N = 95, risk-group with term birth (RG-TB) N = 99, and control group (CG) N = 90). CB-PTSD symptoms and anxiety were measured using standardized psychological questionnaires. Pain intensity, medical support, and childbirth experience were assessed by single items. Delivery modes were subdivided into planned vs. unplanned delivery modes. Group differences were examined using MANOVA. To examine direct and indirect effects on CB-PTSD symptoms, a multi-sample path analysis was performed. Rates of PTS were highest in the RG-PB = 11.58% . MANOVA revealed higher values of CB-PTSD symptoms and pain intensity in RG-PB compared to RG-TB and CG. Women with planned delivery mode reported a more positive birth experience. Path modeling revealed a good model fit. Explained variance was highest in RG-PB (R2 = 44.7%). Direct enhancing effects of trait anxiety and indirect reducing effects of planned delivery mode on CB-PTSD symptoms were observed in all groups. In both risk groups, CB-PTSD symptoms were indirectly reduced via support by medical staff and positive childbirth experience, while trait anxiety indirectly enhanced CB-PTSD symptoms via pain intensity in the CG. Especially in the RG-PB, a positive birth experience serves as protective factor against CB-PTSD symptoms. Therefore, our data highlights the importance of involving patients in the decision process even under stressful birth conditions and the need for psychological support antepartum, mainly in patients with PTB-risk and anxious traits.Childbirth-related post-traumatic stress disorder (CB-PTSD) occurs in 3–7% of all pregnancies and about 35% of women after preterm birth (PTB) meet the criteria for acute stress reaction. Known risk factors are trait anxiety and pain intensity, whereas planned delivery mode, medical support, and positive childbirth experience are protective factors. It has not yet been investigated whether the effects of anxiety and delivery mode are mediated by other factors, and whether a PTB-risk alters these relationships. 284 women were investigated antepartum and six weeks postpartum (risk-group with preterm birth (RG-PB) Trial registration number:NCT01974531 . For expectant parents, childbirth is a joyous but also challenging event. Previous research has already provided evidence that women are more vulnerable to develop mental health problems during the peripartum .In this regard, considerable research attention has been paid to the trauma of birth. Indeed, since 1994, the Diagnostic Statistical Manual (DSM) included childbirth as a potentially traumatic event . “Birth The experience of a real or perceived trauma during labor and birth can be associated with different and more or less long-lasting consequences. The most serious consequence is surely the development of a childbirth-related post-traumatic stress disorder (CB-PTSD) with symptoms of reexperiencing, avoidance, negative cognitions and mood, hyperarousal . The preCB-PTSD symptoms are associated with potentially lasting and negative consequences for the well-being of women, infants and families. Indeed, several studies underlined that CB-PTSD symptoms may affect the parent–infant bond, child’s attachment, self-image and emotions, and social relationships –10. FurtIt is important to underline that postpartum PTSD may be a direct consequence of a birth experienced as traumatic but may also be a continuation of pre-existing PTSD, a reactivation of a PTSD that had previously remitted, or a consequence of an event unrelated to childbirth .Given the confusion in the use of terminology in the literature, this study refers to postpartum PTSD symptoms related to the traumatic experience of childbirth, excluding postpartum PTSD.CB-PTSD is associated with obstetric factors as well as with psychological risk factors. Recent research suggests that risk factors for traumatic birth experience include previous psychopathology, pregnancy-related pathology, perceived lack of support by obstetric caregivers, and several types of negative feelings concerning birth , 3. PrevResearch on protective factors focuses on the quality of support provided by the medical staff, whereby medical support was defined as caregiver support –20. SomeMoreover, few studies have shown that CB-PTSD symptoms are associated with the delivery mode. Unplanned birth procedures seem to enhance the risk of CB-PTSD. Previous studies provided evidence that PTSD-risk was highest in women who had unplanned caesarean section or vaginal instrumental delivery , 25, 26.Previous studies on CB-PTSD in the context of preterm birth did not differentiate between women with imminent PTB who actually had either a premature birth or who actually had a term birth as the final outcome. Our study aims to investigate a multifactorial explanatory model for CB-PTSD symptoms differentiating between these groups.Thus, the main goal of this study was to determine the influencing factors for CB-PTSD symptoms in women with prior risk for PTB and to further differentiate between women at risk  with actual PTB (RG-PB) or actual term birth (RG-TB), and to compare these groups with women of a control group who had a term birth without any prior risk of PTB (CG).Additionally, we investigated direct and indirect effects of the risk factor trait anxiety and the protective factor planned delivery mode on CB-PTSD symptoms via intervening variables  in the three groups.We hypothesized that women with imminent preterm birth are at higher risk for CB-PTSD symptoms than healthy controls and that especially women with actual preterm birth (RG-PB) would be emotionally strained, followed by women of the RG-TB. Additionally, we assumed that high levels of the risk factor trait anxiety may enhance CB-PTSD symptoms directly or indirectly by increasing pain intensity as well as reducing positive birth experience and support by medical staff. On the other hand, planned delivery mode as a protective factor may reduce CB-PTSD symptoms directly or indirectly by increasing positive birth experience and the feeling of being supported by medical staff as well as by reducing pain intensity.As part of a longitudinal research project on development of peripartum mental illness in association with the risk of preterm birth, pregnant women were recruited at a German University Hospital. Women with a prior risk of preterm birth were recruited into the risk group (RG) and those without imminent preterm birth into the control group (CG). Recruitment took place between 2013 and 2018. The RG consisted of women who had to be hospitalized between the 24+0 and 36+6 week of pregnancy with a risk of premature birth. Relevant symptoms were shortening of the cervix, premature labour, premature rupture of the membrane. Women of the RG were contacted by staff of the psychology department after being hospitalized with risk of preterm birth. Risk of preterm birth was assessed by the ward physicians. The CG consisted of women with regular pregnancy in outpatient care. They were approached at registration for birth at our hospital around 36 gestational weeks. The RG was retrospectively subdivided into women with an actual preterm birth (RG-PB) and women with an initial prematurity risk, but term birth (RG-TB). We defined these risk groups because we wanted to investigate whether actual preterm birth or the risk of imminent preterm birth leads to higher psychological distress and lower mental well-being. The effects of imminent preterm birth on the expecting mother’s mental health are only seldom studied so far .N = 446 women participated. However, since only women with complete questionnaires at T1 and T2 were considered for this analysis, sample size was reduced to N = 284 women after taking into account all inclusion and exclusion criteria. A dropout analysis was subsequently calculated. For this purpose, correlations were calculated using Kendal’s tau. Due to the unequal distribution of individuals across categories, the bootstrap approach was used instead of the normal standard error. No significant correlations with the group variable (dropout/non-dropout) were observed for the following variables: previous preterm birth or miscarriage, partnership status and duration, multipara, and in vitro fertilization. Dropouts were slightly younger than the sample , had a slightly lower level of education , and a marginally lower gestational age at recruitment , and at birth . However, it should be emphasized that the significant correlations were consistently very low, with an r < .15, so it can be assumed that the groups are comparable.Women were measured at four-time points. For this publication, only the first two measurement points .State-Trait Anxiety-Depression Inventory  to 4 (very much). The STADI has a 4-factor structure of anxiety and depression including emotionality, worry, dysthymia, and anhedonia.The y (STADI ) is a 40H = .75 for the total scores based on a bifactor model [-value of ≥ 61 (elevated scores) and scores ≥ 32 are equal to T-values of ≥ 70 (extremely high anxiety scores).In the present study we used the trait anxiety scale (STADI-T) consisting of two subscales Emotionality (5 items) and Worry (5 items). Reliability of the scale was sufficiently high in our sample with ω = .86 for Emotionality, ω = .87 for Worry, and ωor model . As the IES-G [Impact of Event Scale (IES-R [IES-G captures the two facets Intrusion (7 items) and Avoidance (8 items) that may also be used as a single sum score for post-traumatic stress symptoms. All 15 items are rated on a 5-point Likert-type scale ranging from 0 (never) to 4 (often). Reliability of the scale was sufficiently high in our sample with ω = .75 for Intrusion, ω = .79 for Avoidance, and ωH = .84 for the total scores based on a bifactor model. High risk for PTS corresponds to IES-G sum scores ≥ 20. It should be noted that the IES-G does not qualify for a diagnosis of PTSD, as it only captures postpartum CB-PTSD symptoms. High risk for PTSD corresponds to IES-G sum scores ≥ 20 [To measure CB-PTSD symptoms six weeks postpartum, a birth-related, adapted version (IES-G ) of the e (IES-R ) was useres ≥ 20 .subjective pain intensity  to 10 (unbearable)) and birth experience  to 10 (absolutely positive)), and support by medical staff was assessed using a seven-point rating scale  to 6 (absolutely)).In addition to these questionnaires, anamnesis was collected at each time point, including sociodemographic data, pregnancy-related aspects, and birth-related characteristics. We used an 11-point single item rating scale to capture As mentioned above, delivery modes were subdivided depending on whether they were planned or not . Spontaneous vaginal delivery as well as elective caesarean sections constituted the planned delivery mode (1), whereas unplanned caesarean sections, emergency caesarean sections, as well as vaginal operative delivery were assigned to the unplanned delivery mode (0). STADI-T was administered, and at T2, IES-G was used. In addition, an anamnesis was obtained at both measurement time points, whereby the variables "delivery mode", "subjective pain intensity", "support by medical staff," and "birth experience" were recorded postpartum at T2.At T1, SPSS version 20 was used to investigate whether the means of the observed variables trauma, pain intensity, birth experience, and support by medical staff differ significantly between the groups RG-PB, RG-TB, and CG, and the type of delivery mode (planned vs. unplanned). Although the assumptions of variance homogeneity and normally distributed variables were violated, MANOVA was nevertheless carried out because Wilks’s lambda is known to be robust against the violation of the normality assumption and equality of covariance matrices if sample sizes are about equal [A two-factorial MANOVA using the statistic program ut equal , 40.2(V) = 0.0625, N = 284, two factors with 3x2 factor levels, and alpha set at .05, power was 99.6% indicating that a small to medium effect size could have been detected with our sample size.In order to check whether power had been sufficiently high for detecting group differences, post-hoc power analyses were performed using the G*power program, version 3.1.9.2 . The resplus program [χ2 test and its associated p-value, the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the standardized root mean square residual (SRMR). Good model fit was indicated by a nonsignificant χ2-value, RMSEA ≤.06, CFI ≥.95, and SRMR ≤.08 [We conducted a multi-sample path analysis in order to examine direct and indirect effects in all groups for the prediction of CB-PTSD symptoms. For parameter estimation, the robust maximum likelihood estimator of the M program  was usedRMR ≤.08 , 44.Mplus program. For analyses in SPSS, missing values were replaced using multiple imputation.The amount of missing data was very small (0.26% at T1 and 0.78% at T2), and simultaneously replaced when estimating model parameters, using the MLR estimator of the IES-G ≥ 20) was 6.7% in the total sample. As expected, the prevalence was highest in the RG with preterm birth (11.58%) compared to RG-TB (7.01%) and CG (1.1%).The percentage of women with high risk for CB-PTSD symptoms  as well as between modes of delivery (planned and unplanned delivery), a two-factorial MANOVA was performed.F = 7.79, p≤.01, and a significant effect of delivery mode on the observed dependent variables, Λ = .82, F = 19.67, p≤.01. The follow-up ANOVAs revealed significant group differences between the groups RG-PB, RG-TB, and CG regarding CB-PTSD symptoms six weeks postpartum  = 3.34, p = .04, ηp2 = .03) and pain intensity  = 15.85, p≤.01, ηp2 = .11).Using Wilks’ lambda, there was a significant overall effect of group on birth experience, pain intensity, and CB-PTSD symptoms six weeks postpartum, Λ = .84, F = 49.27, p≤.01, ηp2 = .18): Women with planned delivery mode reported a more positive birth experience than women with unplanned delivery mode  = .87, p = .52.As expected, CB-PTSD symptoms were highest in RG-PB and lowest in CG . Pain inery mode . There wχ2 = 0.35, df = 2, p = .84, RMSEA = .00, CFI = 1.00, SRMR = .01). Results partially differed between the groups. Overall, the path model accounted for R2 = 44.7% of variance of CB-PTSD symptoms 6 weeks postpartum in the RG-PB, R2 = 28.2% in the RG-TB, and R2 = 41.5% in the CG . An indirect effect of trait anxiety via subjective birth experience was observed in the RG-TB .In all groups, trait anxiety directly increased CB-PTSD symptoms .Delivery mode did not directly influence CB-PTSD symptoms in any group. However, the planned delivery mode indirectly reduced CB-PTSD symptoms via birth experience in each group , while a higher pain intensity directly enhanced CB-PTSD symptoms only in the CG . Furthermore, pain intensity indirectly enhanced PTS via birth experience in the CG . Medical support directly reduced CB-PTSD symptoms in the CG , but not in the risk groups. CB-PTSD symptoms were indirectly reduced by medical support via birth experience in the CG .A positive birth experience directly reduced CB-PTSD symptoms in all groups  or a timely birth (RG-TB) compared to a control group of women without the risk of preterm birth (CG). Group differences revealed that the RG-PB exhibited higher scores in trauma and pain intensity than the other groups. Differentiating between delivery modes (planned vs. unplanned) showed that women with a planned delivery mode reported a more positive birth experience than women with an unplanned delivery mode.Using multi-sample path modelling, direct and indirect longitudinal effects of delivery mode and trait anxiety on CB-PTSD symptoms six weeks postpartum by taking into account obstetric and psychological factors  were investigated. Compared to Gürber et al. , who alsFor all three groups we could show that trait anxiety was a direct risk factor and planned delivery mode an indirect protective factor for CB-PTSD symptoms. However, the three groups differed with regard to the indirect effects of these factors on CB-PTSD symptoms transmitted by other protective factors. For example, in the CG, trait anxiety enhanced CB-PTSD symptoms indirectly through reducing support of medical staff, whereas perceived support additionally reduced PTS directly and indirectly via birth experience. These effects were not found in both risk groups.As our results indicate, higher levels of antepartum trait anxiety are associated with higher CB-PTSD symptoms values six weeks postpartum. These results are in accordance with the previous research , 22, 45.In the RG-TB, the planned delivery mode indirectly reduced CB-PTSD symptoms by enhancing support and positive birth experience. Especially in the RG-PB, positive birth experience is an important protective aspect against CB-PTSD symptoms. This allows to conclude that especially women with imminent preterm birth may reduce their risk of CB-PTSD symptoms if anxiety is treated in time and if the delivery mode matches with the planned mode and if mothers feel supported by medical staff. These results furnish evidence in how far a good medical support by the obstetricians can influence the psychological outcome of women after birth. Although reasons for not being able to fulfil the desired delivery mode of mothers are complex and the process of labouring cannot be controlled to a maximum extend by the becoming mother and also by the obstetricians or midwives, the present results implicate that a good communication regarding women’s intentions of delivery mode before and after birth are essential factors for the mental health of mothers.Another interesting result refers to sufficient pain control during childbirth as being a protective factor only in the CG. These findings are consistent with results from former studies , 16, 20.subjective pain intensity, birth experience, and support by medical staff. While subjective pain intensity is often captured by a single item, the psychometric properties of the other two measures should to be investigated in the future.Some further limitations should also be noted. First, although the use of single-item measures in empirical research should be taken with caution , single-IES-G only allows a risk assessment for CB-PTSD symptoms but not a diagnosis. As an alternative method the Posttraumatic Diagnostic Scale (PDS) [Second, particularly in the RG-TB, there seem to be further influencing factors that have not yet been considered in our analyses. For example, antepartum traumatization has been identified as a risk factor for CB-PTSD symptoms in previous studies. Screening for antepartum traumatization and dissociative symptoms should be included in future research. Furthermore, the le (PDS)  may be uThird, it may be a bias that the first measurement point is heterogeneous in the risk group as we assessed patients 1 to 3 days after arriving at our obstetric department. The “gestational week when recruited” did not differ significantly between RG-PB and RG-TB see . We coulHowever, the present research could demonstrate the importance of investigating complex relationships between protective and risk factors in order to understand how CB-PTSD symptoms may be reduced by clinical interventions while taking the risk of preterm birth into account.Although other protective and risk factors could be investigated in future studies, our results highlight that the presented path model already explains a significant amount of the variability in CB-PTSD symptoms in all groups, with more than 40% explained variance in the RG-PB and in the CG.Our study shows that the risk of preterm birth–independent of the outcome preterm or term birth—is associated with higher rates of CB-PTSD symptoms in women six weeks after birth compared to the control group with term birth. This finding is supported by the result that the path model partially differed between the groups.The observed differences between both risk groups and the control group provide practical implications for medical staff involved in peripartum care, as women of the different groups seem to have partially different needs. The focus of subsequent research should therefore be on the investigation of specific interventions and therapeutic measures for post-traumatic stress symptoms in women with and without preterm birth. Further mediator variables should be analysed, although our path model detected a high amount of variability of CB-PTSD symptoms mainly in the specific risk group with PTB and in the control group. Furthermore, our results indicate that anxious women in particular have a significantly increased risk of developing CB-PTSD symptoms. An early psychological screening appears to be useful in order to make psychological support available to these patients at an early stage. Furthermore, our results indicate that a delivery mode, which is mismatched with the intention, may enhance CB-PTSD symptoms, although only indirectly via support by medical staff. Therefore, our data highlight the importance of stronger communication between staff and patients, aiming to include the patients in the decision process even under unplanned, stressful birth conditions.


The Apo B/A1 ratio is a major factor that predicts future cardiovascular outcomes. However, it is unclear whether the apolipoprotein B (Apo B)/apolipoprotein A1 (Apo A1) is a better predictor of future outcome than the total cholesterol (TC)/HDL-C ratio or lipoprotein (a) (Lp (a)) after the percutaneous coronary intervention (PCI). Therefore, we performed this study to evaluate the impact of the Apo B/A1 ratio on the patients who achieved LDL-C below 70 mg/dL one year after PCI.We included 448 PCI patients whose LDL-C levels were below 70 mg/dL at follow-up. The Apo B/A1 ratio, TC/HDL-C ratio, and Lp (a) levels were measured at the time of PCI and follow-up, and decreases in these parameters between baseline and follow-up were assessed as potential markers to predict major cardiovascular adverse events (MACEs).During a median follow-up period of 38.0 months, 115 MACEs were recorded. The tertile with the lowest decrease in the Apo B/A1 ratio (≤ 0.146) showed a lower MACE survival rate compared to the other tertiles. There were no differences in MACE survival rates for the TC/HDL-C ratio or Lp (a) levels.The Apo B/A1 ratio had better predictive accuracy for clinical outcomes compared to the TC/HDL-C ratio and Lp (a) level. A lower decrease in the Apo B/A1 ratio may be a residual risk factor for MACEs in patients who have reached LDL-C levels below 70 mg/dL after PCI. The 2018 American Heart Association (AHA)/American College of Cardiology (ACC) guideline strongly suggests that low-density lipoprotein cholesterol (LDL-C) should be maintained below 70 mg/dL in patients with clinical atherosclerotic cardiovascular disease by using statins to achieve 50% or greater reduction  = 0.485, p = 0.620, LDL-C decrease: [AUC] = 0.556, p = 0.072) In multivariate analysis using logistic regression, the lowest tertile of LDL-C (≤ 87.6 mg/dL) and use of non-high-intensity statins at the time of PCI were the independent predictors of the lowest tertile for the decrease in the Apo B/A1 ratio  levels may be associated with a poor prognosis after PCI , 15, 16.The results of a meta-analysis demonstrated that the percentage reduction of LDL-C is more important for secondary prevention than for the achievement of a target LDL-C level . EuropeaCho et al.  demonstrIt has been reported that the decrease in the Apo B/A1 ratio varies depending on the type and intensity of statin or addition of ezetimibe. Patients who used high-intensity statins or added ezetimibe had a more reduced Apo B/A1 ratio than those who used non-high-intensity statins , 24. TanOur study had several limitations. First, because of the retrospective nature of this study, there were differences in duration for lipid and clinical follow-up for each patient. However, this study was conducted with patients who had good treatment compliance and arrived at the target LDL-C 1 to 3 year after statin use, and the clinical follow-up duration (median 38.0 months) was long enough to assess the effects of the decrease in the Apo B/A1 ratio. Second, this study was a single-center study and was conducted with a relatively small number of patients. Third, since the type of statins could be changed during clinical follow-up at the discretion of the physician, it was difficult to evaluate the effects of these statin changes on the lipid profiles and future clinical outcomes.The Apo B/A1 ratio had greater predictive accuracy for clinical outcomes compared with the TC/HDL-C ratio and Lp (a) after PCI. A lower decrease in the Apo B/A1 ratio over time can be a residual risk factor of MACE in patients who have reached LDL-C levels below 70 mg/dL after PCI.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


Is elevated inflammation in pregnant women with or without HIV associated with adverse birth outcomes and infant growth deficits?In this cohort study of pregnant women in Pune, India, higher levels during pregnancy of interleukin 17A were associated with increased odds of both preterm birth and low birth weight. Higher levels of interleukin 1β were associated with increased preterm birth and infant growth deficits.This study suggests that elevated inflammation during pregnancy is associated with adverse birth outcomes and infant growth deficits, and future studies should test whether modulating specific inflammatory pathways could reduce adverse birth outcomes and growth deficits. The association of elevated levels of specific inflammatory markers during pregnancy with adverse birth outcomes and infant growth could indicate pathways for potential interventions.To evaluate whether higher levels of certain inflammatory markers during pregnancy are associated with preterm birth (PTB), low birth weight (LBW), and infant growth deficits.In this cohort study of pregnant women with or without HIV, 218 mother-infant pairs were followed up from pregnancy through 12 months post partum from June 27, 2016, to December 9, 2019. Pregnant women aged 18 to 40 years and between 13 and 34 weeks of gestation who were receiving antenatal care were enrolled in a cohort stratified by HIV status; sampling was based on convenience sampling from women receiving antenatal care at Byramjee Jeejeebhoy Government Medical College.Levels of multiple circulating inflammation markers during the third trimester of pregnancy.z scores).The primary study outcome was PTB (<37 weeks’ gestation). Secondary outcomes were LBW (<2500 g) and repeated measures  of infant growth outcomes .The median age of the 218 women at enrollment was 23 years . In multivariable models, higher pregnancy levels of interleukin 17A were associated with increased odds of both PTB  and LBW . Higher levels of interleukin 1β were associated with increased PTB  and infant growth deficits (lower length-for-age This study suggests that increased levels of certain systemic inflammatory markers, including interleukin 1β and interleukin 17A, during pregnancy were associated with adverse birth outcomes and infant growth deficits. Future studies should evaluate whether potential interventions to modulate specific inflammatory pathways during pregnancy could improve birth outcomes and infant growth. This cohort study evaluates whether higher levels of certain inflammatory markers during pregnancy are associated with preterm birth, low birth weight, and infant growth deficits among women with or without HIV in India. Trimester-specific changes have also been observed, with an immunosuppressive and anti-inflammatory profile in midpregnancy to late pregnancy compared with a more proinflammatory profile during labor.5 studies have not examined whether and how maternal inflammation during pregnancy is associated with infant growth outcomes. For instance, it is not known whether higher maternal levels of specific systemic inflammatory markers during pregnancy are associated with infant length and weight measures related to stunting, wasting, and being underweight. Understanding the association of maternal inflammatory responses during pregnancy with birth and infant outcomes could potentially help identify interventions  that could reduce adverse birth outcomes and infant growth deficits.6Data are also lacking on how maternal levels of these inflammatory markers are associated with infant birth weight despite low birth weight (LBW) being another common and clinically significant adverse birth outcome, especially in resource-limited settings. Furthermore, despite data supporting the association of perinatal factors with health outcomes during childhood and adulthood within the developmental origins of health and disease framework,H responses in pregnant women from a cohort study in India. We then assessed the association of these markers with adverse birth outcomes (PTB and LBW) and infant growth .To address these gaps in research, we measured inflammatory markers, informing on aspects of general inflammation and acute phase response, inflammasome activation, gut integrity, monocyte activation, and T7 We enrolled adult pregnant women aged 18 to 40 years and between 13 and 34 weeks of gestation (confirmed by early pregnancy ultrasonography) who were receiving antenatal care at Byramjee Jeejeebhoy Government Medical College, a tertiary care hospital that serves primarily low-income populations and is a referral center for HIV care. The study excluded pregnant women with active tuberculosis or severe anemia at entry, as well as women who were taking antibiotics for more than 14 days, had a history of an autoimmune or immunosuppressive disease, or were taking immunosuppressive medication. As the primary aim of the PRACHITi study was to compare immune responses during pregnancy by HIV status, women were enrolled in a cohort (N = 218) stratified by HIV status (69 HIV positive and 149 HIV negative).8 Sample size was determined based on the PRACHITi study’s goals. Sampling within each stratum was based on convenience sampling of those who met the eligibility criteria.We conducted a longitudinal cohort study of pregnant women (PRACHITi [Pregnancy Associated Changes in Tuberculosis Immunology] study) in Pune, India, from June 27, 2016, to December 9, 2019.STROBE) reporting guideline for observational studies.This study received approval from the institutional review boards of The Johns Hopkins University, Columbia University, Weill Cornell Medicine, and Byramjee Jeejeebhoy Government Medical College. Written informed consent was obtained from all mothers. All guidelines for human experimentation from the US Department of Health and Human Services were followed. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (7 single-plex immunoassays were performed on third-trimester plasma samples according to the manufacturer’s (R&D Systems) directions for soluble CD163, soluble CD14 (sCD14), intestinal fatty acid–binding protein, C-reactive protein, alpha 1-acid glycoprotein, and interferon β. Multiplex immunoassays  measuring interferon γ, IL-1β, IL-6, IL-13, IL-17A, and tumor necrosis factor were also performed on these samples. These immune biomarkers were chosen based on their importance to birth outcomes and HIV.9We collected sociodemographic information and clinical data from study participants at enrollment. The gestational age of the mothers was determined by ultrasonography performed in early pregnancy. Follow-up visits were conducted during the third trimester (for those enrolled in the second trimester), at delivery, 6 weeks post partum, and 3, 6, and 12 months post partum. Gestational age at delivery, along with infant length and weight at each visit, were recorded thereafter. Further details on data collection can be found in the eMethods in the t test. All P values were from 2-sided tests, and results were deemed statistically significant at P < .05.We generated descriptive statistics of sample characteristics for the overall sample as well as by the PTB and LBW status of infants. Preterm birth was defined as birth prior to 37 weeks of gestation, and LBW was defined as birth weight less than 2500 g. The Fisher exact test was used to assess differences in study population characteristics by PTB and LBW for categorical variables, and the Wilcoxon rank sum test was used for continuous variables owing to the violation of normality assumption for the 2 scale to approximate normality. To evaluate the association between each inflammatory marker at the third trimester and birth outcomes , we used logistic regression models with PTB or LBW as separate binary outcome variables. Multivariable model 1 adjusted for maternal age, mid–upper arm circumference ,10 HIV status, parity, smoking, and history of PTB. Multivariable model 2 additionally adjusted for maternal educational level, anemia, and latent tuberculosis infection (LTBI) status.We compared median levels of each inflammatory marker during the third trimester between women with PTB and women with term births using the Wilcoxon rank sum test. All inflammatory markers were transformed to the log11 We used a generalized linear model with an identity-link function to assess the association of inflammation with infant LAZ, WAZ, and WLZ . Multivariable model 1 adjusted for maternal age, MUAC, HIV status, parity, and smoking. Multivariable model 2 additionally adjusted for maternal educational level, anemia, and LTBI. We used a generalized estimating equation method with an exchangeable working correlation matrix and a robust variance estimator to account for the within-individual correlation owing to repeated outcome measures of infant growth at multiple time points of 0 (time of delivery) and 3, 6, and 12 months.To evaluate the association between each inflammatory marker at the third trimester and infant growth, we first computed 3 different variables at each time point for LAZ, WAZ, and WLZ using the World Health Organization child growth standards.We also conducted exploratory analyses, using similar approaches, to assess whether the association of inflammatory markers with birth outcomes and infant growth differed by strata of HIV infection status. Our analyses for this study are focused on hypothesis generation, and therefore we report and interpret effect estimates and 95% CIs. All analyses were conducted in SAS software, version 9.4 (SAS Institute Inc).12 and 52 (24%) reported having an educational level of primary school or less. A total of 62 women (28%) were undernourished, defined as MUAC less than 23 cm10; 193 women (89%) were nonsmokers; and 18 women (8%) had a history of PTB. Based on the stratified design of the parent study, 69 women (32%) had HIV infection. All women with HIV were receiving antiretroviral therapy, and 52 of them (75%) were receiving efavirenz-based regimens. Twenty-five of the women (12%) in this cohort gave birth to PTB infants. The study population characteristics did not differ significantly by PTB status.The median age of the 218 women at enrollment during pregnancy was 23 years  . A total2-transformed levels of third-trimester inflammatory markers by birth status  using the Wilcoxon rank sum test .The median gestational age at which we measured the biomarkers was 29.3 weeks . We compared the median logWe used univariable (N = 218) and multivariable (n = 211) logistic regression models to assess the association between inflammation during pregnancy and PTB. In univariable models, higher levels of IL-1β , IL-6 , and IL-17A  were associated with increased odds of PTB . SimilarP = .05) (eTable 2 in the Data on birth weight and inflammatory markers were available from 213 pregnant women. Thirty percent (n = 64) of the infants in this cohort were born with LBW; the study population characteristics did not differ significantly by LBW status apart from a higher proportion of undernourished women among those who had LBW infants than among those who had normal-weight infants  . When adFollow-up data for the first year of the infant’s life that were needed to calculate LAZ, WAZ, and WLZ were available for 205 infants. We used univariable and multivariable generalized linear models with the generalized estimating equation method to assess the association between inflammation during the third trimester and LAZ during the first year of the infant’s life. Increased levels of maternal IL-1β were associated with a mean decrease in LAZ in univariable  and multivariable  models when adjusting for maternal age, MUAC, HIV status, parity, and smoking (model 1) . SimilarWe used the same generalized linear models with the generalized estimating equation approach to assess the association between inflammatory markers and WAZ during the first year of the infant’s life. Similar to the LAZ results, IL-1β was negatively associated with WAZ , whereas sCD14 was positively associated with WAZ  . FurtherFor WLZ, only maternal C-reactive protein was positively associated with WLZ during the first year of life. This association was observed in the univariable model , multivariable model 1  , and mulAlthough our power was limited to assess whether HIV status was associated with inflammation and birth outcomes, exploratory analyses suggested that IL-1β was associated with PTB in both women with HIV  and women without HIV .8 Thus, based on prior literature that suggests that infants of mothers with HIV have growth deficits at birth, we hypothesized that these results could be partly explained by maternal HIV status. In stratified analysis, in which the positive association of sCD14 with LAZ  and WAZ  was confirmed, this hypothesis was supported and observed only among mothers with HIV.Our results on the association between higher maternal sCD14 levels and higher LAZ and WAZ were surprising. An analysis from this cohort had noted that sCD14 levels were significantly higher among pregnant women with HIV compared with those without HIV.H17 pathway) could be associated with birth outcomes and growth deficits. If these findings are confirmed, future studies should identify and test such an intervention for improved maternal-infant health outcomes.In our maternal-infant cohort study from Pune, India, higher levels of various inflammatory markers during pregnancy were independently associated with adverse birth outcomes and infant growth deficits. For example, higher levels of IL-1β during pregnancy were associated with increased PTB and growth deficits, and IL-17A was positively associated with PTB and LBW. These results suggest a need for future studies to test whether modulating specific inflammatory pathways (eg, those associated with the inflammasome pathway or the T13 Interleukin 1β is a cytokine involved in inducing systemic and local immune responses to pathogens, and it does so by enhancing transcription or messenger RNA stability of other proinflammatory genes. This infection-induced proinflammatory environment is consequently shown to be associated with preterm labor.13 In addition, IL-1β can also increase prostaglandin levels, which could be associated with increased myometrial contractions and eventual preterm labor. These results were also confirmed by our exploratory analyses that showed that higher levels of IL-1β were positively associated with PTB in women with HIV and those without HIV. In addition, maternal IL-1β levels were associated with the infant growth indicators LAZ and WAZ, in which higher levels of this cytokine were associated with less infant growth. Given that there is limited literature on the association with inflammation during pregnancy and infant growth, to our knowledge, this finding with IL-1β is novel and warrants further confirmation. Although inflammatory markers are known to reduce levels of insulinlike growth factor 1,14 which is associated with linear growth, further research into the potential mechanisms is needed.In our study, we observed a positive association of high maternal IL-1β levels with PTB, which is in line with existing literature.H17 and other immune cells, plays a crucial role in the defense against various microbial pathogens. While the reasons for increased PTB with IL-17A are unclear, higher levels of IL-17A associated with excess inflammation can result in tissue damage. In fact, limited data suggest that IL-17A in the feto-maternal interface could be associated with PTB,15 and future studies should evaluate whether and how circulating levels of IL-17A as measured in this study are associated with PTB. Similar associations were also observed for IL-17A and LBW, another novel association observed by our study. Whether this association is being driven by concurrent PTB needs further assessment. Overall, IL-17A levels could have potential prognostic utility for PTB and LBW, and if this association is causal, interventions to target IL-17A could hypothetically reduce adverse birth outcomes.Levels of IL-17A were associated with both PTB and LBW. Interleukin 17A, a cytokine produced by T8 Thus, we conducted a stratified analysis for which our results showed that this association was true only in women with HIV. This finding suggests that infants from mothers with higher sCD14 levels start with low LAZ and WAZ at birth and then “catch up” over time. If future studies confirm these results, it would indicate that, while infants born to HIV-infected mothers with high sCD14 levels are likely to be born with growth deficits, they are able to partly recover these deficits through the first year of life.Our results also showed a positive association between maternal sCD14 levels and higher LAZ or WAZ. As this result was surprising, we hypothesized that this result could be partly explained by maternal HIV status. More specifically, prior analysis from this cohort had noted that pregnant mothers with HIV had higher levels of sCD14.16 and nutritional profile.17This study addresses several gaps in the literature related to maternal inflammation and infant health outcomes. Our results, particularly the associations of maternal inflammation during pregnancy with infant growth outcomes, are novel. Furthermore, this study examines multiple inflammatory markers, providing insights into different inflammatory pathways associated with general inflammation, monocyte activation, gut integrity, and inflammasome activation. As most existing research on some of these inflammatory markers and PTB have been conducted in Western countries, our study provides valuable data from India with its unique immune, metabolic,There are some limitations to this study. The sample size was limited with respect to PTB outcomes and particularly the stratified analyses. Larger studies with formal interaction analyses may be required to highlight how these findings differ by relevant effect modifiers. Our cohort had a high percentage of women with HIV owing to the parent study design, and 28% were undernourished; therefore, our results may not be generalizable to other populations with different characteristics. Additionally, our results associated with HIV largely represent individuals receiving efavirenz-based antiretroviral therapy regimens, and we do not know the generalizability to populations receiving other antiretroviral therapies. Given that we included mothers up to 34 weeks’ gestation, the possibility of selection bias against mothers who delivered before they could be enrolled in the study cannot be discounted. Our findings associated with the secondary outcomes of LBW and growth outcomes were exploratory and will need to be confirmed in future studies. In addition, there may be some unmeasured or unknown confounders that could potentially explain the observed associations. Despite these limitations, our results suggest that specific inflammatory markers during pregnancy are associated with adverse birth outcomes and early infant growth.This cohort study found that increased levels of certain inflammatory markers, particularly IL-1β and IL-17A, during pregnancy were associated with adverse birth outcomes and infant growth deficits. If future studies confirm these associations, the mechanisms by which these biomarkers are associated with these outcomes need further study to evaluate whether potential interventions could improve birth outcomes and infant growth.


Suboptimal maternal health already from preconception onwards is strongly linked to an increased risk of birth complications. To enable identification of women at risk of birth complications, we aimed to develop a prediction model for birth complications using maternal preconception socio-demographic, lifestyle, medical history and early-pregnancy clinical characteristics in a general population.In a population-based prospective cohort study among 8340 women, we obtained information on 33 maternal characteristics at study enrolment in early-pregnancy. These characteristics covered the preconception period and first half of pregnancy (< 21 weeks gestation). Preterm birth was < 37 weeks gestation. Small-for-gestational-age (SGA) and large-for-gestational-age (LGA) at birth were gestational-age-adjusted birthweight in the lowest or highest decile, respectively. Because of their co-occurrence, preterm birth and SGA were combined into a composite outcome.The basic preconception model included easy obtainable maternal characteristics in the preconception period including age, ethnicity, parity, body mass index and smoking. This basic preconception model had an area under the receiver operating characteristics curve (AUC) of 0.63  0.61 to 0.65) and 0.64 (95% CI 0.62 to 0.66) for preterm birth/SGA and LGA, respectively. Further extension to more complex models by adding maternal socio-demographic, lifestyle, medical history and early-pregnancy clinical characteristics led to small, statistically significant improved models. The full model for prediction of preterm birth/SGA had an AUC 0.66 (95% CI 0.64 to 0.67) with a sensitivity of 22% at a 90% specificity. The full model for prediction of LGA had an AUC of 0.67 (95% CI 0.65 to 0.69) with sensitivity of 28% at a 90% specificity. The developed models had a reasonable level of calibration within highly different socio-economic subsets of our population and predictive performance for various secondary maternal, delivery and neonatal complications was better than for primary outcomes.Prediction of birth complications is limited when using maternal preconception and early-pregnancy characteristics, which can easily be obtained in clinical practice. Further improvement of the developed models and subsequent external validation is needed.The online version contains supplementary material available at 10.1186/s12884-022-04497-2. Preterm birth, small-for-gestational-age at birth (SGA) and large-for gestational-age at birth (LGA) are among the most common birth complications, affecting a substantial amount of pregnancies –5. TheseMaternal health and lifestyle during pregnancy are important determinants for fetal development and birth outcomes , 10. AccTherefore, in a population based prospective cohort study, we aimed to develop prediction models for common birth complications based on maternal preconception socio-demographic, lifestyle, medical history characteristics. We assessed whether maternal early-pregnancy clinical characteristics could further improve model performance. Finally, we translated these models into a clinical prediction tool. Additionally, we examined whether paternal characteristics could improve model performance of the developed prediction models within a subgroup of our population and we examined the predictive performance of the developed prediction models on secondary maternal, delivery and neonatal complications.n = 94) or non-live births were excluded (n = 103). After excluding women without information on offspring gestational age or weight at birth (n = 84), we had a population for analyses of 8340 women. Information on 6062 fathers was available.We used data from the Generation R Study, a population-based prospective cohort study from early-pregnancy onwards in Rotterdam, the Netherlands . Inclusi. The result of this literature search is shown in Supplementary Methods SWe selected maternal preconception and early-pregnancy candidate predictors based on their associations with outcomes in literature and on a priori hypotheses. For selection, we focused on candidate predictors which could be obtained in clinical practice already within the preconception period and during early-pregnancy. Candidate predictors were clustered according to availability in clinical practice and timing of assessment within the cohort. Due to the study enrolment of women from early-pregnancy onwards, maternal preconception characteristics were assessed retrospectively using questionnaires. Early-pregnancy characteristics were assessed using hands on measurements in first half of pregnancy . The resAt study enrolment  weeks, we obtained information on maternal age, ethnicity, and parity through questionnaires. Maternal preconception weight and smoking was retrospectively assessed using questionnaires and defined as the weight and smoking just before pregnancy was conceived. Maternal height was measured at study enrolment and pre-pregnancy body mass index (BMI) was calculated.At enrolment, we obtained information on educational level, household income, marital status, current occupational status and planning of pregnancy through questionnaires.Information on maternal alcohol consumption, caffeine consumption and multivitamin and folic acid supplementation before pregnancy was obtained through questionnaires –22. We uData on the presence of preexisting chronic diseases was assessed using questionnaires at study enrolment and included the presence of chronic bronchitis, severe intestinal disorder, epilepsy, heart conditions, high blood pressure, diabetes, HIV/aids or answering ‘yes’ in the questionnaire on the questionnaire: ‘are you being under treatment at a specialist for a chronic disease’, without further specification for which chronic disease. Information on obstetric complications in previous pregnancies , and being a first or second degree relative of the planned biological father was obtained through questionnaires . InformaWe measured maternal systolic and diastolic blood pressure in early-pregnancy  weeks) using the Omron 907 automated digital oscillometric sphygmomanometer  , 27. At Similarly as for the maternal candidate predictors, we selected common paternal characteristics based on a literature search and availability in clinical practice for additional analyses. We obtained information on paternal age, ethnicity, educational level, smoking and alcohol consumption by questionnaire. At enrolment, we measured height, weight and systolic and diastolic blood pressure through one prenatal questionnaire . We calcn = 259).Information about offspring sex, gestational age and weight at birth was obtained from medical records . PretermWe additionally assessed the model performance of the prediction models for the prediction of: 1) maternal complications, including gestational hypertension and preeclampsia; 2) delivery complications, including fetal distress and caesarian section; 3) neonatal complications, including a 5 min Apgar score < 7, low birthweight (< 2500 g) and macrosomia (> 4000 g). Information was obtained from medical records.Linear variables were used continuously, non-linear variables as quintiles and categorical variables were categorized in common clinical categories  we calculated the number of women with a predicted probability of ≥20% for preterm birth/SGA or ≥ 14% for LGA or higher, who would have actually had a preterm birth/SGA and LGA newborn. Cut offs were determined on the highest quartiles of predicted probabilities within the study population and could potentially serve in clinical settings as a definition of women at increased risk who may require intervention; and 3) we examined the performance of our final prediction models for prediction of secondary maternal, delivery and neonatal complications.Finally, we further examined the clinical applicability of our developed prediction models: 1) based on our developed prediction models, we constructed a risk calculator as a screening tool using the shrinked effect estimates and calculated as an example the risks of delivering a preterm born or SGA newborn and LGA newborn for three The statistical analyses were performed using the Statistical Package of Social Sciences version 24.0 for Windows , and R version 3.3.4  (R Core Team 2015).Table For the composite outcome preterm birth/SGA, the basic preconception model had an AUC 0.63  0.61 to 0.65) with a sensitivity of 20% at 90% specificity  with a sensitivity of 22% at a 90% specificity. Maternal characteristics from each cluster improved prediction of the model. Maternal characteristics additionally selected in the model included educational level, household income, daily vegetable consumption, multivitamin supplementation, presence of a chronic disease and maternal early-pregnancy systolic blood pressure, diastolic blood pressure, hemoglobin concentrations, random glucose concentrations, triglycerides concentrations and ferritin concentrations. The full prediction model for LGA had an AUC of 0.67 (95% CI 0.66 to 0.69), with a sensitivity of 26% at 90% specificity, but was significantly improved in comparison to the basic preconception model Table . There iPaternal baseline characteristics are shown in Supplementary Table STo illustrate clinical translation of the developed maternal full prediction models, we calculated the risks of preterm birth/SGA or LGA for examples of three women with different risk profiles ranging from healthy to unhealthy in Fig. The full maternal prediction model for preterm birth/SGA had AUCs ranging from 0.68 (95% CI 0.66 to 0.70) to 0.80 (95% CI 0.78 to 0.83) for the outcomes gestational hypertension, preeclampsia, fetal distress, low Apgar score, low birthweight and macrosomia Table . Model pIn this population-based prospective cohort study, we observed that easy obtainable maternal characteristics that can be applied in the preconception period and early-pregnancy can already predict common birth complications moderately. Addition of detailed maternal preconception and early-pregnancy characteristics led to a significant, though small, improvement of the developed prediction models. These final maternal prediction models developed based on preterm birth, SGA and LGA had a better performance for the prediction of secondary maternal, delivery and neonatal complications than for the prediction of preterm birth, SGA and LGA. Paternal characteristics did not strongly improve prediction of common birth complications, in addition to these maternal characteristics.The major strength of this study is the prospective data collection and information on a large number of maternal characteristics. We considered clinical applicability in the development of the prediction models by using a stepwise model estimation including maternal preconception socio-demographic, lifestyle, medical history characteristics and early-pregnancy clinical characteristics. The response rate at birth was 61%. The non-response at baseline and the relatively low prevalence of birth complications in our sample might have let to selection towards a relatively healthy population. Although we corrected for overfitting in our models to maintain adequate prediction performance in new populations and the models had good calibration in subsets of our population with large in-between differences in socio-demographic characteristics, external validation of the prediction models is needed to assess generalizability to other populations. We obtained high quality data for a large number of maternal characteristics through hands-on measurements, blood withdrawal and questionnaires. Although we used validated questionnaires to assess socio-demographic and lifestyle characteristics, these measurements may still have been affected by measurement error, recall bias and reporting bias. Also, we were only able to create a composite predictor for the presence of chronic diseases and a history of obstetric complications. We had a limited proportion of women with chronic diseases or obstetric complications in medical history available, due to our study sample with a relatively high proportion of nulliparous women. All included chronic diseases and complications in previous pregnancies are associated with either preterm birth, SGA or LGA. We therefore do not consider the use of composite candidate predictors to have affected the overall model performance. To assess the predictive value on birth complications of each chronic disease and history of obstetric complications in previous pregnancies separately, future studies with higher proportions of women with diseases and multiparous women should use further differentiation for these candidate predictors. We only had information on maternal biomarker characteristics available from non-fasting samples in the first half of pregnancy. Previous studies have shown that maternal early-pregnancy biomarkers at least partly reflect maternal biomarkers in the preconception period , 38. HowIncreasing efforts are made to develop integrated strategies to optimize maternal health and lifestyle already from preconception onwards to improve pregnancy outcomes , 39–42. Only a few previous studies developed models for prediction of birth complications , 18, 44.We developed two prediction models for preterm birth or SGA and LGA and translated these models in an online risk calculator as screening tool, which can provide an extensive maternal risk profile already within the preconception period and in early pregnancy. As preterm birth, SGA and LGA are major risk factors for maternal and neonatal mortality and morbidity, it is necessary to consider this full range of birth complications in maternal risk stratification. Model performances for only spontaneous preterm birth as an outcome, were similar to those when also including induced preterm birth. Importantly, we observed a better model performance of the developed prediction models for the detection of secondary maternal, delivery and neonatal complications and of newborns within the more extreme ranges of abnormal birthweight. Our range of birthweight may cover SGA or LGA newborns that are rather constitutionally than pathologically small or large for their gestational age . Birthwe. Previous intervention studies often included women based only on one risk factor such as overweight or smoking [. Further randomized controlled trials are needed to assess whether our models may aid in the early-risk stratification for the development of timely and effective interventions in women already from preconception onwards. After development of effective interventions, optimal screening methods need to be developed to enable screening for women at increased risk of birth complications from preconception onwards on a population level.Risk assessment based on clinically available characteristics in preconception and early-pregnancy remains a major challenge. Additional information throughout pregnancy, such as fetal and placental ultrasound, enable more accurate risk prediction for birth complications , 47. How smoking , 18. How smoking , 50, 51.Maternal characteristics, which can easily be obtained in clinical practice already during the preconception period and in early-pregnancy, can aid in the early prediction of common birth complications within the general population. Further improvement of the developed models and subsequent external validation is needed to enable identification of women from preconception onwards at risk of birth complications in future intervention studies or clinical practice.Additional file 1.


The thermal motionof charged proteins causes randomly fluctuatingelectric fields inside cells. According to the fluctuation–dissipationtheorem, there is an additional friction force associated with suchfluctuations. However, the impact of these fluctuations on the diffusionand dynamics of proteins in the cytoplasm is unclear. Here, we providean order-of-magnitude estimate of this effect by treating electricfield fluctuations within ageneralized Langevin equation model with a time-dependent frictionmemory kernel. We find that electric friction is generally negligiblecompared to solvent friction. However, a significant slowdown of proteindiffusion and dynamics is expected for biomolecules with high netcharges such as intrinsically disordered proteins and RNA. The resultsshow that direct contacts between biomolecules in a cell are not necessarilyrequired to alter their dynamics. I1 Many studies in the past have investigated howthe cellular interior affects the stability and dynamics of proteins.2 Clearly, the excluded volume of numerous intracellularmacromolecules  can substantially impact the behaviorof proteins. In fact, protein concentrations can reach 300 g/L, thusproteins may occupy 30% of the cell volume.3 As a result, translational diffusion slows down and becomes anomalous,4 intrinsically disordered proteins (IDPs) becomemore compact,5 and folded proteins areexpected to be stabilized.6 Whereas excludedvolume crowding has long been the focus of theoretical approaches,7 other interactions between biomolecules, sometimescalled quinary interactions, have received less attention even thoughthey may not be negligible.8The cell is densely filled with proteins, RNAs, and metabolites.10 Electrostatic forces, however,also act on much greater length scales, with consequences for thedynamics of biomolecules. For example, previous simulations founda slowdown in the diffusion of a charged protein in the presence ofcharged cytosolic biomolecules.11 Althoughcharge–charge interactions can cause a nonspecific “sticking”of proteins to other cytosolic compounds, here we are concerned witha more general question: do electric field fluctuations caused bycytosolic macromolecules affect the diffusion of a charged particle?Electrostaticinteractions are particularly relevant owing to theirlong-range nature. Given that most biological macromolecules carrycharges, we would expect nonspecific electrostatic interactions toplay an important role inside cells. In fact, intramolecular charge–chargeinteractions sensitively alter the conformation of intrinsically disorderedproteins (IDPs).12 we assume that electric field fluctuations arewell-approximated by colored Gaussian noise with a finite correlationtime, which is associated with an electric friction memory kernel.Whereas proteins are by far the dominating macromolecules in cells,1 they carry only moderate net charges on average.13 Electric friction effects are therefore unlikelyto arise from protein net charges. Protein dipole moments, on theother hand, are substantial.14 The thermallydriven tumbling and diffusion of the enormous number of cytosolicproteins therefore appear to be a more likely source of electric friction describes the time evolution of the x componentof the particle’s position, ẋ(t) = v(t) = dx(t)/dt is the velocity of the particle, m is its mass, ξs is thesolvent friction coefficient, and fs(t) is a Gaussian-distributed, delta-correlated random forcethat satisfies the fluctuation–dissipation theoremkB is Boltzmann’sconstant, T is the temperature, and δ is theDirac delta function. A well-known consequence of ξs tothe microscopic diffusion coefficient D viafe(t), which wouldhave to be added to the rhs of 17 there is an associated friction force such thatthe resulting equation of motion is of the form of the generalizedLangevin equation12a (GLE)t) satisfies the fluctuation–dissipationtheoremfe cannot,in general, be viewed as delta-correlated, so the GLE is an integro-differentialequation.In the absence of electric friction (to be consideredbelow), the diffusion of a tracer particle in a liquid can be describedby the Langevin equation (LE). Assuming an isotropic and homogeneousmedium, it suffices to consider the LE in one dimensionE can be described by a single characteristic time scale τe, then its autocorrelation function can be approximatedby an exponential function,Q2⟨E2⟩ is the mean-squared electrostatic forcethat acts on a diffusing particle with charge Q.(Note that ⟨E⟩ = 0 for a randomly tumblingdipole.) As a result, the friction memory kernel is also an exponentialfunction,18If the fluctuations of the electric field E2⟩ andthe memory time τe. When the timescale of interest is much longer than τe, however, the effect of electric friction can be characterizedby a single parameter, the electric friction coefficientt) ≈ 2δ(t) ∫0∞ Γ(τ) dτ = 2ξeδ(t), and thus the electric friction force in ξeẋ(t). Hence, the friction coefficient defined in τe. In general, it can beshown that an exponential memory kernel  is the dipole moment vector with magnitude |d| = d, κ is the inverse Debye screening length,and C = 4πϵ0ϵw with the dielectric constantof water ϵw = 80 and the vacuumpermittivity ϵ0. Assuming that the dipole length l is much smaller than the distance r betweenthe dipole and the particle, one findsE = −∇ψ,and its mean-square magnitude ⟨E2⟩is obtained by averaging over all angles θ and positions r, thus leading tor  for the positional distribution must be chosen such that the tracerparticle does not collide with the dipole (a > l/2); in fact, the dipole approximation used to arrive at a ≫ l/2. To understand how the magnitudeof force fluctuations depends on the system size and the dipole concentration,we first consider the case without screening (κ → 0)and then discuss the effects at physiological ionic strengths. Underthe assumption that the cell is much larger than the dipole, we findcp = N/V isthe concentration of dipoles (proteins) in a cell. Notably, the fluctuationmagnitude is independent of the cell size R  and scales linearlywith the concentration of dipoles. Hence, despite the long-range natureof electrostatics, only neighboring dipoles effectively contributeto the force amplitude. In addition, the mean-squared fluctuationamplitude and therefore the electric friction coefficient increasewith the squared charge of the tracer particles (Q2), which may lead to significant contributions for highlycharged particles. The expressions in the presence of salt-screening are given in To estimate the electric frictionmemory kernel, we need the magnitudeof the electric field fluctuations ⟨ect to r IV.B. Herξe) and solvent friction(ξs) for realistic values of cp, d, and a. A previous study computed the dipole moments of 11 981 proteinsfrom the protein database and obtained a mean dipole moment of 639D (median = 452).14 The average size ofa bacterial protein is 267 amino acids,19 which results in a Stokes radius of 2.4 nm using the experimentalscaling laws for folded proteins.20 Thedipole length is therefore l ≈ 4.8 nm, andwe took a = 3l/4. The protein concentrationinside a bacterial cell is ∼6–11 mM (for a randomlytumbling dipole), and we assume a cell volume of 1 fL. Because ξe ∝ τe 3 for the volume of a dipole withη being the viscosity. We assume that the dipoles tumble independentlyand experience only the drag force of the solvent that is definedby the viscosity η (1 mPa s). Given these numbers, we find thatthe relative impact of electric friction decreases drastically withincreasing ionic strengths (Q| < 5e), the electric friction coefficient is several orders ofmagnitude smaller than solvent friction at physiological ionic strengths(100–300 mM) 23 that even exceed those found in cells.With  ξe ∝ τe 8, we alsy kernel 5 to disptrengths 2A. For m–300 mM) 2A. This –300 mM) 2B. Givene 2-fold2C. Impor24 which would suggest that crowding and “sticking” dominateinside cells. We note, however, that our estimate for the volume dependenceof electric friction is valid only under the assumption that the timescale of electric field fluctuations (τe) is invariant under volume changes. Clearly, a dipole ina cell experiences the field of neighboring protein dipoles such thatvolume changes will also affect the average tumbling time.How can electric frictionbe identified experimentally? Clearly,disentangling electric friction from other effects such as excludedvolume crowding or “sticking” is challenging, yet comparedto processes that require direct physical contacts, electric frictionhas an unusual dependence on the cell volume within the limit givenby our assumption of a fixed time scale of electric field fluctuations.Although electric friction does not explicitly depend on the volumeof a cell, altering the cellular volume by applying osmotic stresswill alter the concentration of protein dipoles 13 togethII.B25 Because proteinsfunction in aqueous solutions, frictional forces determine not onlytheir translational diffusion but also their internal dynamics suchas folding. Theories based on Kramers’ model of diffusive barriercrossing26 in the overdamped limit predicta direct proportionality between the folding time τ and viscosity(τ ∝ η),27 which hasindeed been observed for the millisecond folding dynamics of two-stateproteins.28 In contrast, deviations fromthis behavior were found for proteins that fold within microseconds29 or that exhibit significant ruggedness in theirenergy landscapes.27b Two alternative waysof analyzing these experimental data have been used in the past. Eithera friction component resulting from interactions within the proteinhas been invoked, which leads to an additive time scale that is independentof the viscosity of the solvent,29a or if a scalingrelationship between the reaction time and solvent viscosity is assumed,then a weaker viscosity dependence such as τ ∝ ηβ with β < 1 was used todescribe the experimental data.30 Indeed,such fractional viscosity dependencies have been identified for thediffusion of small molecular compounds31 and the internal dynamics of molecules.32 Physical interpretations of such behaviors range from a viscosity-dependentchange in the hydrodynamic coupling between the solvent and the molecule33 and a breakdown of continuum hydrodynamics dueto the finite size of the solvent molecules34 to memory effects caused by solvent relaxation or degrees of freedomdifferent from the probed reaction coordinate.35 Non-Markovian electric friction may also contribute to the complexityof the observed internal dynamics of charged biopolymers.The internal dynamicsof proteins cover many orders of magnitude in time, from the reconfiguration timescales in unfolded proteins to those of protein folding and largemolecular assemblies.36 and other biopolymers.37 Their success is in part due to the fact that they arecoarse-grained models capturing specific intermonomer interactionsvia a few adjustable parameters. Here, for simplicity, we focus onthe Rouse model, which ignores hydrodynamic interactions. The usualcoarse-graining monomeric unit of the Rouse chain is the Kuhn segmentchosen to be sufficiently large such that adjacent Kuhn segments arestatistically independent. In the present case, however, it is essentialto consider another length scale lC, whichis the length over which the spatial correlations of the electricfield acting on the polymer decay. If lC is comparable to or smaller than the size of the Kuhn segment, thenthe electric forces acting on each Kuhn segment are statisticallyindependent. Is this a reasonable assumption?Thusmotivated, we seek to understand the effect of electric friction onthe internal dynamics of charged biopolymers. To this end, we considerthe dynamics of the simplest polymer model, a Rouse chain. Despitetheir simplicity, Rouse/Zimm-type models often offer a quantitativedescription of reconfiguration dynamics of intrinsically disorderedproteins38 between twodipoles as an upper limit for lC, we find3–3.4 nm at cellular concentrations (6–11mM)  and also larger than the Kuhn length (0.55 nm)of polypeptide chains).39 Hence, neighboringamino acids will experience the same electrostatic force.When taking thenearest-neighbor distancelC exceeds the size of the Kuhnsegment but is still much smaller than the radius of gyration of theentire chain, Rg, then it is expedientto further coarse grain the chain to de Gennes “blobs”of size lC. Because of the self-similarityof the Rouse model, such coarse graining does not affect the model’sdynamic properties , yet now the electricforces acting on each blob may be considered statistically independent.This case is discussed in detail below. If, however, lC is comparable to or greater than Rg, then electric forces on all monomers of the chain are correlated.It is then instructive to consider the limit in which all monomersfeel the same electric field. Interestingly, unless the chain is chargeduniformly, interaction with the electric field also leads to friction.This case is discussed at the end of this section and in When lC ≪ Rg and assume thatthe problem is adequatelycourse-grained such that the electric forces on each monomeric unitare statistically independent of one another. The chain is then treatedas a Rouse chain ).40 A Rouse modelwith explicit noncovalent contacts between beads has been developedby Barsegov, Morrison, and Thirumalai41 but since we mainly focus on interactions of the beads with externalelectric fields, we implicitly account for electrostatic interactionsbetween chain beads by the spring constant of the interbead interaction(which may depend on the ionic strength). In addition to the solventfriction (and the associated delta-correlated random force), eachbead also experiences an electric friction force along with the stochasticforce caused by the fluctuations in the electric field. These forcesdepend on the charges of each bead, and in general, a numerical solutionof the problem can be obtained for any given value of such charges.To ensure that the problem is tractable analytically, however, herewe will consider the case in which all of the monomer charges arethe same.Consider the case x coordinateofthe kth bead is thus given by the GLE  is given by42 we define this reconfiguration time by the correlation function Ck(t) of thevector that connects a bead at position 0 with another bead at position kk) to modes (q) using Fouriertransform , q = 2π{0, 1,2, . . .}/n for a periodic chain. The solution isobtained after reducing 1 and τ2) that are given by the two eigenvalues (ω1 and ω2) of eq M = ξs + ξe + 2γτe(1 –cos q) and L = 8ξsτeγ(1 – cos q). Using z.The mode-specific correlation function in τe is the tumbling time of the dipoles as defined in section A andβ = 1 + ξe/ξs. Using q) ≈ q2/2. In the limit of thevanishing electric friction , we find τq = ξs/γq2, which is the relaxation time scale spectrum of theclassical Rouse model without electric friction. Notably, the additionalfriction component adds two contributions to the standard Rouse moderelaxation times. The first component is mode-dependent  such that the second term in We are interested in computing the average reconfiguration timeof the chain. Following previous approaches,2) dominates the correlation function, thus causinga nearly exponential decay on the diffusive dynamics of biopolymers. We described the complexcytosolic mixture of crowding proteins as a collection of diffusingand tumbling dipoles that cause a fluctuating electric field insidethe cell. We obtained an analytical estimate of the magnitude of suchfield fluctuations and how these fluctuations impact the diffusionof a charged particle and the dynamics of a charged Rouse chain. Tokeep the problem analytically tractable, we treated electric fieldfluctuations as colored noise with a single characteristic time scaleand an exponential memory kernel. The time scale is determined bythe tumbling time of the dipoles. We show that the resulting frictioncoefficient is independent of the system size and proportional tothe concentration of dipoles . The friction coefficient also increases with the squarednet charge of a diffusing particle, which causes a prominent slowdownin diffusion for highly charged particles. Similar effects were alsofound when investigating the behavior of a chain of charged beadsin such a field. We find a significant effect of electric frictiononly if the net charge of the chain monomers exceeds an absolute chargeof 10e.11 Our results suggest thateven with the conservative estimate made in this work, electric frictionin cells is on the verge of impacting the diffusion of highly chargedparticles and polymers without direct physical contacts.Our numerical results provide only a crude estimateof electricfriction effects. In reality, electrostatic fluctuations occur atmultiple time scales and are caused not only by tumbling protein dipolesbut also by other charged macromolecules such as RNA, charge regulationprocesses such as protonation and deprotonation of ionizable groups,and the translational diffusion of all charged species in the cytosol.However, our main goal was to provide an analytical framework to estimateelectric friction. Our approach of describing the effect of electricfield fluctuations on the diffusion of charged particles using GLEsis sufficiently flexible to account for additional noise sources.However, precise values for the strength of these fluctuations inrealistic compositions of the cytosol are better obtained from molecularsimulations.


The absolute preterm difference in psychological IPV compared to normal was 0.08 . The probability of preterm birth was 8% higher on average in women with psychological IPV during pregnancy. As our analysis controlled for selection bias, our findings give credence to a causal inference. Screening and management for psychological IPV during pregnancy is an important step in antenatal care to prevent preterm birth.Psychological intimate partner violence (IPV), a global public health problem, affects mothers during pregnancy. We evaluated its relationship with preterm birth. We established a cohort of 779 consecutive mothers receiving antenatal care and giving birth in 15 public hospitals in Spain. Trained midwives collected IPV data using the Index of Spouse Abuse validated in the Spanish language. Preterm was defined as birth before 37 completed weeks of gestation. Gestational age was estimated by early ultrasound. With multivariate logistic regression we estimated the relative association of IPV with preterm birth as adjusted odds ratios (AOR), with 95% confidence intervals (CI). In propensity score analysis, using weighting by inverse probability of exposure to IPV, the whole sample was used for estimating the absolute difference in probability of preterm amongst offspring born to mothers with and without IPV. Socio-demographic and other pregnancy characteristics served as covariates in both analyses. Preterm occurred in 57 (7.3%) pregnancies. Psychological IPV, experienced by 151 (21%) mothers, was associated with preterm birth (11.9% vs 6.5%; AOR = 2.4; 95% CI = 1.1–5.0;  Globally, the lifetime prevalence of IPV against women perpetrated by a male partner is around 30%3. In Europe, 32% of women reported experiencing psychological IPV and 43% reported psychological IPV including other forms of violence as controlling behavior, economic violence and blackmail with children4.Violence against women including intimate partner violence (IPV) is one of the most pervasive human rights abuses worldwide. IPV refers to behaviour within an intimate relationship that causes physical, sexual or psychological harm, including acts of physical aggression, sexual coercion, psychological abuse and controlling behaviours. This definition covers violence by both current and former spouses and partners5. During pregnancy most women are in contact with the health care system making antenatal care a window of opportunity for identifying IPV. Violence during pregnancy is higher than many common obstetric conditions6 differing across countries and cultural settings9. In a European wide-survey, women who were pregnant during the relationship with their partner and who experienced violence in the relationship, 20% of the victims of current partner violence and 42% of victims of previous partner violence say that physical or sexual violence also took place during pregnancy4. A recent Spanish study documented that psychological IPV in pregnancy was reported by 21.0% of the women by using validated screening tools10. The awarness of this problem is high in Spain as political agreement to reduce gender violence (2017) was obtained with unanimity among all the political parties and supported by consistent provision of an annual budget. The Spanish agreement emphasizes that doctors, midwives and other allied healthcare professionals must act as active screeners, and that this should happen within a system where early detection is followed by proper multidisciplinary input11.The consequences of IPV are magnified when the victim of violence is pregnant because of additional health risks to both the woman and her unborn child12 with devastating effects on the child's health13. IPV during pregnancy might contribute to preterm birth through an association with obstetric complications, e.g. preeclampsia, gestational diabetes19. Physical IPV during pregnancy has been most linked to preterm birth24. In a recent meta-analysis study, the psychological IPV was assessed in only 2 of the 30 articles14. However, psychological violence is also an important form of IPV but inadequate standardization of its measurement might difficult to quantify the health effects of this type of violence16. The deficiency of valid and reliable evaluations of IPV16 with frequent studies aiming exclusively on physical IPV25 have the result that the matter of psychological IPV during pregnancy mainly unnoticed.A worldwide estimation of 12.9 million children are born before 37 gestational weeks, which implies that 9.6% of all children being born preterm19. Women who are victims of psychological IPV during pregnancy have been found to have more severe family/social problems and higher rates of psychiatric problems and comorbidity15. Consequently, psychological IPV may be a risk factor for negative pregnancy health experiences and behaviors including substance use and inadequate weight gain and prenatal care utilization, suggesting pathways by which psychological IPV may impact pregnancy and birth outcomes17. Continuous and improved investigation has been requested, especially because the harmful effects of non-physical IPV are underestimated27.It is increasingly being recognised that psychological victimization during pregnancy contributes to poorer overall health and temperament of the child. Psychological IPV during pregnancy have been linked to psychological outcomes such as high levels of stress, anxiety, and posttraumatic stress disorder21 of the link between psychological IPV and preterm birth are vulnerable to selection bias, a situation where certain characteristics related to the likelihood of exposure, e.g. socioeconomic status, can lead to an inaccurate estimate of the association, making a causal inference impossible. A thorough search of the literature showed that propensity score analysis has not been used to evaluate the relationship, reducing the risk of bias in the estimation of the association.Observational studiesConsidering the public health importance of IPV and preterm birth as important risk factors for maternal and infant morbidity, we examined if the experience of psychological IPV, perpetrated by current or former male partners, and captured with validated tools in pregnancy may be associated with preterm birth in a propensity score analysis to allow for evaluation of a causal inference.8, provided an accuracy of ± 2.5% with 99% confidence for IPV detection, assuming an IPV prevalence of 7.5%16 and an intraclass correlation coefficient of 5%28. Participants were women who received routine antenatal care giving birth in the hospital. Exclusion criteria were women with miscarriages, inability to converse in the Spanish language, and the presence of cognitive disease preventing collection. The study protocol was approved by the research ethics committees of all participating hospitals  in accordance with the “Ethical Principles for Medical Research Involving Human Subjects” adopted in the Declaration of Helsinki by the World Medical Association . All participants provided written informed consent prior to enrolment.A population-based study was designed for all public hospitals (n = 28) in Andalusia, Spain . A cluster sampling approach was adopted to select 15 hospitals to represent service type . A sample of 750 women, consecutively enrolling 50 women per hospitalData were gathered during the immediate postpartum period by midwives at each hospital who were given specific training for participation in the study. Women were enrolled on successive days until the sample size per hospital was achieved (n = 50), preventing any day without sampling. Data were gathered in a room other than the ward in which the woman was hospitalized, where the study objective was explained, with guarantee that partner was not present. The strict anonymity and confidentiality of the information collected was guaranteed. Women participating signed informed consent. If the questionnaire responses evidenced IPV, the women were given comprehensive information on the police, judicial, and social resources available.Preterm was defined as birth before 37 completed weeks of gestation. Gestational age was estimated by early ultrasound.2 during 12 months before giving birth. It was identified in the immediate postpartum period using Index of Spouse Abuse (ISA), a 30-item self-report instrument measuring the severity and frequency of abuse by weighted items (Supplementary Method)29. ISA (ranging from 0 to 100 points) measured two different types of violence: an ISA-P score that represents the severity of physical violence and an ISA-NP score that represents the severity of nonphysical or psychological violence. The higher scores reflect more severe IPV. Recommended cut-off scores were 10 for physical violence and 25 for psychological violence as at these thresholds the sum of false positives and false negatives was minimized29. The instrument was validated for application in Spanish30.IPV was defined as physical, sexual, coercion or psychological violence, and controlling behaviours perpetrated by a current or past male partner10.Data collected were age, nationality, schooling history, employment, relationship status, number of children, cohabitation with partner/family, and the availability of next of kin support (i.e. a relative who could be turned to when needed). Relationship status was coded as “married”, “committed relationship” but not married and “non-committed relationship” considered between individuals who may have casual sex without demanding or expecting the commitment of a formal relationship31.The women were asked: “At the time you became pregnant, did you want to become pregnant then, did you want to wait until later, did you want no (more) children, or did you not mind either way?” A pregnancy was considered unintended if the respondent stated that at the time, she became pregnant she would have liked to have waited until later to become pregnant (mistimed pregnancy) or that she did not want any (more) children (unwanted pregnancy)Data was extracted from the prospectively documented individual health records during the pregnancy. Outcomes collected were: anaemia (< 10.5 g/dL), vaginal bleeding (threatened abortion and antepartum haemorrhage), stillbirth, urinary tract infection, vaginal infections , gestational diabetes (confirmed by glucose tolerance test at 24–28 weeks), gestational hypertension (> 140/90 mmHg), spontaneous preterm labour, low birth weight, others  and smoking in pregnancy.32. Weighting by inverse probability of exposure to IPV was applied to the whole sample. The Average Treatment Effect (ATE) of psychological IPV vs no IPV on preterm birth was estimated for the entire sample. The covariates in both analyses  were socio-demographic characteristics , desired pregnancy, number of children different from the actual birth, stillbirth, obstetric pathologies (any pathology during pregnancy except anemia or infections), infections during pregnancy and smoking in pregnancy. The covariates were selected based on previous results predicting IPV and on preterm birth theory8. The numbers of cases of physical IPV were too small for a reliable multivariate analysis.Multiple logistic regression analysis determined the relative association between IPV and preterm birth. The results were summarized as crude (COR) and adjusted odds ratios (AORs) with 95% confidence intervals (CIs). The absolute difference in probability of preterm amongst those with and without IPV was estimated using propensity score analysisThe flow diagram of participants in this dataset shown in Fig. p = 0.02). The odds of preterm birth were higher in women with obstetric complications  and those with a previous stillbirth . In the adjusted analysis, the relative association of exposure to psychological IPV, compared to normal, with preterm birth remained significant  (Table p = 0.04), using propensity score analysis  in the local language and midwives trained for data collection. Continuous ISA scores could be used in the logistic regression as an alternative approach in future research. The use of ultrasound scanning for gestational age determination was another strength of the study. This study presented data with a high response rate (> 90%). Despite the small figures, rejection to fill out the IPV instruments should always be considered methodological issues. Nevertheless, the low loss of ISA data (< 5%) should support the minimal influence on the validity of our results8. Further strength is that socio-demographic characteristics had no effect on preterm birth in the adjusted multivariate model. Perinatal outcomes, such as obstetric pathologies and a previous stillbirth were associated to preterm birth.The strength of our investigation is based on the propensity score analysis that was used for the first time to draw causal inference between psychological IPV during pregnancy and preterm birth. The individual sampling weight contributes to the analysis using observed covariates in the whole sample27, that may be considered as one limitation of the study, since women tend to feel particularly vulnerable and may have induced an underreporting of the violence33. Another limitation is that the findings of this study may not generalize to non-Spanish populations, particularly to populations of pregnant women in countries with differing access to healthcare and/or quality of healthcare.Although IPV has been also assessed in postpartum period21. However, maturity in a multi-country study, including 184 countries, showed that 11.1% of all deliveries were preterm13. The reported differences may reflect that pregnant women are exposed to different living conditions and also how accurately gestational age is determined34.We found the prevalence of preterm 7.3% (< 37 weeks), the small numbers of extreme (< 28 weeks) and early (< 32 weeks) premature births did not allow us to draw any conclusion about preterm subgroups. Similar prevalence rates have been reported confirmed also by ultrasound scan27 that is well established leading causes of neonatal morbidity and mortality13. However, it is important to note that several studies have not found a significant relationship between IPV and delivering preterm36. The lack of associations between IPV during pregnancy and preterm birth may be attributable to the small size of the sample studies. Similarly, generalizations are difficult to state among studies finding positive associations owing to different populations sampled, assessments, methods, and data analysis17.Women experiencing IPV during pregnancy were at increased risk for preterm birth 37. Indeed, psychological stress may induce pregnancy complications including such as preeclampsia or preterm labor or may aggravate preexisting conditions such as hypertension and gestational diabetes37. Psychosocial stress may also reflect in unhealthy behaviours during pregnancy such as smoking, or alcohol and drug consume, or may affect the adequate use of antenatal care services38. Regarding to the unhealthy behaviours, we found that smoking during pregnancy was not significantly associated to preterm birth. Besides, the stress of experiencing IPV during pregnancy may increase hypothalamic–pituitary–adrenal (HPA) activity. Higher levels of HPA hormones, including corticotrophin-releasing hormone (CRH), could initiate labor as well as restrict utero-placental perfusion17. Endothelial dysfunction and inflammatory cytokines all seem to be implicated in the pathogenesis of placental insufficiency, abruptio placentae and preterm birth16. Additional studies are needed to disentangle the independent and joint effect of IPV exposure and these risk factors on preterm risk.The association between psychological IPV during pregnancy and preterm birth that we documented was adjusted for other known obstetric pathologies or a previous stillbirth. The causal inference, in addition to our inverse probability weighting analysis, is strengthened by several biological mechanisms. Psychological IPV during pregnancy may increase the risk of preterm birth through psychosocial or physical stress, depression, anxiety, isolation, decreased social support, and low self-esteemBased on our findings, experiencing psychological IPV during pregnancy has a significant positive effect on premature birth. Thus, screening for non-physical IPV during pregnancy is an important step. There is a need for strengthening health care provision by involving other stakeholders who can support victims of psychological IPV by securing social and psychological support. Antenatal care represents an important opportunity to engage in preterm birth prevention through psychological IPV identification and management.


This cohort study evaluates rates of preterm birth and stillbirth in Ontario, Canada, during the first 6 months of the COVID-19 pandemic. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting in epidemiology guideline.5We performed a population-based cohort study in Ontario, Canada, using linked databases at ICES .In-hospital births at 20 weeks’ or more gestational age (GA) from March 15 to September 30, 2020 (pandemic group), were compared with corresponding calendar periods from 2015 to 2019   and stillbirths (intrauterine death at ≥20 weeks’ GA) were the primary outcomes. Secondary outcomes were extreme PTB (<28 weeks’ GA), very PTB (<32 weeks’ GA), severe small for GA (birth weight less than the fifth percentile for sex and GA), neonatal intensive care unit admission, and early (up to 7 days) and late (8-28 days) neonatal death.We used univariable and multivariable logistic regression models to examine the association between birth period  and odds of each outcome. A generalized estimating equations approach was used to account for clustering at the level of birth institution. We assessed the effect of time spent in the pandemic by incorporating an interaction term between our exposure  and number of weeks since March 15, 2020, for each PTB outcome. Analyses were performed using SAS Enterprise Guide statistical software version 7.15 (SAS Institute). Statistical tests were 2-sided, with α < .05 considered significant. Data analysis was performed from March 15 to September 30, 2020.A total of 67 747 births occurred during the pandemic period, and 348 633 births occurred during the historical period. There were no differences in baseline characteristics between groups. There was no difference in the proportion of PTBs (5103 [7.5%] vs 26 216 [7.5%] PTBs) or stillbirths (347 [0.5%] vs 1799 [0.5%] stillbirths) between the pandemic and historical groups. After multivariable analysis, the adjusted odds ratio (aOR) for PTB was 1.00 , and that for stillbirth was 0.99  . We obse2 In contrast, no difference in PTB was observed in a population-based study in Sweden, where strict lockdown orders were not in effect.6We found no differences in the overall risk of PTB, stillbirth, or other perinatal outcomes during the first 6 months of the COVID-19 pandemic. We observed a small reduction in PTB at less than 32 weeks’ GA, similar to Denmark and Ireland, where comparable strict lockdown measures were in effect.Limitations of this study include the inability to evaluate out-of-hospital births; however, less than 3% of births in Ontario occur outside of hospitals. We could not evaluate for some factors that influence PTB risk, such as smoking. We did not evaluate the risk of PTB among women who experienced COVID-19 during pregnancy because this number was small.The COVID-19 pandemic first wave did not coincide with significant changes in overall PTB or stillbirth in Ontario. A small reduction in PTB at less than 32 weeks’ GA suggests that strict lockdown measures may have been associated with reduced risk in this subgroup.