significant positive correlations with PC, PVC, N6, PMMA, PE, N66, ABS, and SBR ( ρ = 0.44–0.93, p < 0.001). Additionally, significant inverse correlations were identified between birth weight with placental PU ( ρ= -0.35, p = 0.001) and PC ( ρ= -0.14, p = 0.048) concentrations. Wilcoxon tests revealed elevated PET (p = 0.031) and PU ( p = 0.053) in placentae from subjects with preeclampsia (n = 29; Fig S1 a). Placentae from individuals with a smoking history (n = 11; Fig S1 b) showed increased cumulative MNPs ( p = 0.0065), PE ( p = 0.0029), and PP ( p = 0.058). In contrast, placentae from participants with gestational diabetes exhibited lower levels of cumulative MNPs ( p = 0.057), N6 ( p = 0.0051), PC ( p = 0.026), PE ( p = 0.028), PVC ( p = 0.032), PMMA ( p = 0.046), and ABS ( p = 0.047). These findings demonstrate that placental MNP concentrations are significantly associated with key maternal comorbidities and environmental factors, highlighting the unlikely probability of their being mere contaminants and emphasizing the need for further investigation into their robust independent association with maternal health. Clustering analysis reveals predictability of patterns in MNP profiles and delivery classifications. To explore patterns in MNP distributions, concentration data were categorized by delivery classifications (term or preterm) and ethnicity and race (Fig. 2 b). Clustering analysis, performed using Euclidean distance and Ward’s D2 method, revealed distinct patterns within and across groups. This approach highlighted co-occurrence trends among MNPs and their association with demographic and clinical variables, providing a basis for further exploration of their roles in pregnancy outcomes. Subjects were grouped into 11 clusters, while MNPs formed 4 distinct clusters. PE and SBR separated into their own clusters, suggesting unique accumulation or exposure pathways. PMMA, N6, PU, ABS, and PC clustered together, indicating potential shared sources or biological interactions, while PET, N6, PP, and PVC formed another distinct group. These clustering results suggest that specific and groups of MNPs may have shared mechanisms of accumulation or exposure and provide hypotheses for investigating their roles in pregnancy outcomes. Gestational age at delivery correlates with specific MNP concentrations. For the comparison of cumulative MNPs, we applied a log transformation (log1p) to stabilize variance and approximate a more normal distribution. Gestational age at delivery showed significant differential correlations with individual MNP levels but no significant correlation with cumulative MNPs, suggesting that potential laboratory environmental contamination is unlikely to contribute to our findings. Specifically, cumulative MNPs did not significantly correlate with gestational age (Fig. 3 a; ρ= -0.11, p = 0.15). However, significant inverse correlations were observed for PU ( ρ= -0.39, p < 0.001), PET ( ρ= -0.37, p < 0.001), and PC ( ρ= -0.16, p = 0.04) (Fig. 3 c-e). In contrast, ABS was the only MNP to positively correlate with gestational age at delivery (Fig. 3 f; ρ = 0.21, p = 0.01). These findings suggest that while cumulative MNPs do not correlate with gestational age, specific MNPs may influence or reflect differences in pregnancy duration. Logistic regression analysis identifies associations between MNP concentrations and PTB. To comprehensively evaluate the PTB risks associated with MNPs, an unadjusted model was compared with an adjusted model accounting for collinearity in MNP exposure (Fig. 2 b) and potentially confounding PTB comorbidities (Table 1 ). In the unadjusted model, SBR, PVC, ABS, PP, and PC were significant predictors (Fig. 4 a; p < 0.05). Model performance comparison demonstrated the adjusted model’s superiority, with a markedly lower AIC (124.31) and residual deviance (82.31) compared to the unadjusted model (Akaike Information Criterion (AIC) = 175.11, residual deviance = 153.11). Incorporating clinical metadata in the adjusted model, significant predictors included PVC, ABS, PP, PC, Hispanic White ethnicity, spontaneous labor, and preeclampsia (Fig. 4 b; p < 0.05). In both models, ABS and SBR had negative β values and odds ratios 6.9, aligning with their positive correlations with PTB risk (Figs. 2 – 3 ). These results underscore the critical importance of integrating clinical metadata to refine risk models and highlight specific MNPs, particularly PVC and PC, as significant and independent predictors of PTB risks, corroborating findings from univariate analyses. Discussion This study demonstrates that placental MNP concentrations are significantly elevated in placentae from preterm deliveries. Cumulative MNP levels were 28% higher in preterm placentae, despite there being a mean of 4.6 weeks less time for accumulation compared to term placentae (term gestation is 37 weeks, representing > 12% less time for bioaccumulation, on average). Specific MNPs such as PVC, PET, PU, and PC showed significant elevations in PTB, while ABS was higher in term placentae. Thus, the specificity and inverse accumulation by gestational time make environmental or laboratory contamination highly unlikely. MNP concentrations correlated with maternal comorbid conditions which are associated with PTB, including elevated PET and PU in preeclamptic placentae, increased cumulative MNPs, PE, and PP in cases with a smoking history, and reduced cumulative MNPs and multiple individual MNPs in gestational diabetes. Clustering analyses revealed distinct patterns of MNP accumulation by delivery classification, highlighting potential shared pathways of exposure or biological interactions. Together, these findings provide initial and robust evidence that the accumulation of specific MNPs in the placenta is associated with adverse pregnancy outcomes, including PTB, warranting further causative investigations. This study aligns with prior studies that reported elevated MNPs in adverse pregnancy outcomes, such as intrauterine fetal growth restriction and recurrent pregnancy loss 32 , 40 and expands on them by showing significantly higher cumulative and specific MNP concentrations in preterm placentae. MNPs such as PET and PU demonstrated strong negative correlations with gestational age, corroborating studies linking MNP exposure to oxidative stress, placental dysfunction, and aberrant vascular and immune pathophysiology. 5 , 44 – 47 The association between MNPs and maternal comorbid conditions, such as preeclampsia and smoking, suggests a complex interaction between environmental exposures and maternal health conditions. Few studies have explored the associations between MNPs and human placental histopathology. One study identified ultrastructural alterations in placentae associated with MNPs utilizing variable pressure scanning electron microscopy and transmission electron microscopy. 33 Another study using Py-GC/MS reported significantly higher PS concentrations in placentae from recurrent pregnancy loss cases compared to controls and detected increased apoptosis in recurrent pregnancy loss placentae using a TUNEL assay. 40 In trophoblast cell cultures, PS nanoparticles reduced Bcl-2 and mitochondrial membrane potential while increasing reactive oxygen species and cleaved caspase-2 and − 3. 40 In a murine model of recurrent pregnancy loss 40 , daily exposure to 50 or 100 mg/kg of PS for 14 days increased fetal demise, while 25 mg/kg/day had no effect. Supplementing Bcl-2 protected against PS-induced trophoblast apoptosis and fetal demise in the mouse model. 40 While MNPs are known to disrupt normal pathophysiology, their influence on placental pathophysiology remains uncharacterized. Rodent models have demonstrated the potential for MNPs to cause pathogenesis during pregnancy. Exposure to MNPs resulted in a range of phenotypes, including metabolic disorders, trophoblast apoptosis, placental dysfunction, intrauterine fetal growth restriction, and fetal demise (stillbirth). 40 , 48 – 51 PS MNPs have been shown to cross the placenta, induce oxidative stress, and disrupt maternal-fetal immune and vascular physiology. 48 , 52 For example, Hu et al. (2021) demonstrated that PS exposure induced fetal demise, reduced decidual NK cells, increased helper T cells, shifted placental macrophage polarization toward anti-inflammatory M2, and led to immunosuppressive cytokine profiles. 48 Notably, some effects of MNP exposure, including transgenerational impacts on metabolic health, extend beyond the F1 generation. 51 Studies using human clinical data and longitudinally collected specimens are essential to uncover how MNPs influence pregnancy outcomes, contribute to the developmental origins of health and disease, and validate animal model findings in the human context. The observed associations between MNPs and PTB, as well as conditions like preeclampsia and smoking, have important clinical implications. Screening for MNPs in placental or maternal blood samples could help identify pregnancies at risk for adverse outcomes. The association between smoking history and elevated MNPs highlights a compounded risk of behavioral and environmental exposures, reinforcing the importance of targeted interventions and public health campaigns aimed at reducing exposure during pregnancy. Furthermore, the inverse correlation between gestational age and specific MNPs, such as PU and PET, suggests that reducing environmental exposure to these plastics may mitigate risks for preterm delivery. MNPs have previously been associated with gestational age, though it is unclear whether PTB cases were included in these studies. An inverse correlation has previously been reported between placental MNPs detected by Raman microspectroscopy ( n = 43 placentae, 13 FGR; 2–38 particles/placenta) and birth weight, length, head circumference, and 1-minute APGAR scores. 32 A significant negative association between MNPs in amniotic fluid ( n = 40 subjects) and gestational age and birth weight was observed by LD-IR. 42 These findings call for the development of guidelines to limit environmental plastic exposure in vulnerable populations, particularly pregnant women. The use of Py-GC/MS for MNP analysis offers a standardized and highly sensitive method for detecting and quantifying MNPs, addressing limitations of earlier techniques and ensuring reliable results. Consistently, MNP concentrations detected by Py-GC/MS in biological specimens from human placentae, brains, kidneys, testes, and livers are at significantly higher concentrations than non-existent or trace levels in operator, environment, and technical blank control samples. 4 , 9 , 14 , 39 , 53 , 54 Additionally, placental MNP accumulation has demonstrably been shown to increase over time 38 , suggesting systematic technical contamination could not be the source of the MNPs detected in the preterm placentae. Lastly, Py-GC/MS is limited in not providing information on the numbers of plastic particles, but the cumulative assessment of all nanoscale polymers is an important advantage, given recent evidence that submicron particles may reflect the major mass balance of MNPs in biological tissues. 4 , 9 , 14 , 39 , 53 – 55 The strengths of this study include its robust nested cohort design, rigorous methodology, and large sample size. However, while the cohort size was sufficient to detect significant associations, it may be underpowered to identify relationships with MNPs at low concentrations or smaller effect sizes. The prospective observational design limits causal inference, and unmeasured confounders, such as dietary habits or regional exposures, may influence findings. Placental tissue, while a practical proxy, may not fully capture dynamic exposure pathways or the timing of MNP accumulation, and the cross-sectional nature of this nested cohort precludes assessment of temporal variability. Differences between univariate and multivariate analyses highlight potential co-linearity among the polymers, which is consistent with recent findings and may reflect environmental MNP concentrations. 4 , 9 , 14 , 39 , 53 , 54 Multivariate models demonstrated larger effect sizes for MNPs such as PVC, N6, and PC, suggesting robust associations after adjustment but also raising questions about shared exposure pathways with clinical factors like preeclampsia and smoking. These findings emphasize the complexity of MNP impacts and the need for statistical approaches to disentangle overlapping effects. While 12 MNP polymer types were assessed, additional environmental exposures could be measured. Additionally, while the cohort's diversity is a strength, findings may not generalize to other populations with differing exposures or healthcare access. Future research should incorporate larger, longitudinal cohorts and complementary analytical techniques to validate these findings and address residual confounding. In conclusion, this study provides compelling evidence of elevated placental MNP concentrations in PTBs, with specific MNPs showing strong associations with maternal comorbid conditions and delivery outcomes. By identifying correlations between MNPs and perinatal outcomes, this study highlights the importance of environmental toxicants in shaping maternal-fetal health. The findings emphasize the need for mechanistic research to elucidate the biological pathways by which specific MNPs influence pregnancy and reproductive outcomes. Clinically, this study supports the development of public health strategies aimed at minimizing environmental exposures in pregnancy to reduce the burden of PTB and associated complications. Methods Inclusion and ethics statement. The authors declare no competing financial or non-financial interests. All authors agree with the manuscript contents, the author list, the author order, and the respective contributions. The composition and procedures of this study’s investigators and subjects align with the Baylor College of Medicine Mission, Vision, and Values, including the Nondiscrimination Policy prohibiting discrimination on the basis of race, ethnicity, age, religion, disability, gender, gender identity, or expression, sexual orientation, nationality, or veteran status. Statement of compliance. The human placentae, collected from 2011 to 2019, were obtained with informed consent and sourced from the Baylor College of Medicine Perinatal Biobank (PeriBank) 24 , 39 , 56 – 76 , governed by protocol H-26364. The subsequent analysis of MNPs in these coded banked samples was also sanctioned by the Baylor College of Medicine IRB (Protocols H-30688, H55700, and H-55735) as participants in PeriBank granted unrestricted permission for future research use. The University of New Mexico Human Research Protections Office also reviewed and approved the study as exempt. Subjects and sample collection. The study population consisted of 159 subjects prospectively enrolled with their placentae and tissue banked for future research. The current study cohort was nested and stratified by gestational age at delivery: Term (≥ 37 weeks; n = 87) and Preterm (< 37 weeks; n = 72) (Table 1 ; relevant clinical metadata, including labor characteristics, are detailed in Table S1 a). Subjects were recruited by PeriBank study personnel at admission to labor and delivery. After obtaining consent, placental samples were collected and rigorously processed, and clinical metadata—including up to 4,700 variables—was extracted from electronic medical records, prenatal records, and directed subject interviews. Data extraction was conducted by trained nurses and research staff, with routine audits by a maternal-fetal medicine physician-scientist (K.M.A.) to ensure quality. Inclusion criteria included 50% preterm and 50% term deliveries, equitable distribution by ethnicity and race (non-Hispanic White, Hispanic White, non-Hispanic Black, and non-Hispanic Asian), an equal fetal sex ratio, cesarean delivery, and live birth. Exclusion criteria included multiparous pregnancies, congenital malformations, fetal anomalies, and cancer. Social deprivation indices (SDI) from 2011–2018 ACS data were incorporated using Zip Codes at the time of delivery. To ensure sufficient statistical power for detecting significant associations between MNP concentrations and PTB, we conducted a power analysis based on previously published data ( n = 3 preterm, n = 57 term).⁴⁰ Significant differences in the concentrations of three MNPs—PVC, PU, and N6—were identified, with effect sizes (Cohen's d ) of 1.44 for PVC, -1.19 for PU, and 1.28 for N6. Using these effect sizes, we calculated the sample sizes required to achieve 80% power at an alpha of 0.05: 9 subjects for PVC, 13 for PU, and 11 for N6. To ensure robustness, we aimed to enroll the maximum required sample size across all three MNPs (26 subjects: 13 term and 13 preterm). The current study exceeds this target with 159 subjects, providing ample power for detecting meaningful associations between MNP concentrations and PTB. As previously described 39 , samples were circumferentially excised 4 cm from the cord insertion site, avoiding maternal decidua to minimize contamination. Dissected sections were placed in sterile polyurethane tubes and immediately flash-frozen at -80°C. Specimen handling adhered to standardized sterile techniques to ensure quality for specimen banking in PeriBank. All specimens used in the current study were primary aliquots stored at -80°C since collection and had not undergone freeze-thawing. Placenta digestion for MNP purification. Placenta samples, each approximately 0.38g, were digested using a 10% potassium hydroxide (KOH) solution in a 3:1 volume ratio as described previously. 39 The digestion was conducted in glass vials, incubated at 40°C with continuous agitation for 72 hours. Post-digestion, the supernatant was transferred to ultracentrifuge tubes, to which 200µl of 100% ethanol was added. Ultracentrifugation at 100,000g for 4 hours separated MNP pellets from the supernatant. These pellets were washed thrice with 100% ethanol and air-dried for 24 hours at room temperature. The dried samples were stored in glass vials for subsequent Py-GC/MS quantitative analysis. Pyrolysis Gas Chromatography/Mass Spectrometry (Py-GC/MS) quantification. Py-GC/MS analyses were conducted using an Agilent 6890 GC/5975 MS system with an EGA/PY-3030D Pyrolysis unit (Frontier Labs, Koriyama, Japan) and a UAMP Column kit for MNP analysis. Placental samples were weighed using an EPE26 Precision Balance (Mettler Toledo), placed in stainless-steel Eco-cup SF sample cups (Frontier Labs, Koriyama, Japan), and subjected to pyrolysis. The pyrolysis parameters, including temperature, hold time, and helium carrier gas flow rate, were controlled via F-Search MPs software v2.1 (Frontier Labs, Koriyama, Japan). Samples were heated to 600°C, with volatile products from polymer degradation captured and analyzed. The total runtime per sample was approximately 45 minutes. Data were normalized to the placental tissue weights. The GC/MS analysis utilized a UAMP column for separating pyrolysis products, targeting twelve specific polymers. MNP quantification was achieved through F-Search MPs 2.1 software (Frontier Labs, Koriyama, Japan). Identification and quantification were based on mass spectra and retention times, using a calibration curve constructed from calcium carbonate MNP polymer standards (Frontier Labs, Koriyama, Japan) at varying weights (0.1mg to 4mg). This calibration facilitated the analysis and quantification of MNPs in biospecimen samples using the software, ensuring precise measurements for each tissue sample examined. Subject MNP concentrations are available in Table S1 a. Extracted Ion Chromatogram Spectrum of the 12 MNP polymers of interest and detailed Py-GC/MS parameters are available in Table S1 b. Statistical analyses. Statistical analyses were performed in R (v4.3.1). Descriptive statistics (mean, standard deviation, etc.) of raw MNP distributions were calculated (Table 2 ). Group and pairwise comparisons were evaluated using Wilcoxon or Kruskal-Wallis tests with Benjamini-Hochberg corrections for multiple comparisons. Categorical variables were assessed using Chi-Square tests. Continuous variables were scaled, log1p normalized, and analyzed using Spearman’s correlation coefficient with Benjamini-Hochberg corrections, with significance set at an adjusted p -value ( q ) < 0.05 (Table S1 c). Multivariable logistic regression was conducted using the glm() function in R with a binomial family to model the binary outcome. Predictor variables included MNP concentrations (unadjusted model) or adjusted by excluding variables with > 85% collinearity and accounting for maternal and fetal characteristics such as preeclampsia, hypertension, labor type, and APGAR scores (Table 1 ; Table S1 d). Multicollinearity was assessed using Variance Inflation Factor (VIF), with a threshold of > 10 indicating potential collinearity. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were derived by exponentiating the model coefficients and their confidence intervals. Statistical significance was defined as p < 0.05. Model fit was assessed using the Akaike Information Criterion (AIC) and residual deviance. Declarations Data and script availability. Raw MNP concentrations and one-hot encoded clinical data are available in Table S1a. Custom scripts have been deposited and are publicly available: https://github.com/MADscientist314/Elevated-Micro-and-Nanoplastics-Detected-in-Preterm-Human-Placentae. Acknowledgments. We would like to acknowledge the tissue donors, J. Chen, and the research coordinators of PeriBank for their collection of placental specimens and data abstraction and entry. This study was supported in part by NIH-NICHD (R01HD091731 to KMA), NSF Postdoctoral Fellowship (#2208903 to ERB), a Loan Repayment Program Award in Pediatrics (NIAID-1L40AI171990-01 to ERB), a pilot from the Maternal and Infant Environmental Health Riskscape award (pilot to ERB, parent award #P50MD015496), and a pilot from the Center for Precision Environmental Health (pilot to ERB, parent award #P30ES030285). NIEHS (R01ES014639 to MJC), Center for Metals in Biology and Medicine P20 (GM130422 to MJC), and a Superfund Research Program P42 (ES027725 to KMA and MAS) The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation. Competing interests The authors declare no competing interests. Author contributions Experimental design (ERB, MDJ, CS, MAS, MAG, MJC, KMA), tissue acquisition (ERB, MAS, LAS, CS, KMA), data curation (MAG, ERB, MDJ, MJC), data analysis (ERB, MAG, MDJ), interpretation (ERB, MAG, MDJ, MJC, KMA), writing the manuscript (ERB, MDJ, MAG, ALH, JCH, MCS, RL, AJN, EEH, JGE, LAS, CS, MAS, MJC, KMA), manuscript revisions (ERB, MDJ, MAG, ALH, JCH, MCS, RL, AJN, EEH, JGE, LAS, CS, MAS, MJC, KMA), and funding (ERB, KMA, MAS, MJC). References Kannan, K. & Vimalkumar, K. A Review of Human Exposure to Microplastics and Insights Into Microplastics as Obesogens. Front Endocrinol (Lausanne) 12 , 724989 (2021). Mortensen, N.P., Fennell, T.R. & Johnson, L.M. Unintended human ingestion of nanoplastics and small microplastics through drinking water, beverages, and food sources. NanoImpact 21 , 100302 (2021). Demirelli, E. , et al. The first reported values of microplastics in prostate. BMC Urol 24 , 106 (2024). Hu, C.J. , et al. 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Unveiling molecular signatures of preeclampsia and gestational diabetes mellitus with multi-omics and innovative cheminformatics visualization tools. Mol Omics 16 , 521-532 (2020). Hill, A.V. , et al. Chlamydia trachomatis Is Associated With Medically Indicated Preterm Birth and Preeclampsia in Young Pregnant Women. Sex Transm Dis 47 , 246-252 (2020). Antony, K.M. , et al. Maternal Metabolic Biomarkers are Associated with Obesity and Excess Gestational Weight Gain. Am J Perinatol 38 , e173-e181 (2021). Burd, J.E. , et al. Blood type and postpartum hemorrhage by mode of delivery: A retrospective cohort study. Eur J Obstet Gynecol Reprod Biol 256 , 348-353 (2021). Whitworth, K.W. , et al. Environmental justice burden and Black-White disparities in spontaneous preterm birth in Harris County, Texas. Front Reprod Health 5 , 1296590 (2023). Sassin, A.M., Sangi-Haghpeykar, H. & Aagaard, K.M. Fetal sex and the development of gestational diabetes mellitus in gravidae with multiple gestation pregnancies. Acta Obstet Gynecol Scand 102 , 1703-1710 (2023). DePaoli Taylor, B. , et al. Sexually transmitted infections and risk of hypertensive disorders of pregnancy. Sci Rep 12 , 13904 (2022). Hooks, S.K. , et al. Evaluating the Impact of Fetal Sex on Gestational Diabetes Mellitus Following Interaction with Maternal Characteristics. Reprod Sci 30 , 1359-1365 (2023). Sassin, A.M., Sangi-Haghpeykar, H. & Aagaard, K.M. Fetal sex and the development of gestational diabetes mellitus in polycystic ovarian syndrome gravidae. Am J Obstet Gynecol MFM 5 , 100897 (2023). Brown, R.E., Noah, A.I., Hill, A.V. & Taylor, B.D. Fetal Sexual Dimorphism and Preeclampsia Among Twin Pregnancies. Hypertension 81 , 614-619 (2024). Barrozo, E.R. , et al. Discrete placental gene expression signatures accompany diabetic disease classifications during pregnancy. Am J Obstet Gynecol (2024). Additional Declarations There is NO Competing Interest. Supplementary Files 04TableS1v5.xlsx Table S1 [Uploaded separately as .xlsx file]. Table S1a: Coded MNP and clinical metadata, related to Tables 1-2 and Figs. 1-4. Table S1b: Raw Extracted Ion Chromatogram Spectrum of the 12 polymers MNPs analyzed and the Pyrolysis-Gas Chromatography/Mass Spectrometry parameters for analysis, related to Table 2 and Figs. 1-4. Table S1c: Spearman’s correlation analysis with Benjamini-Hochberg multiple corrections, related to Fig. 2a. Table S1d: Unadjusted and adjusted regression models for PTB and MNPs. The unadjusted model excludes covariates, while the adjusted model includes maternal, fetal, and labor-related covariates. Columns show coefficients, standard errors, z-values, p -values, significance ( p <0.05, * p <0.01, ** p <0.001), AORs, and 95% CIs. Undefined or infinite CIs are marked as NA., related to Fig. 4. 03NatureMedMNPPTBSupplementalFigurefinal.docx Fig. S1: Specific MNPs were significantly different when stratified by preeclampsia, smoking history, and gestational diabetes, related to Fig. 2. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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(\u003cstrong\u003eb\u003c/strong\u003e) subtypes of MNPs. Individual points are jittered on each violin plot, with preterm denoted in red and term denoted in blue. Statistical comparisons were made using Wilcoxon tests with a significance of \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5903715/v1/88876955a860bc582494ebb4.png"},{"id":75314030,"identity":"f9d39148-a4da-4024-a11d-01ff99220f9d","added_by":"auto","created_at":"2025-02-03 09:27:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":472436,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecific MNPs correlate with birth weight and gestational age, while MNP concentration profiles cluster by preterm delivery status. a, \u003c/strong\u003eCorrelation matrix of cumulative and specific MNP concentrations with birth weight, gestational age, and prior PTB. Spearman’s correlation analysis with Benjamini-Hochberg corrections was used to determine significance, denoted by * for \u003cem\u003eq\u003c/em\u003e\u0026lt;0.05.\u003cstrong\u003eb, \u003c/strong\u003eHeatmap of MNP concentrations stratified by delivery status. Data were log-transformed (log1p) to stabilize variability and approximate a normal distribution. Rows represent MNP measurements, and columns correspond to individual samples. Clustering of rows and columns was performed using Euclidean distance and Ward’s D2 method to highlight patterns within and across groups.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5903715/v1/dd0be7cad915e4c18b9383e7.png"},{"id":75314032,"identity":"cc3042c6-3cbb-45ac-8f74-4f3be609b297","added_by":"auto","created_at":"2025-02-03 09:27:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":305034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGestational age at delivery negatively correlates with PU, PET, and PC levels, positively correlates with ABS levels, and shows no significant correlation with cumulative MNPs. a\u003c/strong\u003e-\u003cstrong\u003ee\u003c/strong\u003e, Scatterplots of gestational age at delivery and:\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) cumulative MNPs, (\u003cstrong\u003eb\u003c/strong\u003e) PU, (\u003cstrong\u003ec\u003c/strong\u003e), PET,\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003ed\u003c/strong\u003e) PC, and (\u003cstrong\u003ee\u003c/strong\u003e) ABS concentrations. Spearman’s correlation ρ and significance defined as \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 visualized with 95% confidence intervals. Individual subject data are depicted with dots colored by gestational age at delivery.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5903715/v1/1fe12c2da6fbfc2cc3be638c.png"},{"id":75314036,"identity":"401d15b9-7680-4fe1-ad54-6cd50e866ee6","added_by":"auto","created_at":"2025-02-03 09:27:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":157126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLogistic regression analysis identifies associations between MNP concentrations and PTB. a-b, \u003c/strong\u003eForest plots showing the unadjusted (\u003cstrong\u003ea\u003c/strong\u003e) or adjusted (\u003cstrong\u003eb\u003c/strong\u003e) odds ratios (OR) and 95% confidence intervals (CIs) for predictors of preterm birth based on placental MNP concentrations. Odds ratios are plotted on a logarithmic scale, with the dashed vertical line representing the null effect (OR = 1). Each point denotes the OR, while horizontal lines indicate the 95% CI.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5903715/v1/9108084bf1feaa423eb18e92.png"},{"id":78007831,"identity":"7f0c0b2e-27cd-46dd-a8f6-a18ac1ffa075","added_by":"auto","created_at":"2025-03-07 19:13:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2904208,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5903715/v1/a2635548-012c-442b-ad75-eeed0feac60e.pdf"},{"id":75314029,"identity":"e5aeca72-e96d-4f6a-a50a-29e33f9ab8c8","added_by":"auto","created_at":"2025-02-03 09:27:41","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":124714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1 [Uploaded separately as .xlsx file]. \u003c/strong\u003eTable S1a: Coded MNP and clinical metadata, related to Tables 1-2 and Figs. 1-4. Table S1b: Raw Extracted Ion Chromatogram Spectrum of the 12 polymers MNPs analyzed and the Pyrolysis-Gas Chromatography/Mass Spectrometry parameters for analysis, related to Table 2 and Figs. 1-4. Table S1c: Spearman’s correlation analysis with Benjamini-Hochberg multiple corrections, related to Fig. 2a. Table S1d: Unadjusted and adjusted regression models for PTB and MNPs. The unadjusted model excludes covariates, while the adjusted model includes maternal, fetal, and labor-related covariates. Columns show coefficients, standard errors, z-values, \u003cem\u003ep\u003c/em\u003e-values, significance (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), AORs, and 95% CIs. Undefined or infinite CIs are marked as NA., related to Fig. 4.\u003c/p\u003e","description":"","filename":"04TableS1v5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5903715/v1/cd1b18caf3c1b329d050e7f5.xlsx"},{"id":75314031,"identity":"c432a7a0-2275-42b0-9628-bbbdf6724b24","added_by":"auto","created_at":"2025-02-03 09:27:41","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":417191,"visible":true,"origin":"","legend":"Fig. S1: Specific MNPs were significantly different when stratified by preeclampsia, smoking history, and gestational diabetes, related to Fig. 2.","description":"","filename":"03NatureMedMNPPTBSupplementalFigurefinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-5903715/v1/c473a91bad272d11ed28630a.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Elevated Micro- and Nanoplastics Detected in Preterm Human Placentae","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMicro- and nanoplastics (MNPs) have emerged as significant environmental contaminants due to their widespread use and persistence in the environment. Humans are estimated to ingest and absorb growing quantities of MNPs through food, water, and air.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e This pervasive exposure raises concerns regarding the potential health impacts of MNPs on vulnerable populations, such as pregnant women and developing fetuses. While MNPs have been detected in human tissues and fluids,\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e their long-term effects, especially during pregnancy, remain poorly understood.\u003c/p\u003e \u003cp\u003e Preterm birth (PTB), a leading cause of maternal and early-life mortality and morbidity\u003csup\u003e \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e \u003c/sup\u003e, is a major global health challenge. Despite multiple decades of research, there are no effective interventions, and the spontaneous PTB rate remains effectively unchanged, affecting approximately 10% of pregnancies worldwide and incurring an estimated \u003cspan\u003e$\u003c/span\u003e25\u0026nbsp;billion in healthcare costs in the United States.\u003csup\u003e \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e \u003c/sup\u003e Despite decades of research, most cases of spontaneous (non-medically indicated) PTB remain idiopathic, with growing evidence implicating both vascular and inflammatory processes as potential drivers.\u003csup\u003e \u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e \u003c/sup\u003e Given their putative causal role in modulating atherosclerotic disease,\u003csup\u003e \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e \u003c/sup\u003e a potential role for MNPs exposure in driving aberrant placental pathophysiology and triggering PTB remains a critical and understudied area of maternal-fetal health research.\u003c/p\u003e \u003cp\u003eEnvironmental toxicants such as air pollution, heavy metals (e.g., lead and cadmium), endocrine-disrupting chemicals (e.g., phthalates and bisphenol A), and polycyclic aromatic hydrocarbons (PAHs) have been implicated in PTB.\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28 CR29 CR30\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Many of these toxicants can accumulate and cross the placenta, disrupting placental function, inducing oxidative stress, and contributing to adverse pregnancy outcomes. Despite these insights, MNPs, which share many characteristics with these toxicants, have yet to be thoroughly investigated for their role in PTB.\u003c/p\u003e \u003cp\u003eIn 2021, MNPs were first identified in human placentae, providing critical evidence that they can reach the maternal-fetal barrier.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Subsequent studies have shown that MNPs are ubiquitous in placental tissues at varying concentrations\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan additionalcitationids=\"CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and have been detected in amniotic fluid, cord blood, and meconium\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. However, there currently are limited studies that have explored the relationship between MNP exposure and health outcomes. Recently, significantly higher concentrations of MNPs were reported in placentae from fetal growth-restricted pregnancies compared to controls, with inverse associations noted between MNP levels and birth weight.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Emerging evidence also implicates MNPs in recurrent pregnancy loss, with a study using pyrolysis gas chromatography-mass spectrometry (Py-GC/MS) reporting significantly higher polystyrene (PS) concentrations in placentae from miscarriage cases compared to controls (odds ratio of 34, 95% CI: 3.61\u0026ndash;320).\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite this growing body of preclinical evidence, significant gaps remain in understanding if and how MNPs contribute to adverse gestational outcomes such as PTB. In this study, we hypothesized that placental MNP concentrations and polymer profiles differ between term and PTBs. Using Py-GC/MS, we quantified 12 types of MNPs in \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;158 human placentae, focusing on their associations with gestational age at delivery, maternal characteristics, and perinatal outcomes. Our findings reveal novel insights into the accelerated bioaccumulation of MNPs in preterm placentae, their correlations with gestational age, and their potential associations with maternal conditions known to exacerbate pathways critical to labor initiation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eClinical and demographic characteristics of the cohort.\u003c/b\u003e The study comprised 158 subjects stratified by gestational age at delivery: Term (\u0026ge;\u0026thinsp;37 weeks; \u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;87) and Preterm (\u0026lt;\u0026thinsp;37 weeks; \u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;71). Nesting of the cohort and study design assured an equal distribution of preterm and term deliveries, equal representation across ethnicity and race (non-Hispanic White, Hispanic White, non-Hispanic Black, and non-Hispanic Asian), an equal fetal sex ratio, cesarean delivery, and live birth. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the clinical and demographic characteristics of the cohort (relevant clinical metadata available in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). Maternal race and ethnicity differed significantly between groups (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), with a higher proportion of Hispanic White participants in the PTB group (70.8%) compared to the Term group (28.7%) and a lower proportion of non-Hispanic Black participants in the PTB group (12.5%) compared to the Term group (20.7%; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.019). As anticipated, hypertension was more prevalent in the PTB group (28.2%) than in the Term group (8.0%; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.002). Similarly, the Social Deprivation Index (SDI) was higher in the PTB group (79.62\u0026thinsp;\u0026plusmn;\u0026thinsp;23.29) compared to the Term group (70.32\u0026thinsp;\u0026plusmn;\u0026thinsp;28.72; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.04). Prior PTBs were more frequent in the PTB group (23.9%) than in the Term group (9.2%; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.021). Similarly and as anticipated, the type of labor also differed significantly (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e), with more spontaneous labor in the PTB group (21.1%) than in the Term group (8.0%; \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.0\u003c/em\u003e33), higher rates of preeclampsia (35.2% vs. 4.6%; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) and PPROM (14.1% vs. 0%; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.001) in the PTB group. Medical co-morbidities accompanying cesarean delivery were significantly different (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), while other medical indications were more common in the Term group (10.3% vs. 1.4%; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.049). As expected, gestational age at delivery (34.72\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11 weeks vs. 39.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39 weeks; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) and birth weight (2499.87\u0026thinsp;\u0026plusmn;\u0026thinsp;674.09 g vs. 3406.91\u0026thinsp;\u0026plusmn;\u0026thinsp;470.70 g; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) were significantly different between preterm and term deliveries, respectively. APGAR scores at 1 minute were lower in PTB deliveries (7.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93) compared to Term deliveries (7.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.04), which is not considered a clinically meaningful difference, despite the statistical difference. Potential confounders of PTB, such as prior PTBs, preeclampsia, and racial health disparities, were controlled for in subsequent univariate and multivariate analyses, ensuring robust comparisons.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eClinical and Demographic Characteristics of the Study Cohort by Delivery Term Classification\u003c/b\u003e. Summary of relevant maternal demographics, clinical characteristics, obstetric outcomes, and neonatal parameters across delivery term classifications: Term (\u0026ge;\u0026thinsp;37 weeks) and Preterm (\u0026lt;\u0026thinsp;37 weeks). Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), with comparisons assessed using the Kruskal-Wallis test. Categorical variables are displayed as counts (n) and percentages (%), with comparisons evaluated using the Chi-Square test. Statistically significant differences are denoted in bold (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Abbreviations: PPROM\u0026thinsp;=\u0026thinsp;Preterm premature rupture of the membranes, SGA\u0026thinsp;=\u0026thinsp;Small for gestational age, LGA\u0026thinsp;=\u0026thinsp;Large for gestational age, APGAR\u0026thinsp;=\u0026thinsp;Scoring for appearance, pulse, grimace, activity, and respiration. \u003csup\u003e#\u003c/sup\u003eNote: All deliveries in this study were Cesarean.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall Cohort (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;158)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreterm Group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTerm Group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal Age, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.71\u0026thinsp;\u0026plusmn;\u0026thinsp;6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.14\u0026thinsp;\u0026plusmn;\u0026thinsp;6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.35\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity/Race:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (70.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (28.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic/Race Not Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Deprivation Index (SDI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.45\u0026thinsp;\u0026plusmn;\u0026thinsp;26.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.62\u0026thinsp;\u0026plusmn;\u0026thinsp;23.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.32\u0026thinsp;\u0026plusmn;\u0026thinsp;28.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal Smoking, never\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147 (93.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (91.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (94.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational Diabetes, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (25.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGravida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior PTB, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (9.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (35.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPROM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of Labor:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Labor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (64.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (49.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpontaneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpontaneous Augmented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndication for Delivery\u003csup\u003e#\u003c/sup\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicated Prelabor Caesarean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlacental Abnormality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFetal Indication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Medically Indicated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfant Gender, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (52.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (57.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational Age, weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.72\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth Weight, grams\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2999.32\u0026thinsp;\u0026plusmn;\u0026thinsp;727.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2499.87\u0026thinsp;\u0026plusmn;\u0026thinsp;674.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3406.91\u0026thinsp;\u0026plusmn;\u0026thinsp;470.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth Weight Percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.84\u0026thinsp;\u0026plusmn;\u0026thinsp;28.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.93\u0026thinsp;\u0026plusmn;\u0026thinsp;25.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.03\u0026thinsp;\u0026plusmn;\u0026thinsp;30.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPGAR at 1 Minute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPGAR at 5 Minutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDespite shorter gestations, PTB placentae have higher concentrations of MNPs than those delivered at term.\u003c/b\u003e Placental specimens were subjected to Py-GC/MS to quantify twelve types of MNPs: polyethylene (PE), styrene-butadiene rubber (SBR), polyvinyl chloride (PVC), polypropylene (PP), nylon 66 (N66), polyethylene terephthalate (PET), nylon 6 (N6), polymethyl methacrylate (PMMA), acrylonitrile butadiene styrene (ABS), polyurethane (PU), polycarbonate (PC), and PS. Samples were run in duplicate and quantified relative to standards (Py-GC/MS parameters, settings, and standards available in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). The cumulative concentrations of MNPs were log1p transformed\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and assessed comparing preterm versus term delivery status, revealing MNP concentrations in PTB placentae were 28% higher (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 224.7\u0026thinsp;\u0026plusmn;\u0026thinsp;180.7 \u0026micro;g/g vs. 175.5\u0026thinsp;\u0026plusmn;\u0026thinsp;137.9 \u0026micro;g/g; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038, Wilcoxon test). Individual MNP concentrations were analyzed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, identifying significantly higher MNP types in PTB placentae, including PVC (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.045; 17% higher), PET (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001; 113% higher), PU (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001; 157% higher), and PC (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.007; 46% higher). Only ABS was higher in term placentae (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.02; 42% higher).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eComparisons of MNP concentrations in placentae across the overall cohort, preterm, and term groups.\u003c/b\u003e Values are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. \u003cem\u003eP\u003c/em\u003e-values were calculated using Wilcoxon tests to compare preterm and term groups, with statistical significance indicated in bold (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003csup\u003e#\u003c/sup\u003eCumulative MNPs were analyzed using log1p-normalized data in the Wilcoxon tests.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicro- and Nanoplastics (MNPs) in \u0026micro;g/g placental tissue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall Cohort (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;158)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreterm Group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTerm Group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolyethylene (PE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e102.6\u0026thinsp;\u0026plusmn;\u0026thinsp;117.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e105.3\u0026thinsp;\u0026plusmn;\u0026thinsp;135.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e88.11\u0026thinsp;\u0026plusmn;\u0026thinsp;96.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStyrene-butadiene rubber (SBR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e33.85\u0026thinsp;\u0026plusmn;\u0026thinsp;29.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e28.49\u0026thinsp;\u0026plusmn;\u0026thinsp;30.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e34.99\u0026thinsp;\u0026plusmn;\u0026thinsp;28.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolyvinyl chloride (PVC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15.18\u0026thinsp;\u0026plusmn;\u0026thinsp;12.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e15.54\u0026thinsp;\u0026plusmn;\u0026thinsp;14.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e13.27\u0026thinsp;\u0026plusmn;\u0026thinsp;10.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolypropylene (PP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13.63\u0026thinsp;\u0026plusmn;\u0026thinsp;12.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.63\u0026thinsp;\u0026plusmn;\u0026thinsp;14.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.78\u0026thinsp;\u0026plusmn;\u0026thinsp;9.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNylon 66 (N66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e10.06\u0026thinsp;\u0026plusmn;\u0026thinsp;8.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.03\u0026thinsp;\u0026plusmn;\u0026thinsp;8.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e10.15\u0026thinsp;\u0026plusmn;\u0026thinsp;8.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolyethylene terephthalate (PET)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.68\u0026thinsp;\u0026plusmn;\u0026thinsp;12.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.49\u0026thinsp;\u0026plusmn;\u0026thinsp;8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNylon 6 (N6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.22\u0026thinsp;\u0026plusmn;\u0026thinsp;15.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.48\u0026thinsp;\u0026plusmn;\u0026thinsp;20.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolymethyl methacrylate (PMMA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcrylonitrile butadiene styrene (ABS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolyurethane (PU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolycarbonate (PC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolystyrene (PS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.534\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.534\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e197.62\u0026thinsp;\u0026plusmn;\u0026thinsp;159.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e224.67\u0026thinsp;\u0026plusmn;\u0026thinsp;180.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e175.54\u0026thinsp;\u0026plusmn;\u0026thinsp;137.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePlacental MNP concentrations are associated with maternal comorbid conditions and environmental factors.\u003c/b\u003e Spearman\u0026rsquo;s correlation analyses were performed to examine associations between placental MNP concentrations and clinical metadata, including maternal comorbidities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec). Cumulative MNPs showed significant positive correlations with PC, PVC, N6, PMMA, PE, N66, ABS, and SBR (\u003cem\u003eρ\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.44\u0026ndash;0.93, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001). Additionally, significant inverse correlations were identified between birth weight with placental PU (\u003cem\u003eρ=\u003c/em\u003e-0.35, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and PC (\u003cem\u003eρ=\u003c/em\u003e-0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) concentrations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWilcoxon tests revealed elevated PET (p\u0026thinsp;=\u0026thinsp;0.031) and PU (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.053) in placentae from subjects with preeclampsia (n\u0026thinsp;=\u0026thinsp;29; Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). Placentae from individuals with a smoking history (n\u0026thinsp;=\u0026thinsp;11; Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb) showed increased cumulative MNPs (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.0065), PE (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.0029), and PP (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.058). In contrast, placentae from participants with gestational diabetes exhibited lower levels of cumulative MNPs (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.057), N6 (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.0051), PC (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.026), PE (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.028), PVC (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.032), PMMA (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.046), and ABS (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.047). These findings demonstrate that placental MNP concentrations are significantly associated with key maternal comorbidities and environmental factors, highlighting the unlikely probability of their being mere contaminants and emphasizing the need for further investigation into their robust independent association with maternal health.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eClustering analysis reveals predictability of patterns in MNP profiles and delivery classifications.\u003c/b\u003e To explore patterns in MNP distributions, concentration data were categorized by delivery classifications (term or preterm) and ethnicity and race (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Clustering analysis, performed using Euclidean distance and Ward\u0026rsquo;s D2 method, revealed distinct patterns within and across groups. This approach highlighted co-occurrence trends among MNPs and their association with demographic and clinical variables, providing a basis for further exploration of their roles in pregnancy outcomes. Subjects were grouped into 11 clusters, while MNPs formed 4 distinct clusters. PE and SBR separated into their own clusters, suggesting unique accumulation or exposure pathways. PMMA, N6, PU, ABS, and PC clustered together, indicating potential shared sources or biological interactions, while PET, N6, PP, and PVC formed another distinct group. These clustering results suggest that specific and groups of MNPs may have shared mechanisms of accumulation or exposure and provide hypotheses for investigating their roles in pregnancy outcomes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGestational age at delivery correlates with specific MNP concentrations.\u003c/b\u003e For the comparison of cumulative MNPs, we applied a log transformation (log1p) to stabilize variance and approximate a more normal distribution. Gestational age at delivery showed significant differential correlations with individual MNP levels but no significant correlation with cumulative MNPs, suggesting that potential laboratory environmental contamination is unlikely to contribute to our findings. Specifically, cumulative MNPs did not significantly correlate with gestational age (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea; \u003cem\u003eρ=\u003c/em\u003e-0.11, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.15). However, significant inverse correlations were observed for PU (\u003cem\u003eρ=\u003c/em\u003e-0.39, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), PET (\u003cem\u003eρ=\u003c/em\u003e-0.37, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), and PC (\u003cem\u003eρ=\u003c/em\u003e-0.16, p\u0026thinsp;=\u0026thinsp;0.04) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-e). In contrast, ABS was the only MNP to positively correlate with gestational age at delivery (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ef; \u003cem\u003eρ\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.21, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.01). These findings suggest that while cumulative MNPs do not correlate with gestational age, specific MNPs may influence or reflect differences in pregnancy duration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLogistic regression analysis identifies associations between MNP concentrations and PTB.\u003c/b\u003e To comprehensively evaluate the PTB risks associated with MNPs, an unadjusted model was compared with an adjusted model accounting for collinearity in MNP exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) and potentially confounding PTB comorbidities (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the unadjusted model, SBR, PVC, ABS, PP, and PC were significant predictors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Model performance comparison demonstrated the adjusted model\u0026rsquo;s superiority, with a markedly lower AIC (124.31) and residual deviance (82.31) compared to the unadjusted model (Akaike Information Criterion (AIC)\u0026thinsp;=\u0026thinsp;175.11, residual deviance\u0026thinsp;=\u0026thinsp;153.11). Incorporating clinical metadata in the adjusted model, significant predictors included PVC, ABS, PP, PC, Hispanic White ethnicity, spontaneous labor, and preeclampsia (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In both models, ABS and SBR had negative β values and odds ratios\u0026thinsp;\u0026lt;\u0026thinsp;0.23, consistent with their inverse correlations with gestational age at delivery (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Conversely, PVC and PC had positive β values and odds ratios\u0026thinsp;\u0026gt;\u0026thinsp;6.9, aligning with their positive correlations with PTB risk (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These results underscore the critical importance of integrating clinical metadata to refine risk models and highlight specific MNPs, particularly PVC and PC, as significant and independent predictors of PTB risks, corroborating findings from univariate analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that placental MNP concentrations are significantly elevated in placentae from preterm deliveries. Cumulative MNP levels were 28% higher in preterm placentae, despite there being a mean of 4.6 weeks less time for accumulation compared to term placentae (term gestation is 37 weeks, representing\u0026thinsp;\u0026gt;\u0026thinsp;12% less time for bioaccumulation, on average). Specific MNPs such as PVC, PET, PU, and PC showed significant elevations in PTB, while ABS was higher in term placentae. Thus, the specificity and inverse accumulation by gestational time make environmental or laboratory contamination highly unlikely. MNP concentrations correlated with maternal comorbid conditions which are associated with PTB, including elevated PET and PU in preeclamptic placentae, increased cumulative MNPs, PE, and PP in cases with a smoking history, and reduced cumulative MNPs and multiple individual MNPs in gestational diabetes. Clustering analyses revealed distinct patterns of MNP accumulation by delivery classification, highlighting potential shared pathways of exposure or biological interactions. Together, these findings provide initial and robust evidence that the accumulation of specific MNPs in the placenta is associated with adverse pregnancy outcomes, including PTB, warranting further causative investigations.\u003c/p\u003e \u003cp\u003eThis study aligns with prior studies that reported elevated MNPs in adverse pregnancy outcomes, such as intrauterine fetal growth restriction and recurrent pregnancy loss\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and expands on them by showing significantly higher cumulative and specific MNP concentrations in preterm placentae. MNPs such as PET and PU demonstrated strong negative correlations with gestational age, corroborating studies linking MNP exposure to oxidative stress, placental dysfunction, and aberrant vascular and immune pathophysiology.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e The association between MNPs and maternal comorbid conditions, such as preeclampsia and smoking, suggests a complex interaction between environmental exposures and maternal health conditions.\u003c/p\u003e \u003cp\u003eFew studies have explored the associations between MNPs and human placental histopathology. One study identified ultrastructural alterations in placentae associated with MNPs utilizing variable pressure scanning electron microscopy and transmission electron microscopy.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Another study using Py-GC/MS reported significantly higher PS concentrations in placentae from recurrent pregnancy loss cases compared to controls and detected increased apoptosis in recurrent pregnancy loss placentae using a TUNEL assay.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e In trophoblast cell cultures, PS nanoparticles reduced Bcl-2 and mitochondrial membrane potential while increasing reactive oxygen species and cleaved caspase-2 and \u0026minus;\u0026thinsp;3.\u003csup\u003e40\u003c/sup\u003e In a murine model of recurrent pregnancy loss\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, daily exposure to 50 or 100 mg/kg of PS for 14 days increased fetal demise, while 25 mg/kg/day had no effect. Supplementing Bcl-2 protected against PS-induced trophoblast apoptosis and fetal demise in the mouse model.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhile MNPs are known to disrupt normal pathophysiology, their influence on placental pathophysiology remains uncharacterized. Rodent models have demonstrated the potential for MNPs to cause pathogenesis during pregnancy. Exposure to MNPs resulted in a range of phenotypes, including metabolic disorders, trophoblast apoptosis, placental dysfunction, intrauterine fetal growth restriction, and fetal demise (stillbirth).\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e PS MNPs have been shown to cross the placenta, induce oxidative stress, and disrupt maternal-fetal immune and vascular physiology.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e For example, Hu et al. (2021) demonstrated that PS exposure induced fetal demise, reduced decidual NK cells, increased helper T cells, shifted placental macrophage polarization toward anti-inflammatory M2, and led to immunosuppressive cytokine profiles.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e Notably, some effects of MNP exposure, including transgenerational impacts on metabolic health, extend beyond the F1 generation.\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e Studies using human clinical data and longitudinally collected specimens are essential to uncover how MNPs influence pregnancy outcomes, contribute to the developmental origins of health and disease, and validate animal model findings in the human context.\u003c/p\u003e \u003cp\u003eThe observed associations between MNPs and PTB, as well as conditions like preeclampsia and smoking, have important clinical implications. Screening for MNPs in placental or maternal blood samples could help identify pregnancies at risk for adverse outcomes. The association between smoking history and elevated MNPs highlights a compounded risk of behavioral and environmental exposures, reinforcing the importance of targeted interventions and public health campaigns aimed at reducing exposure during pregnancy. Furthermore, the inverse correlation between gestational age and specific MNPs, such as PU and PET, suggests that reducing environmental exposure to these plastics may mitigate risks for preterm delivery. MNPs have previously been associated with gestational age, though it is unclear whether PTB cases were included in these studies. An inverse correlation has previously been reported between placental MNPs detected by Raman microspectroscopy (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;43 placentae, 13 FGR; 2\u0026ndash;38 particles/placenta) and birth weight, length, head circumference, and 1-minute APGAR scores.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e A significant negative association between MNPs in amniotic fluid (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;40 subjects) and gestational age and birth weight was observed by LD-IR.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e These findings call for the development of guidelines to limit environmental plastic exposure in vulnerable populations, particularly pregnant women.\u003c/p\u003e \u003cp\u003eThe use of Py-GC/MS for MNP analysis offers a standardized and highly sensitive method for detecting and quantifying MNPs, addressing limitations of earlier techniques and ensuring reliable results. Consistently, MNP concentrations detected by Py-GC/MS in biological specimens from human placentae, brains, kidneys, testes, and livers are at significantly higher concentrations than non-existent or trace levels in operator, environment, and technical blank control samples.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e Additionally, placental MNP accumulation has demonstrably been shown to increase over time\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, suggesting systematic technical contamination could not be the source of the MNPs detected in the preterm placentae. Lastly, Py-GC/MS is limited in not providing information on the numbers of plastic particles, but the cumulative assessment of all nanoscale polymers is an important advantage, given recent evidence that submicron particles may reflect the major mass balance of MNPs in biological tissues.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe strengths of this study include its robust nested cohort design, rigorous methodology, and large sample size. However, while the cohort size was sufficient to detect significant associations, it may be underpowered to identify relationships with MNPs at low concentrations or smaller effect sizes. The prospective observational design limits causal inference, and unmeasured confounders, such as dietary habits or regional exposures, may influence findings. Placental tissue, while a practical proxy, may not fully capture dynamic exposure pathways or the timing of MNP accumulation, and the cross-sectional nature of this nested cohort precludes assessment of temporal variability. Differences between univariate and multivariate analyses highlight potential co-linearity among the polymers, which is consistent with recent findings and may reflect environmental MNP concentrations.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e Multivariate models demonstrated larger effect sizes for MNPs such as PVC, N6, and PC, suggesting robust associations after adjustment but also raising questions about shared exposure pathways with clinical factors like preeclampsia and smoking. These findings emphasize the complexity of MNP impacts and the need for statistical approaches to disentangle overlapping effects. While 12 MNP polymer types were assessed, additional environmental exposures could be measured. Additionally, while the cohort's diversity is a strength, findings may not generalize to other populations with differing exposures or healthcare access. Future research should incorporate larger, longitudinal cohorts and complementary analytical techniques to validate these findings and address residual confounding.\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides compelling evidence of elevated placental MNP concentrations in PTBs, with specific MNPs showing strong associations with maternal comorbid conditions and delivery outcomes. By identifying correlations between MNPs and perinatal outcomes, this study highlights the importance of environmental toxicants in shaping maternal-fetal health. The findings emphasize the need for mechanistic research to elucidate the biological pathways by which specific MNPs influence pregnancy and reproductive outcomes. Clinically, this study supports the development of public health strategies aimed at minimizing environmental exposures in pregnancy to reduce the burden of PTB and associated complications.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eInclusion and ethics statement.\u003c/strong\u003e The authors declare no competing financial or non-financial interests. All authors agree with the manuscript contents, the author list, the author order, and the respective contributions. The composition and procedures of this study\u0026rsquo;s investigators and subjects align with the Baylor College of Medicine Mission, Vision, and Values, including the Nondiscrimination Policy prohibiting discrimination on the basis of race, ethnicity, age, religion, disability, gender, gender identity, or expression, sexual orientation, nationality, or veteran status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of compliance.\u003c/strong\u003e The human placentae, collected from 2011 to 2019, were obtained with informed consent and sourced from the Baylor College of Medicine Perinatal Biobank (PeriBank)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e, governed by protocol H-26364. The subsequent analysis of MNPs in these coded banked samples was also sanctioned by the Baylor College of Medicine IRB (Protocols H-30688, H55700, and H-55735) as participants in PeriBank granted unrestricted permission for future research use. The University of New Mexico Human Research Protections Office also reviewed and approved the study as exempt.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjects and sample collection.\u003c/strong\u003e The study population consisted of 159 subjects prospectively enrolled with their placentae and tissue banked for future research. The current study cohort was nested and stratified by gestational age at delivery: Term (\u0026ge;\u0026thinsp;37 weeks; \u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;87) and Preterm (\u0026lt;\u0026thinsp;37 weeks; \u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;72) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; relevant clinical metadata, including labor characteristics, are detailed in Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003ea). Subjects were recruited by PeriBank study personnel at admission to labor and delivery. After obtaining consent, placental samples were collected and rigorously processed, and clinical metadata\u0026mdash;including up to 4,700 variables\u0026mdash;was extracted from electronic medical records, prenatal records, and directed subject interviews. Data extraction was conducted by trained nurses and research staff, with routine audits by a maternal-fetal medicine physician-scientist (K.M.A.) to ensure quality. Inclusion criteria included 50% preterm and 50% term deliveries, equitable distribution by ethnicity and race (non-Hispanic White, Hispanic White, non-Hispanic Black, and non-Hispanic Asian), an equal fetal sex ratio, cesarean delivery, and live birth. Exclusion criteria included multiparous pregnancies, congenital malformations, fetal anomalies, and cancer. Social deprivation indices (SDI) from 2011\u0026ndash;2018 ACS data were incorporated using Zip Codes at the time of delivery.\u003c/p\u003e\n\u003cp\u003eTo ensure sufficient statistical power for detecting significant associations between MNP concentrations and PTB, we conducted a power analysis based on previously published data (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;3 preterm, \u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;57 term).⁴⁰ Significant differences in the concentrations of three MNPs\u0026mdash;PVC, PU, and N6\u0026mdash;were identified, with effect sizes (Cohen\u0026apos;s \u003cem\u003ed\u003c/em\u003e) of 1.44 for PVC, -1.19 for PU, and 1.28 for N6. Using these effect sizes, we calculated the sample sizes required to achieve 80% power at an alpha of 0.05: 9 subjects for PVC, 13 for PU, and 11 for N6. To ensure robustness, we aimed to enroll the maximum required sample size across all three MNPs (26 subjects: 13 term and 13 preterm). The current study exceeds this target with 159 subjects, providing ample power for detecting meaningful associations between MNP concentrations and PTB.\u003c/p\u003e\n\u003cp\u003eAs previously described\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, samples were circumferentially excised 4 cm from the cord insertion site, avoiding maternal decidua to minimize contamination. Dissected sections were placed in sterile polyurethane tubes and immediately flash-frozen at -80\u0026deg;C. Specimen handling adhered to standardized sterile techniques to ensure quality for specimen banking in PeriBank. All specimens used in the current study were primary aliquots stored at -80\u0026deg;C since collection and had not undergone freeze-thawing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlacenta digestion for MNP purification.\u003c/strong\u003e Placenta samples, each approximately 0.38g, were digested using a 10% potassium hydroxide (KOH) solution in a 3:1 volume ratio as described previously.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e The digestion was conducted in glass vials, incubated at 40\u0026deg;C with continuous agitation for 72 hours. Post-digestion, the supernatant was transferred to ultracentrifuge tubes, to which 200\u0026micro;l of 100% ethanol was added. Ultracentrifugation at 100,000g for 4 hours separated MNP pellets from the supernatant. These pellets were washed thrice with 100% ethanol and air-dried for 24 hours at room temperature. The dried samples were stored in glass vials for subsequent Py-GC/MS quantitative analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePyrolysis Gas Chromatography/Mass Spectrometry (Py-GC/MS) quantification.\u003c/strong\u003e Py-GC/MS analyses were conducted using an Agilent 6890 GC/5975 MS system with an EGA/PY-3030D Pyrolysis unit (Frontier Labs, Koriyama, Japan) and a UAMP Column kit for MNP analysis. Placental samples were weighed using an EPE26 Precision Balance (Mettler Toledo), placed in stainless-steel Eco-cup SF sample cups (Frontier Labs, Koriyama, Japan), and subjected to pyrolysis. The pyrolysis parameters, including temperature, hold time, and helium carrier gas flow rate, were controlled via F-Search MPs software v2.1 (Frontier Labs, Koriyama, Japan). Samples were heated to 600\u0026deg;C, with volatile products from polymer degradation captured and analyzed. The total runtime per sample was approximately 45 minutes. Data were normalized to the placental tissue weights. The GC/MS analysis utilized a UAMP column for separating pyrolysis products, targeting twelve specific polymers. MNP quantification was achieved through F-Search MPs 2.1 software (Frontier Labs, Koriyama, Japan). Identification and quantification were based on mass spectra and retention times, using a calibration curve constructed from calcium carbonate MNP polymer standards (Frontier Labs, Koriyama, Japan) at varying weights (0.1mg to 4mg). This calibration facilitated the analysis and quantification of MNPs in biospecimen samples using the software, ensuring precise measurements for each tissue sample examined. Subject MNP concentrations are available in Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003ea. Extracted Ion Chromatogram Spectrum of the 12 MNP polymers of interest and detailed Py-GC/MS parameters are available in Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eb.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses.\u003c/strong\u003e Statistical analyses were performed in R (v4.3.1). Descriptive statistics (mean, standard deviation, etc.) of raw MNP distributions were calculated (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Group and pairwise comparisons were evaluated using Wilcoxon or Kruskal-Wallis tests with Benjamini-Hochberg corrections for multiple comparisons. Categorical variables were assessed using Chi-Square tests. Continuous variables were scaled, log1p normalized, and analyzed using Spearman\u0026rsquo;s correlation coefficient with Benjamini-Hochberg corrections, with significance set at an adjusted \u003cem\u003ep\u003c/em\u003e-value (\u003cem\u003eq\u003c/em\u003e)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003ec).\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression was conducted using the glm() function in R with a binomial family to model the binary outcome. Predictor variables included MNP concentrations (unadjusted model) or adjusted by excluding variables with \u0026gt;\u0026thinsp;85% collinearity and accounting for maternal and fetal characteristics such as preeclampsia, hypertension, labor type, and APGAR scores (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003ed). Multicollinearity was assessed using Variance Inflation Factor (VIF), with a threshold of \u0026gt;\u0026thinsp;10 indicating potential collinearity. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were derived by exponentiating the model coefficients and their confidence intervals. Statistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Model fit was assessed using the Akaike Information Criterion (AIC) and residual deviance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData and script availability.\u0026nbsp;\u003c/strong\u003eRaw MNP concentrations and one-hot encoded clinical data are available in Table S1a. Custom scripts have been deposited and are publicly available: https://github.com/MADscientist314/Elevated-Micro-and-Nanoplastics-Detected-in-Preterm-Human-Placentae.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments.\u0026nbsp;\u003c/strong\u003eWe would like to acknowledge the tissue donors, J. Chen, and the research coordinators of PeriBank for their collection of placental specimens and data abstraction and entry. This study was supported in part by NIH-NICHD (R01HD091731 to KMA), NSF Postdoctoral Fellowship (#2208903 to ERB), a Loan Repayment Program Award in Pediatrics (NIAID-1L40AI171990-01 to ERB), a pilot from the Maternal and Infant Environmental Health Riskscape award (pilot to ERB, parent award #P50MD015496), and a pilot from the Center for Precision Environmental Health (pilot to ERB, parent award #P30ES030285). NIEHS (R01ES014639 to MJC), Center for Metals in Biology and Medicine P20 (GM130422 to MJC), and a Superfund Research Program P42 (ES027725 to KMA and MAS) The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperimental design (ERB, MDJ, CS, MAS, MAG, MJC, KMA), tissue acquisition (ERB, MAS, LAS, CS, KMA), data curation (MAG, ERB, MDJ, MJC), data analysis (ERB, MAG, MDJ), interpretation (ERB, MAG, MDJ, MJC, KMA), writing the manuscript (ERB, MDJ, MAG, ALH, JCH, MCS, RL, AJN, EEH, JGE, LAS, CS, MAS, MJC, KMA), manuscript revisions (ERB, MDJ, MAG, ALH, JCH, MCS, RL, AJN, EEH, JGE, LAS, CS, MAS, MJC, KMA), and funding (ERB, KMA, MAS, MJC).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKannan, K. \u0026amp; Vimalkumar, K. 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Fetal Sexual Dimorphism and Preeclampsia Among Twin Pregnancies. \u003cem\u003eHypertension\u003c/em\u003e \u003cstrong\u003e81\u003c/strong\u003e, 614-619 (2024).\u003c/li\u003e\n \u003cli\u003eBarrozo, E.R.\u003cem\u003e, et al.\u003c/em\u003e Discrete placental gene expression signatures accompany diabetic disease classifications during pregnancy. \u003cem\u003eAm J Obstet Gynecol\u003c/em\u003e (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"microplastic, preterm birth, obstetrics outcomes, developmental toxicology, environmental pollutants","lastPublishedDoi":"10.21203/rs.3.rs-5903715/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5903715/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Recent analytical advancements have uncovered increasing micro- and nanoplastics (MNPs) in environmental, dietary, and biological domains, raising concerns about their health impacts. Preterm birth (PTB), a leading cause of maternal and neonatal morbidity and mortality, may be influenced by MNP exposure, yet this relationship remains unexplored. This study quantified 12 MNP polymers in placentae from term (n=87) and preterm (n=71) deliveries using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Cumulative MNP concentrations were 28% higher in PTB placentae (mean ±SD: 224.7 ± 180.7 µg/g vs. 175.5 ± 137.9 µg/g; p=0.038). Polyvinyl chloride (PVC), polyethylene terephthalate (PET), polyurethane (PU), and polycarbonate (PC) were significantly elevated in PTB, and PET, PU, and PC inversely correlated with gestational age and birth weight. Logistic regression identified PVC and PC as independent predictors of PTB. These findings suggest total and specific MNPs are associated with PTB, providing actionable insights and emphasizing the importance of minimizing exposure during pregnancy.","manuscriptTitle":"Elevated Micro- and Nanoplastics Detected in Preterm Human Placentae","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-03 09:27:37","doi":"10.21203/rs.3.rs-5903715/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5936a76a-5bba-4263-9a6e-6777483937a7","owner":[],"postedDate":"February 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43483596,"name":"Health sciences/Medical research/Outcomes research"},{"id":43483597,"name":"Biological sciences/Developmental biology"}],"tags":[],"updatedAt":"2025-03-07T19:05:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-03 09:27:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5903715","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5903715","identity":"rs-5903715","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}