Cumulative Life Stressors and Stress Response to Threatened Preterm Labor as Birth Date Predictors

Cumulative Life Stressors and Stress Response to Threatened Preterm Labor as Birth Date Predictors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cumulative Life Stressors and Stress Response to Threatened Preterm Labor as Birth Date Predictors Máximo Vento, Alba Moreno-Giménez, Laura Campos-Berga, Vicente Diago, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-454419/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Purpose: Preterm birth represents one of the main causes of neonatal morbimortality and a risk factor for neurodevelopmental disorders. Appropriate predictive methods for preterm birth outcome, which consequently would facilitate preventing programs, are needed. We aim to predict delivery date in women with a threatened preterm labor (TPL) based on stress response to TPL diagnosis, cumulative life stressors, and relevant obstetric variables. Methods: A prospective cohort of 157 pregnant women with TPL diagnosis between 24 and 31weeks gestation formed the study sample. To estimate the stress response to TPL, maternal salivary cortisol, α-amylase levels, along with anxiety and depression symptoms were measured. To determine cumulative life stressors, previous traumas, social support, and family functioning were registered. Then, linear regression models were used to examine the effect of potential predictors of birth date. Results: The main predictors were lower family adaptation, higher Body Mass Index (BMI), higher cortisol levels and TPL diagnosis week, which showed a non-linear interaction with cortisol levels: TPL women with middle- and high-cortisol levels before 29 weeks of gestation presented an imminent labor. Conclusion: A combination of stress response to TPL diagnosis (salivary cortisol) and cumulative stressors (family adaptation) together with obstetric factors (TPL week and BMI) was the best birth date predictor. Therefore, a psychosocial therapeutic intervention program aimed to increase family adaptation and decrease cortisol levels at TPL diagnosis as well as losing weight, may prevent preterm birth in symptomatic women. Obstetrics & Gynecology cortisol previous traumas family functioning anxiety depressive symptoms preterm birth Figures Figure 1 Figure 2 Figure 3 Introduction Despite advances in increasing survival rate, preterm birth is still the main cause of neonatal morbidity [ 1 ], representing one of the most leading risk factors for neurodevelopmental disabilities during childhood [ 2 ]. Critically, the lack of accurate prediction methods of preterm birth in TPL women is a matter of concern due to the potential iatrogenic effects of repeated antenatal corticosteroid on the future child’s neurodevelopment [ 3 , 4 ], stressful and unnecessary hospitalizations, and elevated costs for public health system [ 5 , 6 ]. One-third of hospitalized pregnant women suffer from a threatened preterm labor (TPL), but more than 50% do not progress to active labor [ 7 ] and only about half of preterm births are preceded by a known risk factor [ 8 ]. Different prevention programs have been implemented around the world aimed to reduce preterm birth [ 9 ] and social determinants are gaining consideration from scientific organizations [ 10 ]. Therefore, reliable methods to stratify TPL women into low and high-risk groups for preterm birth outcome are required. A growing body of research has indicated that both chronic life stress prior to conception and stressful events during pregnancy may act as potential risk factors for preterm birth [ 11 , 12 ]. When coping mechanisms are saturated due to chronic life stress, overexposure to neuroendocrine mediators (e.g., cortisol or α-amylase) that maintain the homeostasis of Hypothalamic-Pituitary-Adrenal (HPA) axis [ 13 , 14 ] and the Sympathetic-Adrenal-Medullary (SAM) axis [ 14 ] may have a deleterious impact on both mother and fetus [ 15 ], increasing the risk of preterm birth. Among possible causes of chronic stress, a history of traumatic events and poor social or family functioning have usually been identified. In fact, it is well-documented that a history of traumatic life events prior to conception may increase the risk of preterm birth [ 16 – 20 ]. However, although social support may modulate the association between life stressful events and preterm birth, findings are inconclusive [ 12 , 21 – 23 ]. Whereas a systematic review concluded null relationship between maternal social support and preterm birth [ 22 ], more recent studies pointed out that the lack of partner support rather than lack of general social support was associated with higher risk of preterm birth [ 21 , 23 , 24 ]. Regarding stressful events during pregnancy, they may alter normal balance of immune mediators, hormones, and neurotransmitters involved in timing of birth, increasing the risk of preterm birth [ 25 , 26 ] as well as psychomotor impairments [ 27 ]. Noteworthy, TPL is considered a stressful prenatal event likely to trigger a biopsychological stress response [28a, 28b]. First, from a biological perspective, TPL event may dysregulate both the HPA axis [ 13 , 14 ] and the SAM axis [ 14 ]. As for HPA axis biomarkers, research has revealed that cortisol levels at TPL diagnosis may predict birth 48 hours after TPL diagnosis [ 29 ]. Conversely, other study addressing SAM activity measured by α-amylase levels has showed inconclusive findings [ 14 ]. Whereas α-amylase dysregulation has been suggested as the underlying mechanism for the link between maternal depression and prematurity [ 30 ], no association between α-amylase levels and preterm birth has been found in non-depressed women [28a, 28b]. Second, from a psychological perspective, both gestational anxiety [ 31 , 32 ] and depressive symptoms [ 33 – 35 ] which may be triggered by TPL diagnosis [ 36 ] can also be associated with preterm birth. In sum, women experiencing chronic life stress may need only another significant stressor during pregnancy such as TPL to reach the tipping point that leads to a preterm birth [ 31 ]. In spite of focused research efforts, it is still unclear which stress-related factors are most strongly associated with preterm birth, for several reasons. Firstly, although the impact of a stressful event during pregnancy can be modulated by a combination of biopsychosocial stress-related pathways (HPA or SAM stress biomarkers, anxious-depressive symptoms at TPL diagnosis, and/or previous traumatic events as well as social support), most studies have considered these pathways separately [ 37 ]. Secondly, the relationship between these biomarkers and self-reported psychosocial stress is not straightforward [ 38 ]. Moreover, the few studies that have simultaneously examined different stress-related outcomes have included asymptomatic pregnant women (i.e., without TPL), reporting inconclusive findings: whereas some studies not using self-reports found an association between preterm birth and stress biomarkers [ 39 – 40 ], others found that maternal self-reports may improve the biomarkers prediction [ 41 ]. Thirdly, prospective studies with symptomatic women usually conduct a follow-up until 48 hours after a TPL diagnosis instead of until birth [ 29 ]. In the present follow-up study, multiple stress-related outcomes are studied simultaneously in symptomatic women from TPL diagnosis to birth date. This study aims to predict the birth date in TPL women by means of a combination of multiple stress-related factors: (i) cumulated life stressors (previous traumas, social support, and family functioning); and (ii) biopsychological response to TPL diagnosis (salivary cortisol and α-amylase as well as anxiety and depression symptoms). We expect that, considering studies that examine a combination of stress-related variables, biomarkers would be the strongest birth date predictors [ 39 – 40 ]. However, self-reports assessing chronic social stress and psychological stress response to TPL diagnosis may improve this prediction [ 41 ]. Also, the association between biomarkers and self-reports should not be constrained to be linear [ 38 ]. Material And Methods This is a prospective cohort study performed in the Division of Obstetrics at a tertiary referral hospital during a 12-month period. The Ethics Committee at the Health Research Institute approved the study protocol (ref. 2015/0086) and informed consent was obtained from all participants. Participants Eligible participants were pregnant women diagnosed of TPL between 24- and 31 + 6- weeks gestation to guarantee that all participants were subjected to the same treatment. TPL was diagnosed if the following clinical signs were present: regular uterine contractions associated with cervical changes (≥ 80% cervical effacement or cervical dilation ≥ 2 cm), measured by the cervical ultrasound (cervical length < 25 mm). After TPL diagnosis, fetal cardiac activity and uterine contractions were monitored by abdominal ultrasound. If contractions continued, women were admitted to the obstetric ward [ 42 ]. All women received one corticosteroids dose at least 12 hours before saliva sample collection, and the second corticosteroids dose was administered after it. Considering that corticosteroid average lifetime is 12 hours, antenatal steroid levels decreased notably when saliva sample was collected. Tocolytic therapy was atosiban or nifedipine [ 43 ]. Atosiban was initiated with a 6.75 mg/min bolus. Then, 300 µg/min − 1 as loading dose for 3 hours and 100 µg/min − 1 as maintenance dose for 48 hours was administered. Alternatively, nifedipine 20 mg, followed by 10 mg each 20 minutes until 40 mg for 1 hour, was administered [ 44 ]. Thus, all women received atosiban or nifedipine for < 24 hours. Finally, in cases of imminent labor (cervix between 4–10 cm dilated, rate of cervical dilation at least 1 cm/hour, effacement is usually complete, and fetal descent through birth canal begins), magnesium sulfate is usually administered but, in our sample, none of the participants received magnesium sulfate before saliva sample collection. Exclusion criteria included severe medical conditions (e.g., diabetes mellitus), severe obstetric complications (placenta abruption, preeclampsia, intrauterine growth restriction, cervical dilation > 4 cm, infection, obstructed labor), fetal anomalies, teratogenic substances use, and social exclusion risk, which is considered a stressful condition that may act as confusing variable. To assess social exclusion risk, multidimensional criteria were employed: (i) risk of poverty; (ii) severe material deprivation; and/or (iii) jobless household [ 45 ]. A final sample of 151 TPL women completed the follow-up until birth. See Fig. 1 for the recruitment flow diagram. Instruments and procedure Psychological assessment . The following questionnaires were completed by participants in a 1 hour-session following recruitment. The Traumatic Experience Questionnaire (TEQ) [ 46 ] is a screening test to diagnose post-traumatic stress disorder. The questionnaire reports in three parts: (i) list of traumatic experiences; (ii) the most important traumatic event; and (iii) list of symptoms; whose sum represents the total score. The Multidimensional Scale of Perceived Social Support (MSPSS) [ 47 ] assesses an individual’s perception of the social support from family, friends, and significant others (partner). The Family Adaptability Cohesion Evaluation Scale III (FACES III) [ 48 ] measures family Cohesion (degree to which family members are separated from or connected to their family) and family Adaptability (extent to which the family system is flexible and able to change facing new circumstances). The State-Trait Anxiety Inventory (STAI) [ 49 ] assesses trait and state anxiety. It can be used in clinical settings to diagnose anxiety and to distinguish it from depressive syndromes. Higher scores indicate greater anxiety. The Beck Depression Inventory Short Form (BDI/SF) [ 50 ] measures characteristic attitudes and symptoms of depression for psychiatric and non-psychiatric populations. Stress biomarkers. Concerning analytical determinations, standard of cortisol was purchased from Sigma-Aldrich Química SA (Madrid, Spain). Saliva samples were collected on the morning after admission between 10–12 a.m. (minimum 1h after breakfast). Samples were stored at -80 ºC and were thawed on ice and homogenized. The sample treatment to determine cortisol was based on a previous work [ 51 ]. Briefly, 25 µL of sample were subjected to liquid-liquid extraction to extract cortisol, then the organic layer was evaporated to dryness and the residues were reconstituted in water (pH 3): methanol (85:15 v/v) solution. Finally, 5 µL were injected in the chromatographic system (ultra-performance liquid chromatography coupled to tandem mass spectrometry). Salivary α-amylase assay kit was acquired from Salimetrics (Suffolk, United Kingdom). For the α-amylase determination, samples were vortexed and centrifuged. Then, they were diluted with the α-amylase diluent at 1:200 as final dilution. Finally, they were subjected to the kinetic enzyme assay. Statistical analysis As for statistical analysis, data were summarized using mean (standard deviation) and median (1st, 3rd quartile) for continuous variables and relative and absolute frequencies for categorical variables. Correlations among stress-related variables were assessed with Spearman’s correlation. Association between potential predictors and birth week was assessed using a linear regression model. Parity, Body Mass Index (BMI), multiple pregnancy, in-vitro fertilization, and the TPL diagnosis week were also included due to their potential influence on preterm birth [ 52 ]. Selection of the predictors included in the model was performed using L1 penalization. The lambda parameter was selected using 500 repetitions of 10-fold cross-validation. Model performance was assessed estimating optimism corrected R-square using bootstrapping [ 53 ]. All statistical analyses were performed using R (version 3.5.3), rms (version 5.1–3.1) and glmnet (version 2.0–16). Results Socio-demographic and clinical variables of the participants are summarized in Table 1 . Prior to modelling, an exploratory data analysis was performed by examining correlations between the different stress-related variables (Fig. 2 ). Chronic stress-related variables showed moderate to strong correlations among them (MSPSS and FACES). Similarly, psychological stress-related responses to TPL (STAI and BDI) moderately correlated to each other. No other evident associations were found. Table 1 Sociodemographic and clinical variables of the final sample. Variable Final sample (n = 157) Mean (SD) / n(%) Median (1st, 3rd Q.) Maternal age 31.75 (5.41) 32 (28, 36) Parity 0.52 (0.92) 0 (0, 1) BMI 22.58 (3.07) 22.04 (21, 23) Threatened preterm labor week 29.57 (2.87) 30 (28, 32) State STAI 20.01 (9.72) 18 (14, 24) Trait STAIR 17.86 (8.76) 17 (11, 22) BDI – II 3.01 (3.11) 2 (1, 4) Friends MSPSS 25.18 (3.61) 27 (24, 28) Family MSPSS 26.43 (2.6) 28 (26, 28) Partner MSPSS 27.1 (1.91) 28 (27, 28) Adaptation FACES III 30.53 (6.42) 30 (28, 34) Cohesion FACES III 31.73 (5.32) 32 (29, 35) TEQ 2.91 (4.26) 0 (0, 5) Cortisol (nmol L − 1 ) 3.06 (4.95) 1.33 (0.05, 3.61) α-amylase (U mL − 1 ) 68.18 (65.42) 54.12 (27.95, 78.92) In-vitro fertilization No 141 (89.81%) Yes 16 (10.19%) Multiple pregnancy No 89 (56.69%) Yes 68 (43.31%) BMI: Body Mass Index; STAI: State-Trait Anxiety Inventory; BDI/SF: Beck Depression Inventory Short Form; MSPSS: Multidimensional Scale of Perceived Social Support; FACES: Family Adaptability Cohesion Evaluation Scale; TEQ: Traumatic Experience Questionnaire. Variable selection using L1 penalization specified four predictors as the optimum complexity for the linear regression predictive model (Table 2 ). These variables were family Adaptation (FACES), BMI, TPL week, and cortisol levels. Additionally, a non-linear trend for TPL week using regression splines was added to the model. Table 2 Results of the fitted linear regression model to predict the birth week. Variable Coefficient 95% CI p -value (Intercept) -43.49 [-70.3, -16.7] 0.002 Adaptation FACES III 0.11 [0.026, 0.19] 0.01 BMI -0.23 [-0.41, -0.05] 0.012 log(cortisol) -18.21 [-28.9, -7.54] 0.001 TPLweek 2.92 [1.91, 3.92] < 0.001 TPLweek’ -3.04 [-4.53, -1.55] < 0.001 TPLweek’’ 17.27 [6.43, 28.1] 0.002 TPLweek:log(cortisol) 0.66 [0.25, 1.07] 0.002 TPLweek’:log(cortisol) -0.75 [-1.38, -0.11] 0.022 TPLweek’’:log(cortisol) 4.91 [0.19, 9.63] 0.041 BMI: Body Mass Index; FACES: Family Adaptability Cohesion Evaluation Scale; TPL: Threatened Preterm Labor. Birth Week= − 43.49–0.23*BMI + 2.92*TPLweek– 0.04*max(TPLweek–24, 0) 3 +0.21*max(TPLweek – 29, 0) 3 – 0.26*max(TPLweek – 31, 0) 3 + 0.009*max(TPLweek– 33, 0) 3 – 18.21*log(cortisol) + 0.11*adaptation + log(cortisol)*(0.66*TPLweek – 0.009*max(TPLweek – 24, 0) 3 + 0.06*max(TPLweek – 29, 0) 3 – 0.08*max(TPLweek – 31, 0) 3 + 0.03*max(TPLweek – 33, 0) 3 This model had an R-squared value of 0.37 and an optimism corrected R-squared value of 0.30. To aid in the interpretation of the non-linear effect of TPL week, a marginal effects plot for this variable and its interaction with log (cortisol) values is provided (Fig. 3 ). Discussion Main findings This study points out the relevance of considering a combination of multiple stress-related factors: (i) cumulated life stressors (previous traumas, social support, and family functioning); and (ii) the biopsychological response to TPL diagnosis (salivary cortisol and α-amylase as well as anxiety and depression symptoms) to predict the birth date in TPL women. According to previous research [ 38 ], the relationship between self-reported stress and biomarkers levels seems to be weak in TPL women. As for which stress measures are most strongly associated with preterm birth, lower family adaptation, higher BMI, and middle- and high-levels of cortisol in women with TPL diagnosis before 29 weeks of gestation were the best predictors. Taking all these elements together, given multifactorial etiology of stress [ 37 ], and performing simultaneous analysis of both psychosocial (family adaptation) and biological variables (cortisol levels) as well as obstetric factors (BMI and the TPL diagnosis week) improves the birth date prediction better than when analyzing these factors separately [ 41 ]. Strengths and limitations The strengths of this study are as follows: (i) the inclusion of multiple stress-related variables; (ii) the successful follow-up of participants from TPL diagnosis to birth; (iii) the rigorous inclusion criteria to control additional stressful variables such as social exclusion or major medical illness; and (iv) the consideration of other potential obstetric predictors such as BMI, TPL diagnosis week, multiple pregnancy, in-vitro fertilization, or parity in the regression model. In turn, these strengths have limited generalizability of results and sample size. Furthermore, participants were not included if any data were missing or the follow-up was not completed, which could cause a selection bias. Finally, although all pregnant women at 24–31 + 6 weeks received similar treatment, individual differences in response to tocolytic agents and corticosteroids could have influenced biomarkers determinations. Clinical interpretation Regarding stress biomarkers, middle- and high-cortisol levels in women with TPL diagnosis before 29 weeks of gestation predicted earlier birth date. In line with Campbell et al. (2005)[ 29 ] increasing levels of HPA axis biomarkers were relevant for determining birth date in TPL women. However, whereas they observed that stress biomarkers had a significant association with prematurity from the 28th week of gestation and onwards [ 29 ], our findings have shown this association from the 24th to 29th weeks of gestation. This discrepancy may be explained due to differences in follow-up length; whereas Campbell et al. (2005) [ 29 ] carried out a follow-up until 48 hours after TPL diagnosis, our research extended it until birth. With regard to α-amylase levels, no differences have been observed, indicating that α-amylase biomarker is not a significant variable to predict birth week in symptomatic women (see García-Blanco et al. (2017) for a similar finding) [28a, 28b]. In this case, previous research that found an association between α-amylase levels and preterm birth suggested that this relationship may be restricted to women with prenatal depression [ 30 ]. As for psychological response to TPL, unlike previous studies that found an association between preterm birth and gestational anxiety [ 31 , 32 ] and depressive symptoms [ 33 – 35 ], these self-reported symptoms after a TPL diagnosis have not been relevant to estimate the birth week in our study. Thus, this apparent inconsistency may be explained by inclusion of women without TPL diagnosis in previous studies. Undoubtedly, all women with a TPL diagnosis were expected to react with a subjective increase of anxiety and depressive symptoms. Nevertheless, only some of those women have shown middle- and high- cortisol levels. Therefore, this study has shown that stress biomarkers were stronger predictors than the subjective state anxiety for TPL women. Like it occurs with anxiety symptoms, suffering traumatic experiences previously to pregnancy may be associated with preterm birth in asymptomatic women [ 17 , 19 – 20 ]. However, such differences have not been observed in this study with symptomatic women. It could be expected that previous traumas are not relevant in this context because TPL represents a traumatic event itself. Hence, TPL can be considered as a pregnancy-specific traumatic event that may provoke a stress-vulnerability status in all cases [ 16 ]. Concerning chronic social stress-related factors, family adaptation was a relevant factor to estimate the birth week. Likewise, other studies with non-TPL women have concluded that some aspects of family functioning (e.g., poor emotional understanding by the partner) are related to preterm birth [ 21 , 23 ]. In our study, family's ability to modify its rules, roles, and structure in response to environmental changes, rather than social support in general [ 22 ], was the most relevant social predictor [ 21 , 23 ]. Therefore, among self-reported variables, chronic social stress has been a stronger predictor than psychological symptoms after a TPL diagnosis. Thus, TPL can trigger an increase of subjective anxiety but it cannot be a modulator of self-reported family functioning. Finally, relating to obstetric variables, according to prior literature about preterm birth [ 52 ], actors such as maternal BMI and TPL week have also been relevant predictors for final birth date after a TPL in our study. Conclusion This follow-up study uses a multidimensional approach to examine stress response in pregnant women with an antenatal adverse event such as TPL in order to explore potential indicators of vulnerability to preterm birth. These findings can be applied as a new useful tool to determine the birth date in TPL women by means of a simultaneous analysis of chronic social stressors (family adaptation) and the biological stress response to TPL diagnosis (salivary cortisol) together with obstetric conditions (BMI and TPL week). This study has important public health implications, since the number of preterm births may be reduced by initiating early preventive psychosocial interventions to increase family adaptation and decrease cortisol levels after a TPL as well as lose weight in symptomatic women. Declarations ACKNOWLEDGEMENTS We are greatly indebted to all the pregnant women, their families, nursing, and medical staff who voluntarily participated in the present study. Without their collaboration and enthusiasm this study could not have been completed. Conflict of interest The authors have no relevant financial or non-financial interests to disclose. Ethics approval The Ethics Committee at the La Fe Health Research Institute approved the study protocol in 2015 (ref. 2015/0086) and informed consent was obtained from all participants. Availability of data and material Collected data and materials of assessment comply with field standards and are available if required. Author Contributions M. Vento: Protocol/project development; Funding acquisition; Supervision; Manuscript Writing/editing. A. Moreno-Giménez: Data collection; Manuscript Writing/editing. L. Campos-Berga: Data collection; Manuscript Writing/editing. V. Diago: Protocol/project development; Funding acquisition; Supervision; Manuscript Writing/editing. D. Hervás: Data analysis; Software; Manuscript Writing. P. Sáenz: Protocol/project development; Data collection; Manuscript Writing/editing. C. Cháfer-Pericás: Protocol/project development; Data analysis; Manuscript Writing/editing. A. García-Blanco: Protocol/project development; Funding acquisition; Data collection; Supervision; Manuscript Writing/editing. Funding Prior to study initiation, a grant was awarded by the Instituto de Salud Carlos III (PI18/01352). To achieve this grant, the project was assessed by an external peer review panel, guaranteeing scientific quality and ethical integrity. MV acknowledges PI17/0131 grant from the Instituto de Salud Carlos III (Spanish Ministry of Economy and Competitiveness) (ISCIII; Plan Estatal de I+D+I 2013-2016) and co-financed by the European Development Regional Fund “A way to achieve Europe” (ERDF); and RETICS funded by the PN 2018-2011 (Spain), ISCIII- Sub-Directorate General for Research Assessment and Promotion and the European Regional Development Fund (FEDER), RD16/0022/0001.VD acknowledges PI18/01352 grant from the ISCIII. 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Preterm Birth and Social Support during Pregnancy: a Systematic Review and Meta-Analysis. Pediatric and Perinatal Epidemiology 29, 523–535. https://doi.org/10.1111/ppe.12225. Orr, S. T (2004). Social support and pregnancy outcome: a review of the literature. Clinical Obstetrics and Gynecology 47(4), 842–855. https://doi.org/10.1097/01.grf.0000141451.68933.9f. Rauchfuss, M., & Maier, B. Biopsychosocial predictors of preterm delivery (2011). Journal of Perinatal Medicine 39, 515–521. https://doi.org/10.1515/JPM.2011.067. Surkan, P. J., Dong, L., Ji, Y., Hong, X., Ji, H., Kimmel, M., Tang, W. Y., & Wang, X. (2019). Paternal involvement and support and risk of preterm birth: findings from the Boston birth cohort. Journal of Psychosomatic Obstetrics and Gynecology 40(1), 48–56. https://doi.org/10.1080/0167482X.2017.1398725. Arck, P. C. (2001). Stress and Pregnancy Loss: Role of Immune Mediators, Hormones and Neurotransmitters. American Journal of Reproductive Immunology 46, 117–123. https://doi.org/10.1111/j.8755-8920.2001.460201.x. Braun, K., Bock, J., Wainstock, T., Matas, E., Gaisler-salomon, I., Fegert, J., et al. (2017). Neuroscience and Biobehavioral Reviews Experience-induced transgenerational (re-)programming of neuronal structure and functions : Impact of stress prior and during pregnancy. Neuroscience and Biobehavioral Reviews 0–1. https://doi.org/10.1016/j.neubiorev.2017.05.021. Polanska K, Krol A, Merecz-Kot D, Jurewicz, J., Makowiec‐Dabrowska, T., Chiarotti, F., & Hanke, W. (2017). Maternal stress during pregnancy and neurodevelopmental outcomes of children during the first 2 years of life. Journal of Paediatrics and Child Health 53(3):263-270. https://doi.org/10.1111/jpc.13422 . a. García-Blanco, A., Diago, V., Serrano, V., Cruz, D. La, & Hervás, D (2017). Can stress biomarkers predict preterm birth in women with threatened preterm labor ? Psychoneuroendocrinology 83(Jan), 19–24. https://doi.org/10.1016/j.psyneuen.2017.05.021 . b. García-Blanco, A., Monferrer, A., Grimaldos, J., Hervás, D., Balanzá-Martínez, V., Diago, V., Vento, M., & Cháfer-Pericás, C (2017). A preliminary study to assess the impact of maternal age on stress-related variables in healthy nulliparous women. Psychoneuroendocrinology 78, 97–104. https://doi.org/10.1016/j.psyneuen.2017.01.018. Campbell, M. K., Challis, J. R. G., Dasilva, O., & Bocking, A. D (2005). A cohort study found that white blood cell count and endocrine markers predicted preterm birth in symptomatic women. Journal of Clinical Epidemiology 58, 304–310. https://doi.org/10.1016/j.jclinepi.2004.06.015. Braithwaite, E. C., Ramchandani, P. G., Lane, T. A., & Murphy, S. E (2015). Symptoms of prenatal depression are associated with raised salivary alpha-amylase levels. Psychoneuroendocrinology 60, 163–172. https://doi.org/10.1016/j.psyneuen.2015.06.013. Ding, X., Wu, Y., Xu, S., Zhu, R., & Jia, X (2014). Maternal anxiety during pregnancy and adverse birth outcomes: A systematic review and meta-analysis of prospective cohort studies. Journal of Afective Disorders 159(81), 103–110. https://doi.org/10.1016/j.jad.2014.02.027. Grigoriadis, S., Graves, L., Peer, M., Mamisashvili, L., Tomlinson, G., Vigod, S. N., et al. (2018). Maternal anxiety during pregnancy and the association with adverse perinatal outcomes: Systematic review and meta-analysis. Journal of Clinical Psychiatry 79(5). https://doi.org/10.4088/JCP.17r12011. Field, T (2017). Prenatal Depression Risk Factors, Developmental Effects and Interventions: A Review. Journal of Pregnancy and Child Health 04(01), 1–25. https://doi.org/10.4172/2376-127x.1000301. Hermon, N., Wainstock, T., Sheiner, E., Golan, A., & Walfisch, A (2019). Impact of maternal depression on perinatal outcomes in hospitalized women—a prospective study. Archives of Women’s Mental Health 85–91. https://doi.org/10.1007/s00737-018-0883-5. Liu, C., Cnattingius, S., Bergström, M., Östberg, V., & Hjern, A. (2016). Prenatal parental depression and preterm birth : a national cohort study. An International Journal of Obstetrics and Gynaecology 123 (12), 1973–1982. https://doi.org/10.1111/1471-0528.13891. Dagklis, T., Tsakiridis, I., Chouliara, F., Mamopoulos, A., Rousso, D., Athanasiadis, A., & Papazisis, G (2018). Antenatal depression among women hospitalized due to threatened preterm labor in a high-risk pregnancy unit in Greece. Journal of Maternal-Fetal and Neonatal Medicine 31(7), 919–925. https://doi.org/10.1080/14767058.2017.1301926. Field, T., Diego, M., & Hernandez-reif, M. (2009). Prematurity and potential predictors. International Journal of Neuroscience 118, 277–298. https://doi.org/10.1080/00207450701239327. Himes, K. P., & Simhan, H. N. (2011). Plasma Corticotropin-Releasing Hormone and Cortisol Concentrations and Perceived Stress among Pregnant Women with Preterm and Term Birth. American Journal of Perinatology 1(212), 443–448. https://doi.org/http://dx.doi.org/10.1055/s-0030-1270119. Hoffman, M. C., Mazzoni, S. E., Wagner, B. D., Laudenslager, M. L., & Ross, R. G. (2016). Measures of Maternal Stress and Mood in Relation to Preterm Birth. Obstetrics and Gynecology 127(3), 545–552. https://doi.org/10.1097/AOG.0000000000001287. Pearce, B. D., Grove, J., Bonney, E. A., Bliwise, N., Dudley, D. J., Schendel, D. E., & Thorsen, P (2010). Interrelationship of Cytokines, Hypothalamic-Pituitary-Adrenal Axis Hormones, and Psychosocial Variables in the Prediction of. Gynecologic and Obstetric Investigation 70, 40–46. https://doi.org/10.1159/000284949. Ruiz, R. J., Fullerton, J., Brown, C. E. L., & Dudley, D. J. (2002). Predicting Risk of Preterm Birth: The Roles of Stress, Clinical Risk Factors, and Corticotropin-. Biological Research For Nursing 4, 54–64. https://doi.org/10.1177/1099800402004001007. American College of Obstetricians and Gynecologists (2016). ACOG practice bulletin no. 171: Management of preterm labor. Obstetrics and Gynecology 128(4),e155-64. https://doi.org/ 10.1097/AOG.0000000000001711. Ali, A. A., Sayed, A. K., Sherif, E., Loutfi, G. O., Mahmoud, A., Ahmed, M., et al. (2019). Systematic review and meta-analysis of randomized controlled trials of atosiban versus nifedipine for inhibition of preterm labor. International Journal Gynecology Obstectrics 145, 139–148. https://doi.org/10.1002/ijgo.12793. Conde-Agudelo, A., Romero, R., & Kusanovic, J. P (2011). Nifedipine in the management of preterm labor: a systematic review and metaanalysis. American Journal of Obstetrics 204(2), 134.e1-134.e20. https://doi.org/10.1016/j.ajog.2010.11.038. Savova, I. (2012). Europe 2020 Strategy-towards a smarter, greener and more inclusive EU economy? Eurostat (EU Commission) 1, ISSN 1977-0316. https://doi.org/10.2785/101636. Vrana, S., & Lauterbach, D (1994). Prevalence of traumatic events and post-traumatic psychological symptoms in a nonclinical sample of college students. Journal of Traumatic Stress 7(2), 289–302. Zimet, G. D., Dahlem, N. W., Zimet, S. G., Gordon, K., & Farley, G. K (1988). The Multidimensional Scale of Perceived Social Support The Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment 52(1), 37–41. https://doi.org/10.1207/s15327752jpa5201. Olsen, D. H., Portner, J., & Lavee, Y. (1985). Family Adaptability and Cohesion Evaluation Scales (FACES-II). Minneapolis: University of Minnesota. Spielberger, C. D., Gorsuch, R. L., Lushene, R., Vagg, P. R., & Jacobs, G. A. (1983). Manual for the state-trait anxiety inventory. Palo Alto, CA, Consulting Psychologists Press. Beck, A. T., Rial, W. Y., & Rickels, K (1974). Short form of depression inventory: cross-validation. Psychological Reports. García-Blanco, A., Vento, M., Diago, V., & Cháfer-Pericás, C (2016). Reference ranges for cortisol and α-amylase in mother and newborn saliva samples at different perinatal and postnatal periods. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences 1022, 249–255. https://doi.org/10.1016/j.jchromb.2016.04.035. Goldenberg, R.L., Culhane, J.F., Iams, J.D., & Romero, R (2008). Preterm Birth 1 Epidemiology and causes of preterm birth. Lancet 371,75–84. https://doi.org/10.1016/S0140-6736(08)60074-4. Harrell, F. E (2015). Describing, resampling, validating, and simplifying the model. In Regression Modeling Strategies. Springer, pp. 103–126. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-454419","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":24305987,"identity":"e68e2439-4150-4846-b1f7-08185745f202","order_by":0,"name":"Máximo Vento","email":"","orcid":"","institution":"Neonatal Research Unit, Health Research Institute La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Máximo","middleName":"","lastName":"Vento","suffix":""},{"id":24305988,"identity":"37085188-95d8-4ab1-958e-f4dc18012a8a","order_by":1,"name":"Alba Moreno-Giménez","email":"","orcid":"","institution":"Neonatal Research Unit, Health Research Institute La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alba","middleName":"","lastName":"Moreno-Giménez","suffix":""},{"id":24305989,"identity":"93d65d82-b034-48a5-aeea-9068a29e7078","order_by":2,"name":"Laura Campos-Berga","email":"","orcid":"","institution":"Neonatal Research Unit, Health Research Institute La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Campos-Berga","suffix":""},{"id":24305990,"identity":"359cb862-728b-4afa-a2c9-569cb409c52a","order_by":3,"name":"Vicente Diago","email":"","orcid":"","institution":"Division of Obstetrics and Gynecology, University and Polytechnic Hospital La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Vicente","middleName":"","lastName":"Diago","suffix":""},{"id":24305991,"identity":"a243f721-cd79-4f07-a702-ab35e457282f","order_by":4,"name":"David Hervás","email":"","orcid":"https://orcid.org/0000-0003-0635-4961","institution":"Data Science Unit, Biostatistics, and Bioinformatics, Health Research Institute La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Hervás","suffix":""},{"id":24305992,"identity":"2f816cb8-b5ba-4101-9610-be528087e0ed","order_by":5,"name":"Pilar Sáenz","email":"","orcid":"","institution":"Neonatal Research Unit, Health Research Institute La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pilar","middleName":"","lastName":"Sáenz","suffix":""},{"id":24305993,"identity":"20444ce3-a5f4-4a66-9a26-ce8a2f18ed67","order_by":6,"name":"Consuelo Cháfer","email":"","orcid":"","institution":"Neonatal Research Unit, Health Research Institute La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Consuelo","middleName":"","lastName":"Cháfer","suffix":""},{"id":24305994,"identity":"555c9274-7fd1-4459-813e-69ec455ba984","order_by":7,"name":"Ana García-Blanco","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIie2PsWrDMBCGJQzSYur1AqV6BYlCnueyNIshKV0ymBAoaCrpHPIS6RsYBJ7yAAoJRVk6dXAhmEwmcteihGyB6uOG47iP/46QSOSWYV25ruOzaxTsurS8JuhXATy/li1fHT0Un+KOl8xhsRNZ77sPZDINKrCrZHJfvSidIpdYfanFMvfK2oRjLMoEGCIjowaQGZTb/FFSHX5I2GGdQOuVzDHA1iubtVfa8GHS5pL+aK/4CBhor9hUOTpLgoqy+djQOSoNjsvB3KjF29Ozwyr8y4MdfuyPDQrxjszVjREZN6uyLsKHdZj0zwjPCoTQ44WFSCQS+eecAOcpUe6Cev3RAAAAAElFTkSuQmCC","orcid":"","institution":"Health Research Institute La Fe: Instituto de Investigacion Sanitaria La Fe","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"García-Blanco","suffix":""}],"badges":[],"createdAt":"2021-04-23 09:06:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-454419/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-454419/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":8779609,"identity":"8e8d4fc0-b7fb-49ca-a516-4c229e08604a","added_by":"auto","created_at":"2021-05-04 22:22:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":152167,"visible":true,"origin":"","legend":"Flow diagram describing the recruitment process, the exclusion determinants, and the participants who completed the study. ","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-454419/v1/3c49a1f18d3dc520bbcd1760.jpg"},{"id":8780166,"identity":"bed79c56-065b-453c-a3c2-8231aab7998e","added_by":"auto","created_at":"2021-05-04 22:25:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":319225,"visible":true,"origin":"","legend":"Correlation plot between the different stress-related variables.","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-454419/v1/7341b870a635c8a27ebb3806.jpg"},{"id":8780432,"identity":"53e5061d-28f5-4714-8d12-e2af5d59d391","added_by":"auto","created_at":"2021-05-04 22:28:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":172731,"visible":true,"origin":"","legend":"Marginal effect plots depicting the relationship between labor week and a) log(cortisol) interaction with TPL week, b) Body Max Index, and c) family adaptation.","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-454419/v1/86e89b4fbf5a2fc3cd3d75b6.jpg"},{"id":13691298,"identity":"b2725838-045b-405c-b67c-a9e1ec45aeee","added_by":"auto","created_at":"2021-09-17 12:37:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":637509,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-454419/v1/f65b0633-1255-4144-b507-b6b0fdd486bb.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eCumulative Life Stressors and Stress Response to Threatened Preterm Labor as Birth Date Predictors\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eDespite advances in increasing survival rate, preterm birth is still the main cause of neonatal morbidity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], representing one of the most leading risk factors for neurodevelopmental disabilities during childhood [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Critically, the lack of accurate prediction methods of preterm birth in TPL women is a matter of concern due to the potential iatrogenic effects of repeated antenatal corticosteroid on the future child\u0026rsquo;s neurodevelopment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], stressful and unnecessary hospitalizations, and elevated costs for public health system [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. One-third of hospitalized pregnant women suffer from a threatened preterm labor (TPL), but more than 50% do not progress to active labor [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and only about half of preterm births are preceded by a known risk factor [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Different prevention programs have been implemented around the world aimed to reduce preterm birth [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and social determinants are gaining consideration from scientific organizations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, reliable methods to stratify TPL women into low and high-risk groups for preterm birth outcome are required.\u003c/p\u003e \u003cp\u003eA growing body of research has indicated that both chronic life stress prior to conception and stressful events during pregnancy may act as potential risk factors for preterm birth [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. When coping mechanisms are saturated due to chronic life stress, overexposure to neuroendocrine mediators (e.g., cortisol or α-amylase) that maintain the homeostasis of Hypothalamic-Pituitary-Adrenal (HPA) axis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and the Sympathetic-Adrenal-Medullary (SAM) axis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] may have a deleterious impact on both mother and fetus [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], increasing the risk of preterm birth. Among possible causes of chronic stress, a history of traumatic events and poor social or family functioning have usually been identified. In fact, it is well-documented that a history of traumatic life events prior to conception may increase the risk of preterm birth [\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, although social support may modulate the association between life stressful events and preterm birth, findings are inconclusive [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Whereas a systematic review concluded null relationship between maternal social support and preterm birth [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], more recent studies pointed out that the lack of partner support rather than lack of general social support was associated with higher risk of preterm birth [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding stressful events during pregnancy, they may alter normal balance of immune mediators, hormones, and neurotransmitters involved in timing of birth, increasing the risk of preterm birth [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] as well as psychomotor impairments [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Noteworthy, TPL is considered a stressful prenatal event likely to trigger a biopsychological stress response [28a, 28b]. First, from a biological perspective, TPL event may dysregulate both the HPA axis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and the SAM axis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As for HPA axis biomarkers, research has revealed that cortisol levels at TPL diagnosis may predict birth 48 hours after TPL diagnosis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Conversely, other study addressing SAM activity measured by α-amylase levels has showed inconclusive findings [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Whereas α-amylase dysregulation has been suggested as the underlying mechanism for the link between maternal depression and prematurity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], no association between α-amylase levels and preterm birth has been found in non-depressed women [28a, 28b]. Second, from a psychological perspective, both gestational anxiety [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and depressive symptoms [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] which may be triggered by TPL diagnosis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] can also be associated with preterm birth. In sum, women experiencing chronic life stress may need only another significant stressor during pregnancy such as TPL to reach the tipping point that leads to a preterm birth [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn spite of focused research efforts, it is still unclear which stress-related factors are most strongly associated with preterm birth, for several reasons. Firstly, although the impact of a stressful event during pregnancy can be modulated by a combination of biopsychosocial stress-related pathways (HPA or SAM stress biomarkers, anxious-depressive symptoms at TPL diagnosis, and/or previous traumatic events as well as social support), most studies have considered these pathways separately [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Secondly, the relationship between these biomarkers and self-reported psychosocial stress is not straightforward [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, the few studies that have simultaneously examined different stress-related outcomes have included asymptomatic pregnant women (i.e., without TPL), reporting inconclusive findings: whereas some studies not using self-reports found an association between preterm birth and stress biomarkers [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], others found that maternal self-reports may improve the biomarkers prediction [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Thirdly, prospective studies with symptomatic women usually conduct a follow-up until 48 hours after a TPL diagnosis instead of until birth [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In the present follow-up study, multiple stress-related outcomes are studied simultaneously in symptomatic women from TPL diagnosis to birth date.\u003c/p\u003e \u003cp\u003eThis study aims to predict the birth date in TPL women by means of a combination of multiple stress-related factors: (i) cumulated life stressors (previous traumas, social support, and family functioning); and (ii) biopsychological response to TPL diagnosis (salivary cortisol and α-amylase as well as anxiety and depression symptoms). We expect that, considering studies that examine a combination of stress-related variables, biomarkers would be the strongest birth date predictors [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, self-reports assessing chronic social stress and psychological stress response to TPL diagnosis may improve this prediction [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Also, the association between biomarkers and self-reports should not be constrained to be linear [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e "},{"header":"Material And Methods","content":"\u003cp\u003eThis is a prospective cohort study performed in the Division of Obstetrics at a tertiary referral hospital during a 12-month period. The Ethics Committee at the Health Research Institute approved the study protocol (ref. 2015/0086) and informed consent was obtained from all participants.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eParticipants\u003c/h2\u003e\n\u003cp\u003eEligible participants were pregnant women diagnosed of TPL between 24- and 31\u0026thinsp;+\u0026thinsp;6- weeks gestation to guarantee that all participants were subjected to the same treatment. TPL was diagnosed if the following clinical signs were present: regular uterine contractions associated with cervical changes (\u0026ge;\u0026thinsp;80% cervical effacement or cervical dilation\u0026thinsp;\u0026ge;\u0026thinsp;2 cm), measured by the cervical ultrasound (cervical length\u0026thinsp;\u0026lt;\u0026thinsp;25 mm). After TPL diagnosis, fetal cardiac activity and uterine contractions were monitored by abdominal ultrasound. If contractions continued, women were admitted to the obstetric ward [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. All women received one corticosteroids dose at least 12 hours before saliva sample collection, and the second corticosteroids dose was administered after it. Considering that corticosteroid average lifetime is 12 hours, antenatal steroid levels decreased notably when saliva sample was collected. Tocolytic therapy was atosiban or nifedipine [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. Atosiban was initiated with a 6.75 mg/min bolus. Then, 300 \u0026micro;g/min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as loading dose for 3 hours and 100 \u0026micro;g/min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as maintenance dose for 48 hours was administered. Alternatively, nifedipine 20 mg, followed by 10 mg each 20 minutes until 40 mg for 1 hour, was administered [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. Thus, all women received atosiban or nifedipine for \u0026lt;\u0026thinsp;24 hours. Finally, in cases of imminent labor (cervix between 4\u0026ndash;10 cm dilated, rate of cervical dilation at least 1 cm/hour, effacement is usually complete, and fetal descent through birth canal begins), magnesium sulfate is usually administered but, in our sample, none of the participants received magnesium sulfate before saliva sample collection.\u003c/p\u003e\n\u003cp\u003eExclusion criteria included severe medical conditions (e.g., diabetes mellitus), severe obstetric complications (placenta abruption, preeclampsia, intrauterine growth restriction, cervical dilation\u0026thinsp;\u0026gt;\u0026thinsp;4 cm, infection, obstructed labor), fetal anomalies, teratogenic substances use, and social exclusion risk, which is considered a stressful condition that may act as confusing variable. To assess social exclusion risk, multidimensional criteria were employed: (i) risk of poverty; (ii) severe material deprivation; and/or (iii) jobless household [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. A final sample of 151 TPL women completed the follow-up until birth. See Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for the recruitment flow diagram.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eInstruments and procedure\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003ePsychological assessment\u003c/em\u003e. The following questionnaires were completed by participants in a 1 hour-session following recruitment.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe Traumatic Experience Questionnaire (TEQ) [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e] is a screening test to diagnose post-traumatic stress disorder. The questionnaire reports in three parts: (i) list of traumatic experiences; (ii) the most important traumatic event; and (iii) list of symptoms; whose sum represents the total score.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe Multidimensional Scale of Perceived Social Support (MSPSS) [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e] assesses an individual\u0026rsquo;s perception of the social support from family, friends, and significant others (partner).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe Family Adaptability Cohesion Evaluation Scale III (FACES III) [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e] measures family Cohesion (degree to which family members are separated from or connected to their family) and family Adaptability (extent to which the family system is flexible and able to change facing new circumstances).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe State-Trait Anxiety Inventory (STAI) [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e] assesses trait and state anxiety. It can be used in clinical settings to diagnose anxiety and to distinguish it from depressive syndromes. Higher scores indicate greater anxiety.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe Beck Depression Inventory Short Form (BDI/SF) [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e] measures characteristic attitudes and symptoms of depression for psychiatric and non-psychiatric populations.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eStress biomarkers.\u003c/em\u003e Concerning analytical determinations, standard of cortisol was purchased from Sigma-Aldrich Qu\u0026iacute;mica SA (Madrid, Spain). Saliva samples were collected on the morning after admission between 10\u0026ndash;12 a.m. (minimum 1h after breakfast). Samples were stored at -80 \u0026ordm;C and were thawed on ice and homogenized. The sample treatment to determine cortisol was based on a previous work [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. Briefly, 25 \u0026micro;L of sample were subjected to liquid-liquid extraction to extract cortisol, then the organic layer was evaporated to dryness and the residues were reconstituted in water (pH 3): methanol (85:15 v/v) solution. Finally, 5 \u0026micro;L were injected in the chromatographic system (ultra-performance liquid chromatography coupled to tandem mass spectrometry).\u003c/p\u003e\n\u003cp\u003eSalivary \u0026alpha;-amylase assay kit was acquired from Salimetrics (Suffolk, United Kingdom). For the \u0026alpha;-amylase determination, samples were vortexed and centrifuged. Then, they were diluted with the \u0026alpha;-amylase diluent at 1:200 as final dilution. Finally, they were subjected to the kinetic enzyme assay.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eAs for statistical analysis, data were summarized using mean (standard deviation) and median (1st, 3rd quartile) for continuous variables and relative and absolute frequencies for categorical variables. Correlations among stress-related variables were assessed with Spearman\u0026rsquo;s correlation. Association between potential predictors and birth week was assessed using a linear regression model. Parity, Body Mass Index (BMI), multiple pregnancy, \u003cem\u003ein-vitro\u003c/em\u003e fertilization, and the TPL diagnosis week were also included due to their potential influence on preterm birth [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]. Selection of the predictors included in the model was performed using L1 penalization. The lambda parameter was selected using 500 repetitions of 10-fold cross-validation. Model performance was assessed estimating optimism corrected R-square using bootstrapping [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. All statistical analyses were performed using R (version 3.5.3), rms (version 5.1\u0026ndash;3.1) and glmnet (version 2.0\u0026ndash;16).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSocio-demographic and clinical variables of the participants are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Prior to modelling, an exploratory data analysis was performed by examining correlations between the different stress-related variables (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Chronic stress-related variables showed moderate to strong correlations among them (MSPSS and FACES). Similarly, psychological stress-related responses to TPL (STAI and BDI) moderately correlated to each other. No other evident associations were found.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSociodemographic and clinical variables of the final sample.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFinal sample\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;157)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean (SD) / n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian (1st, 3rd Q.)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMaternal age\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.75 (5.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (28, 36)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eParity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.52 (0.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0, 1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.58 (3.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.04 (21, 23)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eThreatened preterm labor week\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.57 (2.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (28, 32)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eState STAI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.01 (9.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (14, 24)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTrait STAIR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.86 (8.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (11, 22)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBDI \u0026ndash; II\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.01 (3.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (1, 4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eFriends MSPSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.18 (3.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (24, 28)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eFamily MSPSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.43 (2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (26, 28)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePartner MSPSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.1 (1.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (27, 28)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAdaptation FACES III\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.53 (6.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (28, 34)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCohesion FACES III\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.73 (5.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (29, 35)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTEQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.91 (4.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0, 5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCortisol (nmol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.06 (4.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.33 (0.05, 3.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026alpha;-amylase (U mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.18 (65.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.12 (27.95, 78.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eIn-vitro\u003c/em\u003e fertilization\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141 (89.81%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (10.19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMultiple pregnancy\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89 (56.69%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68 (43.31%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003eBMI: Body Mass Index; STAI: State-Trait Anxiety Inventory; BDI/SF: Beck Depression Inventory Short Form; MSPSS: Multidimensional Scale of Perceived Social Support; FACES: Family Adaptability Cohesion Evaluation Scale; TEQ: Traumatic Experience Questionnaire.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVariable selection using L1 penalization specified four predictors as the optimum complexity for the linear regression predictive model (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These variables were family Adaptation (FACES), BMI, TPL week, and cortisol levels. Additionally, a non-linear trend for TPL week using regression splines was added to the model.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eResults of the fitted linear regression model to predict the birth week.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCoefficient\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Intercept)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-43.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[-70.3, -16.7]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdaptation FACES III\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.026, 0.19]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[-0.41, -0.05]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003elog(cortisol)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-18.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[-28.9, -7.54]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTPLweek\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[1.91, 3.92]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTPLweek\u0026rsquo;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[-4.53, -1.55]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTPLweek\u0026rsquo;\u0026rsquo;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[6.43, 28.1]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTPLweek:log(cortisol)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.25, 1.07]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTPLweek\u0026rsquo;:log(cortisol)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[-1.38, -0.11]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTPLweek\u0026rsquo;\u0026rsquo;:log(cortisol)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.19, 9.63]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eBMI: Body Mass Index; FACES: Family Adaptability Cohesion Evaluation Scale; TPL: Threatened Preterm Labor.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBirth Week= \u0026minus;\u0026thinsp;43.49\u0026ndash;0.23*BMI\u0026thinsp;+\u0026thinsp;2.92*TPLweek\u0026ndash; 0.04*max(TPLweek\u0026ndash;24, 0)\u003csup\u003e3\u003c/sup\u003e +0.21*max(TPLweek \u0026ndash; 29, 0)\u003csup\u003e3\u003c/sup\u003e\u0026ndash; 0.26*max(TPLweek \u0026ndash; 31, 0)\u003csup\u003e3\u003c/sup\u003e + 0.009*max(TPLweek\u0026ndash; 33, 0)\u003csup\u003e3\u003c/sup\u003e \u0026ndash; 18.21*log(cortisol)\u0026thinsp;+\u0026thinsp;0.11*adaptation\u0026thinsp;+\u0026thinsp;log(cortisol)*(0.66*TPLweek \u0026ndash; 0.009*max(TPLweek \u0026ndash; 24, 0)\u003csup\u003e3\u003c/sup\u003e + 0.06*max(TPLweek \u0026ndash; 29, 0)\u003csup\u003e3\u003c/sup\u003e \u0026ndash; 0.08*max(TPLweek \u0026ndash; 31, 0)\u003csup\u003e3\u003c/sup\u003e + 0.03*max(TPLweek \u0026ndash; 33, 0)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThis model had an R-squared value of 0.37 and an optimism corrected R-squared value of 0.30. To aid in the interpretation of the non-linear effect of TPL week, a marginal effects plot for this variable and its interaction with log (cortisol) values is provided (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":" \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMain findings\u003c/h2\u003e \u003cp\u003eThis study points out the relevance of considering a combination of multiple stress-related factors: (i) cumulated life stressors (previous traumas, social support, and family functioning); and (ii) the biopsychological response to TPL diagnosis (salivary cortisol and α-amylase as well as anxiety and depression symptoms) to predict the birth date in TPL women. According to previous research [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], the relationship between self-reported stress and biomarkers levels seems to be weak in TPL women. As for which stress measures are most strongly associated with preterm birth, lower family adaptation, higher BMI, and middle- and high-levels of cortisol in women with TPL diagnosis before 29 weeks of gestation were the best predictors. Taking all these elements together, given multifactorial etiology of stress [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and performing simultaneous analysis of both psychosocial (family adaptation) and biological variables (cortisol levels) as well as obstetric factors (BMI and the TPL diagnosis week) improves the birth date prediction better than when analyzing these factors separately [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe strengths of this study are as follows: (i) the inclusion of multiple stress-related variables; (ii) the successful follow-up of participants from TPL diagnosis to birth; (iii) the rigorous inclusion criteria to control additional stressful variables such as social exclusion or major medical illness; and (iv) the consideration of other potential obstetric predictors such as BMI, TPL diagnosis week, multiple pregnancy, \u003cem\u003ein-vitro\u003c/em\u003e fertilization, or parity in the regression model. In turn, these strengths have limited generalizability of results and sample size. Furthermore, participants were not included if any data were missing or the follow-up was not completed, which could cause a selection bias. Finally, although all pregnant women at 24\u0026ndash;31\u0026thinsp;+\u0026thinsp;6 weeks received similar treatment, individual differences in response to tocolytic agents and corticosteroids could have influenced biomarkers determinations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClinical interpretation\u003c/h2\u003e \u003cp\u003eRegarding stress biomarkers, middle- and high-cortisol levels in women with TPL diagnosis before 29 weeks of gestation predicted earlier birth date. In line with Campbell et al. (2005)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] increasing levels of HPA axis biomarkers were relevant for determining birth date in TPL women. However, whereas they observed that stress biomarkers had a significant association with prematurity from the 28th week of gestation and onwards [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], our findings have shown this association from the 24th to 29th weeks of gestation. This discrepancy may be explained due to differences in follow-up length; whereas Campbell et al. (2005) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] carried out a follow-up until 48 hours after TPL diagnosis, our research extended it until birth. With regard to α-amylase levels, no differences have been observed, indicating that α-amylase biomarker is not a significant variable to predict birth week in symptomatic women (see Garc\u0026iacute;a-Blanco et al. (2017) for a similar finding) [28a, 28b]. In this case, previous research that found an association between α-amylase levels and preterm birth suggested that this relationship may be restricted to women with prenatal depression [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs for psychological response to TPL, unlike previous studies that found an association between preterm birth and gestational anxiety [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and depressive symptoms [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], these self-reported symptoms after a TPL diagnosis have not been relevant to estimate the birth week in our study. Thus, this apparent inconsistency may be explained by inclusion of women without TPL diagnosis in previous studies. Undoubtedly, all women with a TPL diagnosis were expected to react with a subjective increase of anxiety and depressive symptoms. Nevertheless, only some of those women have shown middle- and high- cortisol levels. Therefore, this study has shown that stress biomarkers were stronger predictors than the subjective state anxiety for TPL women. Like it occurs with anxiety symptoms, suffering traumatic experiences previously to pregnancy may be associated with preterm birth in asymptomatic women [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, such differences have not been observed in this study with symptomatic women. It could be expected that previous traumas are not relevant in this context because TPL represents a traumatic event itself. Hence, TPL can be considered as a pregnancy-specific traumatic event that may provoke a stress-vulnerability status in all cases [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConcerning chronic social stress-related factors, family adaptation was a relevant factor to estimate the birth week. Likewise, other studies with non-TPL women have concluded that some aspects of family functioning (e.g., poor emotional understanding by the partner) are related to preterm birth [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In our study, family's ability to modify its rules, roles, and structure in response to environmental changes, rather than social support in general [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], was the most relevant social predictor [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, among self-reported variables, chronic social stress has been a stronger predictor than psychological symptoms after a TPL diagnosis. Thus, TPL can trigger an increase of subjective anxiety but it cannot be a modulator of self-reported family functioning.\u003c/p\u003e \u003cp\u003eFinally, relating to obstetric variables, according to prior literature about preterm birth [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], actors such as maternal BMI and TPL week have also been relevant predictors for final birth date after a TPL in our study.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion","content":" \u003cp\u003eThis follow-up study uses a multidimensional approach to examine stress response in pregnant women with an antenatal adverse event such as TPL in order to explore potential indicators of vulnerability to preterm birth. These findings can be applied as a new useful tool to determine the birth date in TPL women by means of a simultaneous analysis of chronic social stressors (family adaptation) and the biological stress response to TPL diagnosis (salivary cortisol) together with obstetric conditions (BMI and TPL week). This study has important public health implications, since the number of preterm births may be reduced by initiating early preventive psychosocial interventions to increase family adaptation and decrease cortisol levels after a TPL as well as lose weight in symptomatic women.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are greatly indebted to all the pregnant women, their families, nursing, and medical staff who voluntarily participated in the present study. Without their collaboration and enthusiasm this study could not have been completed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee at the La Fe Health Research Institute approved the study protocol in 2015 (ref. 2015/0086) and informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollected data and materials of assessment comply with field standards and are available if required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM. Vento: Protocol/project development; Funding acquisition; Supervision; Manuscript Writing/editing.\u003c/p\u003e\n\u003cp\u003eA. Moreno-Gim\u0026eacute;nez: Data collection; Manuscript Writing/editing.\u003c/p\u003e\n\u003cp\u003eL. Campos-Berga: Data collection; Manuscript Writing/editing.\u003c/p\u003e\n\u003cp\u003eV. Diago: Protocol/project development; Funding acquisition; Supervision; Manuscript Writing/editing.\u003c/p\u003e\n\u003cp\u003eD. Herv\u0026aacute;s: Data analysis; Software; Manuscript Writing.\u003c/p\u003e\n\u003cp\u003eP. S\u0026aacute;enz: Protocol/project development; Data collection; Manuscript Writing/editing.\u003c/p\u003e\n\u003cp\u003eC. Ch\u0026aacute;fer-Peric\u0026aacute;s: Protocol/project development; Data analysis; Manuscript Writing/editing.\u003c/p\u003e\n\u003cp\u003eA. Garc\u0026iacute;a-Blanco: Protocol/project development; Funding acquisition; Data collection; Supervision; Manuscript Writing/editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to study initiation, a grant was awarded by the Instituto de Salud Carlos III (PI18/01352). To achieve this grant, the project was assessed by an external peer review panel, guaranteeing scientific quality and ethical integrity. MV acknowledges PI17/0131 grant from the Instituto de Salud Carlos III (Spanish Ministry of Economy and Competitiveness) (ISCIII; Plan Estatal de I+D+I 2013-2016) and co-financed by the European Development Regional Fund \u0026ldquo;A way to achieve Europe\u0026rdquo; (ERDF); and RETICS funded by the PN 2018-2011 (Spain), ISCIII- Sub-Directorate General for Research Assessment and Promotion and the European Regional Development Fund (FEDER), RD16/0022/0001.VD acknowledges PI18/01352 grant from the ISCIII. CC-P acknowledges a \u0026ldquo;Miguel Servet I\u0026rdquo; grant (CP16/00082) from the ISCIII. AG-B acknowledges a \u0026ldquo;Juan Rod\u0026eacute;s\u0026rdquo; grant (JR17/00003) and a health research project (PI18/01352) from the ISCIII. LC-B acknowledges a \u0026ldquo;R\u0026iacute;o Hortega\u0026rdquo; grant (CM20/00143) from the ISCIII.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVogel, J. P., Chawanpaiboon, S., Moller, A. B., Watananirun, K., Bonet, M., \u0026amp; Lumbiganon, P. (2018). The global epidemiology of preterm birth. Best Practice and Research: Clinical Obstetrics and Gynaecology 52, 3\u0026ndash;12. https://doi.org/10.1016/j.bpobgyn.2018.04.003.\u003c/li\u003e\n\u003cli\u003eLuu, T. M., Mian, M. O. R., \u0026amp; Nuyt, A. M. (2017). Long-term impact of preterm birth: neurodevelopmental and physical health outcomes. Clinics in Perinatology 44(2), 305\u0026ndash;314. https://doi.org/10.1016/j.clp.2017.01.003.\u003c/li\u003e\n\u003cli\u003eJobe, A. H. Neonatal stress and resilience \u0026mdash; lasting effects of antenatal corticosteroids (2019). Canadian Journal of Physiology and Pharmacology 97(3), 155\u0026ndash;157. https://doi.org/10.1139/cjpp-2018-0240.\u003c/li\u003e\n\u003cli\u003eRoberts, D., Brown, J., Medley, N., \u0026amp; Dalziel, S. R. (2017). Antenatal corticosteroids for accelerating fetal lung maturation for women at risk of preterm birth. The Cochrane database of systematic reviews 3(3)CD004454.. https://doi.org/10.1002/14651858.CD004454.pub3..\u003c/li\u003e\n\u003cli\u003eGarcia-Casado, J., Ye-Lin, Y., Prats-Boluda, G., Mas-Cabo, J., Alberola-Rubio, J., \u0026amp; Perales (2018). A. Electrohysterography in the diagnosis of preterm birth: a review. Psysiological Measurament 39(2). https://doi.org/10.1088/1361-6579/aaad56.\u003c/li\u003e\n\u003cli\u003eRenzo, G. C. Di, Roura, L. C., Facchinetti, F., Helmer, H., Hubinont, C., J\u0026oslash;rgensen, J. S., et al. (2017). Preterm Labor and Birth Management: Recommendations from the European Association of Perinatal Medicine Preterm Labor and Birth Management. The Journal of Maternal-Fetal \u0026amp; Neonatal Medicine 30(17), 2011\u0026ndash;2030. https://doi.org/10.1080/14767058.2017.1323860.\u003c/li\u003e\n\u003cli\u003eGazmararian, J. A., Petersen, R., Jamieson, D. J., Schild, L., Adams, M. M., Deshpande, A. D., \u0026amp; Franks, A. L (2002). Hospitalizations During Pregnancy Among Managed Care Enrollees. The American College of Obstetricians and Gynecologist 100(1), 94\u0026ndash;100. https://doi.org/10.1016/s0029-7844(02)02024-0.\u003c/li\u003e\n\u003cli\u003eGlover, A. V, \u0026amp; Manuck, T. A (2018). Seminars in Fetal \u0026amp; Neonatal Medicine Screening for spontaneous preterm birth and resultant therapies to reduce neonatal morbidity and mortality : A review. 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An International Journal of Obstetrics and Gynaecology 123 (12), 1973\u0026ndash;1982. https://doi.org/10.1111/1471-0528.13891.\u003c/li\u003e\n\u003cli\u003eDagklis, T., Tsakiridis, I., Chouliara, F., Mamopoulos, A., Rousso, D., Athanasiadis, A., \u0026amp; Papazisis, G (2018). Antenatal depression among women hospitalized due to threatened preterm labor in a high-risk pregnancy unit in Greece. Journal of Maternal-Fetal and Neonatal Medicine 31(7), 919\u0026ndash;925. https://doi.org/10.1080/14767058.2017.1301926.\u003c/li\u003e\n\u003cli\u003eField, T., Diego, M., \u0026amp; Hernandez-reif, M. (2009). Prematurity and potential predictors. International Journal of Neuroscience 118, 277\u0026ndash;298. https://doi.org/10.1080/00207450701239327.\u003c/li\u003e\n\u003cli\u003eHimes, K. P., \u0026amp; Simhan, H. N. (2011). Plasma Corticotropin-Releasing Hormone and Cortisol Concentrations and Perceived Stress among Pregnant Women with Preterm and Term Birth. American Journal of Perinatology 1(212), 443\u0026ndash;448. https://doi.org/http://dx.doi.org/10.1055/s-0030-1270119.\u003c/li\u003e\n\u003cli\u003eHoffman, M. C., Mazzoni, S. E., Wagner, B. D., Laudenslager, M. L., \u0026amp; Ross, R. G. (2016). Measures of Maternal Stress and Mood in Relation to Preterm Birth. Obstetrics and Gynecology 127(3), 545\u0026ndash;552. https://doi.org/10.1097/AOG.0000000000001287.\u003c/li\u003e\n\u003cli\u003ePearce, B. D., Grove, J., Bonney, E. A., Bliwise, N., Dudley, D. J., Schendel, D. E., \u0026amp; Thorsen, P (2010). Interrelationship of Cytokines, Hypothalamic-Pituitary-Adrenal Axis Hormones, and Psychosocial Variables in the Prediction of. Gynecologic and Obstetric Investigation 70, 40\u0026ndash;46. https://doi.org/10.1159/000284949.\u003c/li\u003e\n\u003cli\u003eRuiz, R. J., Fullerton, J., Brown, C. E. L., \u0026amp; Dudley, D. J. (2002). Predicting Risk of Preterm Birth: The Roles of Stress, Clinical Risk Factors, and Corticotropin-. Biological Research For Nursing 4, 54\u0026ndash;64. https://doi.org/10.1177/1099800402004001007.\u003c/li\u003e\n\u003cli\u003eAmerican College of Obstetricians and Gynecologists (2016). ACOG practice bulletin no. 171: Management of preterm labor. Obstetrics and Gynecology 128(4),e155-64. https://doi.org/\u003ca href=\"https://doi.org/10.1097/aog.0000000000001711\"\u003e10.1097/AOG.0000000000001711. \u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eAli, A. A., Sayed, A. K., Sherif, E., Loutfi, G. O., Mahmoud, A., Ahmed, M., et al. (2019). Systematic review and meta-analysis of randomized controlled trials of atosiban versus nifedipine for inhibition of preterm labor. International Journal Gynecology Obstectrics 145, 139\u0026ndash;148. https://doi.org/10.1002/ijgo.12793.\u003c/li\u003e\n\u003cli\u003eConde-Agudelo, A., Romero, R., \u0026amp; Kusanovic, J. P (2011). Nifedipine in the management of preterm labor: a systematic review and metaanalysis. American Journal of Obstetrics 204(2), 134.e1-134.e20. https://doi.org/10.1016/j.ajog.2010.11.038.\u003c/li\u003e\n\u003cli\u003eSavova, I. (2012). Europe 2020 Strategy-towards a smarter, greener and more inclusive EU economy? Eurostat (EU Commission) 1, ISSN 1977-0316. https://doi.org/10.2785/101636.\u003c/li\u003e\n\u003cli\u003eVrana, S., \u0026amp; Lauterbach, D (1994). Prevalence of traumatic events and post-traumatic psychological symptoms in a nonclinical sample of college students. Journal of Traumatic Stress 7(2), 289\u0026ndash;302.\u003c/li\u003e\n\u003cli\u003eZimet, G. D., Dahlem, N. W., Zimet, S. G., Gordon, K., \u0026amp; Farley, G. K (1988). The Multidimensional Scale of Perceived Social Support The Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment 52(1), 37\u0026ndash;41. https://doi.org/10.1207/s15327752jpa5201.\u003c/li\u003e\n\u003cli\u003eOlsen, D. H., Portner, J., \u0026amp; Lavee, Y. (1985). Family Adaptability and Cohesion Evaluation Scales (FACES-II). Minneapolis: University of Minnesota.\u003c/li\u003e\n\u003cli\u003eSpielberger, C. D., Gorsuch, R. L., Lushene, R., Vagg, P. R., \u0026amp; Jacobs, G. A. (1983). Manual for the state-trait anxiety inventory. Palo Alto, CA, Consulting Psychologists Press.\u003c/li\u003e\n\u003cli\u003eBeck, A. T., Rial, W. Y., \u0026amp; Rickels, K (1974). Short form of depression inventory: cross-validation. Psychological Reports.\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Blanco, A., Vento, M., Diago, V., \u0026amp; Ch\u0026aacute;fer-Peric\u0026aacute;s, C (2016). Reference ranges for cortisol and \u0026alpha;-amylase in mother and newborn saliva samples at different perinatal and postnatal periods. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences 1022, 249\u0026ndash;255. https://doi.org/10.1016/j.jchromb.2016.04.035.\u003c/li\u003e\n\u003cli\u003eGoldenberg, R.L., Culhane, J.F., Iams, J.D., \u0026amp; Romero, R (2008). Preterm Birth 1 Epidemiology and causes of preterm birth. Lancet 371,75\u0026ndash;84. https://doi.org/10.1016/S0140-6736(08)60074-4.\u003c/li\u003e\n\u003cli\u003eHarrell, F. E (2015). Describing, resampling, validating, and simplifying the model. In Regression Modeling Strategies. Springer, pp. 103\u0026ndash;126.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"archives-of-gynecology-and-obstetrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arch","sideBox":"Learn more about [Archives of Gynecology and Obstetrics](https://www.springer.com/journal/404)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/arch/default.aspx","title":"Archives of Gynecology and Obstetrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"cortisol, previous traumas, family functioning, anxiety, depressive symptoms, preterm birth","lastPublishedDoi":"10.21203/rs.3.rs-454419/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-454419/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e Preterm birth represents one of the main causes of neonatal morbimortality and a risk factor for neurodevelopmental disorders. Appropriate predictive methods for preterm birth outcome, which consequently would facilitate preventing programs, are needed. We aim to predict delivery date in women with a threatened preterm labor (TPL) based on stress response to TPL diagnosis, cumulative life stressors, and relevant obstetric variables. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA prospective cohort of 157 pregnant women with TPL diagnosis between 24 and 31weeks gestation formed the study sample. To estimate the stress response to TPL, maternal salivary cortisol, α-amylase levels, along with anxiety and depression symptoms were measured. To determine cumulative life stressors, previous traumas, social support, and family functioning were registered. Then, linear regression models were used to examine the effect of potential predictors of birth date. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The main predictors were lower family adaptation, higher Body Mass Index (BMI), higher cortisol levels and TPL diagnosis week, which showed a non-linear interaction with cortisol levels: TPL women with middle- and high-cortisol levels before 29 weeks of gestation presented an imminent labor.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e A combination of stress response to TPL diagnosis (salivary cortisol) and cumulative stressors (family adaptation) together with obstetric factors (TPL week and BMI) was the best birth date predictor. Therefore, a psychosocial therapeutic intervention program aimed to increase family adaptation and decrease cortisol levels at TPL diagnosis as well as losing weight, may prevent preterm birth in symptomatic women.\u003c/p\u003e","manuscriptTitle":"Cumulative Life Stressors and Stress Response to Threatened Preterm Labor as Birth Date Predictors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-04 22:22:57","doi":"10.21203/rs.3.rs-454419/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-05-06T03:42:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-04-30T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Archives of Gynecology and Obstetrics","date":"2021-04-29T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-04-23T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Gynecology and Obstetrics","date":"2021-04-22T11:22:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-gynecology-and-obstetrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arch","sideBox":"Learn more about [Archives of Gynecology and Obstetrics](https://www.springer.com/journal/404)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/arch/default.aspx","title":"Archives of Gynecology and Obstetrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"230d912e-a40e-4365-b565-a3d8455c6857","owner":[],"postedDate":"May 4th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":4079937,"name":"Obstetrics \u0026 Gynecology"}],"tags":[],"updatedAt":"2021-09-01T19:39:25+00:00","versionOfRecord":[],"versionCreatedAt":"2021-05-04 22:22:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-454419","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-454419","identity":"rs-454419","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}