Optimizing the effect of gestational hypertension on sex-specific neonatal anthropometric characteristics and Apgar score

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Ezemagu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6612239/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 The study aimed to investigate the effect of gestational hypertension on neonatal anthropometric characteristics and Apgar scores, with particular consideration of sex differences among neonates. Maternal and neonatal anthropometric characteristics were obtained using direct standard anthropometric protocol. Diagnosis of gestational hypertension was established based on standard clinical criteria, while socio-demographic data were collected structured questionnaire. Clinicians recorded the mode of delivery and Apgar scores of the newborns at birth. Chi-square tests, ANOVA, correlation, and stepwise-forward regression analyses were performed at a significance level of p < 0.05 (*). Place of residence and educational status shows significant associations with gestational hypertension. Maternal age, WHR, percentage body fat and Apgar score differ between the two groups (η² = 0.004–0.049*). Gestational hypertension correlated with maternal weight, height, BMI, WHR, % body fat and Apgar score of the newborn (r= -0.430–0.467). A final regression model predicted gestational hypertension based on Apgar score (adjusted R²= 0.035, p = 0.077). The study contributes to understanding the role of gestational hypertension in influencing the Apgar score of newborns, emphasizing the importance of maternal blood pressure regulation for favourable on neonatal outcomes. Understanding the effect of gestational hypertension on neonatal features has implications for the close monitoring and early intervention in the treatment and management of maternal blood pressure during pregnancy. Health sciences/Anatomy Health sciences/Health care gestational hypertension maternal BMI WHR educational status Apgar score regression Introduction Hypertension is a common chronic condition characterized by elevated systolic and diastolic blood pressure (Bernard et al., 2020 ). Gestational hypertension significantly contributes to maternal morbidity and perinatal mortality (Bailey et al., 2017 ; Lewis, 2007 ). During pregnancy, elevated blood pressure (BP) may result from physiological changes and occurs in approximately 6%-15% of pregnancies (Stephanie and Andrei, 2019 ; Khan et al. , 2011). It is defined as a systolic blood pressure (SBP) ⩾ 140 mmHg and/or a diastolic blood pressure (DBP) ⩾ 90 mmHg (Gonzalez et al., 2019; American College of Obstetricians and Gynecologists, 2013). Hypertension is associated with numerous complications, including a > 60% increased risk of stroke, accelerated biological aging, reduced uteroplacental blood flow, autonomous nervous system impairment, and preeclampsia. These complications contribute to an estimated 7.5 million deaths worldwide annually (Renata, 2023 ; Vesna et al., 2022 ; Garovic et al., 2020 ; Honigberg et al., 2019 ; Ngene and Moodley, 2018 ; Umegbolu and Ogamba, 2017 ; Shikha et al., 2017 ; Magee et al., 2016 ; Mancia et al., 2013 ). Expectant women with gestational hypertension (after 20 weeks of gestation) experience increase risk of labour induction, cesarean delivery, pregnancy loss, prolonged neonatal care (> 48 hrs), small-for-gestational age (SGA), preterm delivery, maternal death, and other various adverse obstetric outcomes (Renata, 2023 ; Magee et al., 2016 ). These complications can be mitigate through timely treatment following diagnosis, with the goal of stabilizing blood pressure to a Systolic BP of 120 mmHg and a Diastolic BP of 80 mmHg (Shekhar et al., 2016 ). In addition, close monitoring is essential to prevent complications that could lead to maternal mortality. In developing nations, several factors such as limited access to healthcare services, delayed referrals, poor documentation, inadequate team communication, financial constraints, cultural reluctant to seek care, physicians shortages, lack of health insurance, limited internet services and insufficient equipment or education negatively impact the quality of health care received and increase risk of hypertension in pregnancy (Phipps et al., 2018; Sharma et al., 2020 ; Hansen, 2020 ; Eastabrook et al., 2011 ). Excessive gestational weight gain, particularly mothers with a BMI > 30, is a well-established risk factor for hypertensive disorders in pregnancy (Momoka et al., 2023 ; Miller et al., 2020 ), with maternal age and obesity identified as the primary contributors (Lisa et al., 2024 ; Renata, 2023 ). Additionally, a large waist circumference, indicative of excess abdominal fat is associated with an increased risk of hypertension (Ariana et al., 2024 ; Ezemagu et al. , 2020). Conversely, uncontrolled hypertension combined with an excessive weight gain during pregnancy can result in preterm delivery (< 37 weeks of gestation) (Ariana et al., 2024 ). The effects of hypertension on neonatal outcomes have been documented in the previous studies (Ariana et al., 2024 ; Marta et al., 2021 ). For instance, Ariana et al. ( 2024 ) reported that preterm infants born to hypertensive mothers are at an increased risk of cardiovascular disease, insulin resistance, asthma, and neuromotor and cognitive abnormalities. However, there is limited evidence in the literature regarding neonatal anthropometric characteristics. Furthermore, most existing studies on maternal hypertension on neonates, focus on measurement protocol (American College of Obstetricians and Gynecologists, 2013), preventive and management approaches (Marta et al., 2021 ) that primarily address hypertensive mothers. This presents a significant gap in understanding the effects of hypertension on neonates. Furthermore, the literature lacks evidence on the relationship between neonatal anthropometric characteristics and maternal hypertension, particularly with respect to neonatal sex. Therefore, the aim of this study was to investigate the effect of maternal hypertension on neonatal anthropometric characteristics with consideration of neonatal sex differences. Filling the above mentioned gaps in the literature is important to improve the understanding of the relevance on the effects of hypertension on neonates while accounting for sex. Materials and Methods Participants The study involves 164 women, comprising 82 Cases (age: 30.11 ± 4.87 years, height: 1.65 ± 0.07 m, weight: 84.53 ± 15.20 kg) and 82 controls (age: 30.65 ± 5.04 years, height: 1.67 ± 0.06 m, weight: 84.35 ± 12.60 kg), conducted between June 1 and December 1, 2021. The sample size was determined using the method described by Jaykaram and Tamoghna (2013) for cases and control. Ethical approval was obtained from the Ethics and Research committee of AE-FUTHA with a reference number: AE-FUTHA/REC/VOL 3/2021/241 in accordance with the Declaration of Helsinki (2013). Informed consent was voluntarily signed before participating in the study. Inclusion criteria for cases were pregnant women diagnosed with pregnancy- induced hypertension without any sever complications. For controls, all pregnant women without hypertension were included. Women with severe complications or who did not provide consent were excluded from the study. Research Design The study implemented case-control design and collected data in a labor ward of the Obstetrics and Gynecology department at Alex Ekwueme Federal University Teaching Hospital. Abakaliki. Two types of data were obtained in different ways: first, individual information including maternal blood pressure (confirmation of hypertension), anthropometric characteristics and responses to structured questionnaires on socio-demographic characteristics, all obtained by nurses prior to the delivery stage. Second, neonatal anthropometric data were collected with the assistance of clinicians and nurses immediately following delivery. Data Collection Hypertension in pregnancy was diagnosed using a spengler cuff-mounted sphygmomanometer with a stethoscope (Max + 3, England). The cuff is correctly sized and placed snugly on the upper arm, with the stethoscope positioned over the brachial artery. Thereafter, the cuff was Inflated and the pressure was slowly released while listening to Korotkoff sounds to determine the systolic and diastolic blood pressure readings, as described by Turner et al. ( 2024 ). It is confirmed if the systolic BP (SBP) was ≥ 140 mmHg and/or diastolic BP (DBP) was ≥ 90 mmHg (O’Brien et al., 2013). Direct standard anthropometric measurements were conducted using a stadiometer with an integrated weight scale (model RGZ-160, Jiangsu Suhong Medical Instruments Co., Ltd, Shanghai, China) and non-stretchable anthropometric tape were used to measure the maternal body height, weight, BMI, and circumferences (Christen et al., 2025 ; Ezemagu et al., 2020). The percentage body fat of each mother was calculated using the formular by Swainson et al. ( 2017 ). An infant weighing scale (Bassinet: model 180) was used to measure neonatal body weight, following standardized criteria by Rustagi et al. ( 2012 ). Birth length and head circumference were measured using a non-stretchable anthropometric tape and recorded to the nearest 0.1 cm. The average of three consecutive measurements was considered. Delivery outcomes, including mode of delivery (spontaneous vagina and Cesarean) and Apgar score were recorded in a standard form adopted in Obstetric by two qualified nurses trained in midwifery who served as research assistant. The data were thoroughly checked and verified by gynecologists involved in the study. Structured questionnaires include items on marital status, place of residence, education status, occupation status, and alcohol consumption intake were administered to the mothers in the antenatal ward. The reliability and validity of the instruments were assessed before the commencement of data collection. A Case was defined as a pregnant woman diagnosed with hypertensive after 20 weeks of pregnancy (as per standard WHO definition), with a systolic blood pressure > 140 mmHg and a diastolic pressure > 90 mmHg, without any signs of eclampsia. A Control was defined as a pregnant woman who was not diagnosed with hypertension during pregnancy and was selected from the same facility. Statistical Analysis Microsoft office 2007 (Microsoft Corporation, Redmond, WA, USA) and SPSS version 23.0 (SPSS Inc. Chicago, IL) were used to organize and analyze data. Descriptive statistics were presented as mean ± standard deviation (SD) and as confidence intervals (95% CI). Normal distribution was checked via Shapiro-Wilk test, and subsequent statistical tests were selected accordingly. Chi-square determined the association between socio-demographic factors and delivery outcomes of hypertensive and normotensive mothers. Difference between maternal and neonatal anthropometrics, and maternal BP of hypertensive and normotensive mothers were tested via multivariate analysis of variance (MANOVA). The relationship between neonatal anthropometric characteristics, Apgar score and maternal BP was determined through spearman correlation. For regression analysis to estimate delivery outcomes based on individual blood pressure and anthropometric characteristics, co-linearity between independent variables was excluded. Effect sizes were provided as correlation coefficient (r), partial eta squared (η²), and coefficient of determination (adjusted R²) for correlation, difference, and regression analysis, respectively. For all tests, statistical significance was set at p < 0.05 Results Chi-square results were presented in Table 1 . In maternal anthropometrics and blood pressure (4 out of 13 variables) show significant difference while for neonatal anthropometrics and Apgar score (1 out of 4 variables) shows significant difference in the two groups in Table 2 . Significant correlation was confirmed between Apgar score and maternal SBP and DBP in Table 3 . After removing co-linearity, the final regression model (adjusted R²=0.035, p = 0.077) derived the following equation: mode of delivery = 2.638 Apgar score – 0.016 SBP (mmHg) + 0.001 DBP ( mmHg ) (Apgar score: 1 = normal, 0 = abnormal) Table 1 Chi-square of socio-demographic characteristics and delivery outcomes of hypertensive and normotensive mothers Variables Hypertensive mothers (%) Normotensive mothers (%) Chi-Square p-value General socio-demographic characteristics Sex Male neonates 44(53.66) 51(62.20) 18.403 0.104 Female neonates 38(46.34) 31(37.80) Residence Rural 14(17.07) 5(6.10) 15.080 0.020 Semi-Urban 5(6.10) 5(6.10) Urban 63(76.83) 72(87.80) Educational status Primary 14(17.07) 8(9.76) 21.533 0.006 Secondary 17(20.73) 15(18.29) Tertiary 51(62.20) 59(71.95) Occupation Civil servant 29(35.37) 27(32.93) 23.220 0.806 Farming 10(12.20) 12(14.63) Labour 6(7.32) 8(9.76) Trading 31(37.80) 23(28.05) Student 6(7.32) 12(14.63) Alcoholic consumption intake Yes 10(12.20) 3(3.66) 1.207 0.547 No 72(87.80) 79(96.34) Experience of any form of disease Yes 19(23.17) 5(6.10) 48.012 < 0.001 No 63(76.83) 77(93.90) Delivery outcomes Mode of delivery Vagina spontaneous 40(48.78) 43(52.44) 5.165 0.523 Cesarean 42(51.22) 39(47.56) Apgar score Normal 56(68.29) 77(93.90) 0.614 0.736 Abnormal 26(31.71) 5(6.10) Table 2 Difference of anthropometrics and blood pressure of hypertensive and normotensive mothers, and neonatal anthropometrics Variables Hypertension (cases) Normotensive (control) η² p-value Mean ± SD 95% CI Mean ± SD 95% CI General maternal anthropometries Age (years) 30.11 ± 4.87 29.06–31.16 30.65 ± 5.04 29.56–31.74 2.038 0.049 Gestational age (weeks) 39.00 ± 1.68 38.64–39.36 38.48 ± 1.89 38.07–38.89 0.865 0.543 Height (m) 1.65 ± 0.07 1.63–1.67 1.67 ± 0.06 1.66–1.68 0.552 0.914 Weight (kg) 84.53 ± 15.20 81.24–87.82 84.35 ± 12.60 81.65–87.05 1.162 0.385 BMI (kg/m 2 ) 30.79 ± 4.45 29.83–31.72 30.31 ± 3.99 29.45–31.17 1.816 0.093 Waist circumference (cm) 90.76 ± 3.84 89.93–91.59 90.26 ± 4.32 89.33–91.19 0.729 0.749 Hip circumference (cm) 96.24 ± 5.11 95.13–97.35 95.20 ± 5.08 94.10–96.30 0.521 0.925 WHR 0.97 ± 0.04 0.96–0.98 0.97 ± 0.04 0.96–0.98 2.720 0.014 % body fat 37.65 ± 4.44 36.72–38.61 37.86 ± 4.97 38.95–38.94 0.372 0.004 Height of fundus (cm) 37.73 ± 2.61 37.17–38.29 37.76 ± 3.05 37.10–38.42 1.180 0.338 Maternal Blood pressure Systolic blood pressure (mmHg) 141.51 ± 31.25 134.75–148.27 109.04 ± 10.34 106.80–111.28 2.743 0.024 Diastolic blood pressure (mmHg) 97.62 ± 20.36 93.22–102.02 86.67 ± 9.80 84.55–88.79 1.120 0.373 Pulse pressure 43.89 ± 17.52 40.10–47.68 22.37 ± 9.36 20.35–24.40 0.590 0.779 Neonatal anthropometries Weight (kg) 3.15 ± 0.56 3.03–3.27 3.17 ± 0.50 3.06–3.28 1.044 0.451 Length (cm) 48.69 ± 2.74 48.10–49.28 48.52 ± 3.29 47.81–49.23 0.964 0.486 Head circumference (cm) 34.80 ± 1.89 34.39–35.21 34.82 ± 1.96 34.40–35.24 1.064 0.412 Apgar score 7.49 ± 2.28 7.00–7.98 8.57 ± 1.17 8.32–8.82 2.273 0.033 Table 3 Correlation between maternal gestational hypertension, maternal and neonatal anthropometrics and Apgar score Variables Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Pulse pressure r p r p r p Maternal Anthropometrics Age (years) -0.232 0.125 -0.153 0.315 -0.197 0.195 Gestational age (weeks) 0.134 0.381 0.139 0.363 0.069 0.653 Height (m) 0.421** 0.004 0.323* 0.030 0.446** 0.002 Weight (kg) 0.467** 0.001 0.446** 0.002 0.398** 0.007 BMI (kg/m 2 ) 0.368* 0.013 0.414** 0.005 0.347* 0.020 Waist circumference (cm) 0.210 0.167 0.070 0.649 0.439** 0.003 Hip circumference (cm) 0.209 0.168 0.096 0.530 0.411** 0.005 WHR -0.430** 0.003 -0.453** 0.002 -0.407** 0.005 % body fat 0.309* 0.039 0.345* 0.020 0.307* 0.040 Height of fundus (cm) -0.009 0.952 0.009 0.951 -0.023 0.879 Neonatal anthropometrics Weight (kg) -0.188 0.074 -0.117 0.271 -0.188 0.074 Length (cm) -0.017 0.876 -0.024 0.824 -0.004 0.968 Head circumference (cm) -0.004 0.967 0.029 0.782 -0.085 0.422 Apgar score -0.209* 0.046 -0.207* 0.050 -0.123 0.244 Discussion The determination of maternal blood pressure is a valuable tool for evaluating and predicting birth outcomes (Gunderson et al., 2023 ). This observational hospital-based case-control study investigated the impact of gestational hypertension on neonatal anthropometric characteristics and delivery outcomes. Additionally, the study provides insight into the influence of maternal anthropometric characteristics and blood pressure on the neonatal Apgar score. Major findings indicated significant differences in maternal age, WHR, percentage body fat, and systolic blood pressure between hypertensive and normotensives mothers. The result confirmed significant association between residence, educational status and experience of disease condition among the two groups. In addition, among all variables, the Apgar score of neonates correlated with the systolic and diastolic blood pressure of hypertensive mothers. Other neonatal anthropometries negatively correlated with systolic and diastolic BP of hypertensive mothers. The study developed a regression model to pinpoint the most influential maternal characteristics and determine key factors associated with delivery outcomes. The study identified key socio-demographic factors associated with gestational hypertension, including place of residence, educational status and experience of any form diseases during pregnancy. For instance, women with relatively higher education levels have a lower risk of gestational hypertension, likely due to greater knowledge and awareness of the condition from early pregnancy, particularly in resource-limited settings like Sub-Saharan Africa. This aligns with the findings of Kosar et al. ( 2022 ) who observed that educational interventions contribute to increase knowledge of hypertensive disorder during pregnancy. It was currently observed in the present study that attaining a higher level of education facilitates regular antenatal visits, improves the ability to recognize symptoms related to hypertension, and increase compliance with established obstetric protocol. Furthermore, minimal misconceptions were observed, supporting women understanding of the treatment and management of hypertension, thereby reducing birth complications. This is in agreement with Tamma et al. ( 2023 ), who found that a high burden of misconceptions negatively affects expectant women’s perception of hypertension, leading them to disengage from treatment and management protocols. Based on these findings, the study recommends the incorporation of telemedicine for more frequent assessments and timely referral of pregnant women with gestational hypertension to facilities equipped to provide optimal delivery outcomes. Additionally, clinicians should be trained to provide culturally and linguistically competent care to build trust among pregnant patients, particularly as it pertains to gestational hypertension. A holistic approach integrating place of residence into intervention strategies appears reasonable for effective health planning. Residence status contributes meaningfully to birth outcomes and is relevant in assessing newborn health. Conversely, newborns of mothers residing in rural areas are more likely to experience complications, underscoring the role of residence in healthcare accessibility, quality of care, food security and socio-economic disparity (Nicholls-Dempsey et al., 2023 ; Mainous et al., 2004 ). The current study observed a strong association between maternal residence and the occurrence of maternal hypertension. As currently observed, expectant women who visited the hospital at the period of this study predominantly resided in urban areas, which facilitated access to obstetric services and awareness of self-monitored blood pressure, contributing to high prevalence of spontaneous vagina delivery. Furthermore, expectant mothers residing in urban areas receive more adequate education on the treatment and management of hypertension during pregnancy than their rural counterparts. The present study observed a relationship between maternal anthropometries and blood pressure among hypertensive mothers. Consequently, the findings reveal an intricate relationship between maternal anthropometric characteristics, blood pressure status, and delivery outcomes, emphasizing the need for close motoring of blood pressure level during pregnancy. The study identifies a significant relationship between BMI and maternal hypertension, highlighting an important finding in understanding risk factors associated with gestational hypertension. For instance, BMI a widely accepted anthropometric index that measures general obesity has been closely linked to the risks of hypertension disorders and preeclampsia during pregnancy (Dantas et al., 2013 ; Marshall et al., 2012 ; Tsai et al., 2012 ). A high BMI, indicating obesity, is consequently associated with adverse birth outcomes. Additionally, other maternal anthropometric factors in the current study, such as WHR and percentage body fat were found to correlate with gestational hypertension. The strong association between WHR and gestational hypertension may be attributed to accumulation of visceral and intra-abdominal fat in expectant mothers. This type of fat is a sensitive predictive characteristic, closely linked to an increased risk of hypertension during pregnancy. This is corroborated by the study of Mahboubeh et al. ( 2015 ), who observed that WHR has high sensitivity in predicting the risk of pre-eclampsia, primarily due to accumulation of intra-abdominal fat. A more moderate WHR may facilitate positive pregnancy outcome thereby reducing risk of maternal and neonatal mortality. Although some maternal anthropometrics did not significantly correlate with gestational hypertension in the current study, it is important to note that these characteristics remain crucial for pregnancy outcomes. The association between maternal age and hypertension is well documented. For instance, Romy et al. ( 2011 ) observed an increased risk of gestational hypertensive disorders with advancing maternal age. However, in the current study, maternal age fell within the reproductive age range, with hypertensive women exhibiting significantly higher maternal age. Furthermore, women of reproductive age have higher odds of preterm delivery, chorioamnionitics and endometritirs (Cavazos-Rehg et al., 2015 ). In the present study, Apgar score correlated with maternal blood pressure among hypertensive mothers, with 56% of neonates recording a normal > 7 Apgar score. Supporting this finding, studies have shown that gestational hypertension significantly associated with lower Apgar score in newborns compared to those born to normotensive women (Yunxia et al., 2024 ), often resulting from the premature termination of pregnancy. The current linear regression model revealed that, gestational hypertension accounted for 3.5% of the variance in Apgar score. Most other newborn anthropometric characteristics, such as body weight and length, and head circumference did not provide additional information beyond what was captured by sex, as they co-correlated with sex while showed weaker correlation with gestational hypertension. Ardently, intravenous labetalol was observed to be prescribed as a safe first-line antihypertensive medication, alongside clinician’s recommendations for the consumption of non-processed foods with normal sodium content. This is similar to the studies that recommended intravenous hydralazine, intravenous labetalol, and calcium channel blockers as effective antihypertensive medications (Mariana et al., 2025 ; Eustace et al., 2023 ; Shekhar et al., 2016 ). Limitation This study was hospital-based and limited to a single visit per participant, follow up of the subjects was not possible. Additionally, it was conducted in only one region, which may limit generalizability of the findings to other settings. In addition, relying only on a single interviewer could have introduced bias into the data collection process. The study did not investigate confounding factors such as physical activity, diet, cholesterol, pre-pregnancy body mass index, pre-pregnancy diabetes, and history of autoimmune disease which may be associated with the variations in gestational hypertension and should be considered when interpreting the findings. Practical Implication They are several ways of applying the key findings to reduce the risk of gestation hypertension and optimize delivery outcomes. For instance, the significant relationship between Apgar score and gestational hypertension in the present study indicates that close monitoring and early intervention of maternal gestational hypertension can play a crucial role in ensuring normal Apgar score of in newborn. Additionally, this study lays the groundwork for further exploration of hypertension factors and promotes an integrative perspective on maternal health that incorporates anthropometries and sociocultural aspects. Understanding the predictors of pregnancy-induced hypertension is crucial in clinical practice, as it will facilitate the prioritization of interventions, implementation policies and allocation of resources accordingly. Conclusion The study provides insight into the role of hypertensive maternal anthropometries and socio-demographic factors on neonatal anthropometrics and Apgar score, revealing the significant correlation between maternal BMI, WHR and % body fat, and Apgar score with gestational hypertension. These findings offer crucial guidance for clinicians in tailoring programs that emphasize the importance of early screening, effective management system, and frequent monitoring during pregnancy to improve both maternal and fetal outcomes. The present analysis strongly supports the idea that regulation of maternal blood pressure with particular attention to gestational hypertension will contribute to optimal fetal outcomes. Declarations Conflicts of Interests There are no conflicts of interests to be reported. Funding Authors declare no funding Author Contribution Conceptualization: UGC and EO; Methodology: UGC, EO, UKE and POE; Statistical analysis: UGC; Data collection: EO, POE, CA and UFU; Manuscript draft preparation: UGC, UKE and POE; Manuscript writing: UGC, UKE and POE; Review and editing: UGC and POE; Supervision: UGC. Approval to submit to your journal: All authors Acknowledgement We wish to acknowledge the staff of Gynecology and Obstetrics department for their support and guidance throughout the period of the study Data Availability All data generated or analysed during this study are included in this manuscript [and its supplementary information files] References Bernard CMY, Benjamin OR, Yue F, Man-Fung T. Korean Cycle Journal. 2020; 50(6):e7010 Bailey PE, Andualem W, Brun M. Lynn F, Sourou G, Malick K, Emily K, Edwin L, Allisyn CM, Halima M, Dahada Ould el J, Kavita S . 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Best Pract Res Clin Obstet Gynaecol. 2011;25:435–47 Momoka I, Hyo K, Tomoko Y, Misa S, Tsuyoshi M, Tsuyoshi H, Fumihiro I, Daisuke S, Toma F, Shun Y, Keiya F, Yasuhisa N. Association between Gestational Weight Gain and Risk of Hypertensive Disorders of Pregnancy among Women with Obesity: A Multicenter Retrospective Cohort Study in Japan. Nutrients. 2023;15(11):2428. doi: 10.3390/nu15112428 Miller MJ, Butler P, Gilchriest J, Taylor A, Lutgendorf MA. Implementation of a standardized nurse initiated protocol to manage severe hypertension in pregnancy. J Matern Fetal Neonatal Med. 2020;33(6):1008-1014. Lisa K, Meabh M, Kelly-Ann E. Global trends in prevalence of maternal overweight and obesity: A systematic review and meta-analysis of routinely collected data retrospective cohorts. International Journal of Population Data Science. 2024;9(2) Ariana T, Apoorva S, Maria CG. Hypertensive Disorders of Pregnancy: A Literature Review – Pathophysiology, Current Management, Future Perspectives, and Healthcare Disparities. Journal of Asian Pacific Society of Cardiology. 2024; doi.org/10.15420/usc.2023.01 Ezemagu UK, Uzomba GC, Ubochi C, Ogbu R, Egba F, Olisa O. Maternal and neonatal anthropometric analysis: Determining birth outcomes in low-risk pregnancies at Alex Ekwueme Federal University Teaching Hospital, Abakaliki. Int J Gynaecol Obstet. 2021;154(2):324-330. doi: 10.1002/ijgo.13527. Marta DR de M, Paulo RM, Karina NC , Maria RCGN. Hypertension induced by pregnancy and neonatal outcome: Results from a retrospective cohort study in preterm under 34 weeks. PLoS One. 2021 18;16(8):e0255783. doi: 10.1371/journal.pone.0255783 Jaykaran C, Tamoghna B. How to Calculate Sample Size for Different Study Designs in Medical Research_.html. Indian Journal of Psychological Medicine. 2013; 35, 121-126. doi.org/10.4103/0253-7176.116232 Turner JM, Catherine S, Noel B. Sphygmomanometer calibration Why, how and how often? Australian Family Physician. 2024; 36(10):834-8 O'Brien E, Gianfranco P, George S, Roland A, Laurie B, Grzegorz B, Denis C, Alejandro de la S, Peter de L, Eamon D, Robert F, John G, Geoffrey AH, Yutaka I, Kazuomi K, Empar L, Jean-Michel M, Giuseppe M, Thomas M, Martin M, Gbenga O, Takayoshi O, Stefano O, Paolo P, Josep R, Luis MR, Andrew S, Jan AS, Gert van M, Paolo V, Bernard W, Jiguang W, Alberto Z, Yuqing Z. European Society of Hypertension position paper on ambulatory blood pressure monitoring. J Hypertens. 2013 Sep;31(9):1731-68.doi: 10.1097/HJH.0b013e328363e964. Christen RE, Niren RM, Anil AC. Anthropometric measurements as predictors of nutritional status in black South African women during pregnancy. The Journal of Obstetric and Gynecology Research. 2025; 5(1). e16184 doi.org/10.1111/jog.16184 Swainson MG, Batterham AM, Tsakirides C, Rutherford ZH, Hind K (2017) Prediction of whole-body fat percentage and visceral adipose tissue mass from five anthropometric variables. PLoS ONE 12(5): e0177175. doi.org/10.1371/journal.pone.0177175 Rustagi N, Prasuna J, Taneja D. Anthropometric surrogates for screening of low birth weight newborns: a community-based study. Asia Pacific J Public Health . 2012;24(2):343–351. doi: 10.1177/1010539510384717 Gunderson EP, Mara G, Baiyang S, Nancy G, Alan SG, James MR, Nguyen-Huynh MN, Wei T, Stacey EA. Early Pregnancy Systolic Blood Pressure Patterns Predict Early- and Later-Onset Preeclampsia and Gestational Hypertension Among Ostensibly Low-to-Moderate Risk Groups. Journal of the American Heart Association.2023; 12, 15doi.org/10.1161/JAHA.123.029617 Kosar G, Narges N, Meraj K, Hamidreza G, Atieh JA, Shamim K, Mahdi SF, Maryam D, Yasaman S, Amirmohammad K, Sara H, Niloofar D. Impact of Educational Interventions on Knowledge About Hypertensive Disorders of Pregnancy Among Pregnant Women: A Systematic Review. Front Cardiovasc Med. 2022;9:886679. doi: 10.3389/fcvm.2022.886679 Tamma E, Adu-Bonsaffoh K, Nwameme A, Dako-Gyeke P, Srofenyoh E, Browne J. Maternal hypertensive mother’s knowledge, attitudes and misconceptions on hypertension in pregnancy: A multi-center qualitative study in Ghana. PLOS Glob Public Health. 2023; 3(1): e0001456. doi.org/10.1371/journal.pgph.0001456 Nicholls-Dempsey L, Ahmad B, Haitham B, Michael HD. How does high socioeconomic status affect maternal and neonatal pregnancy outcomes? A population-based study among American women. Eur J Obstet Gynecol Reprod Biol X. 2023;20:100248. doi: 10.1016/j.eurox.2023.100248 Mainous AG, King DE, Garrr DR, Pearson WS. Race, rural residence, and control of diabetes and hypertension. Ann Fam Med, 2004; 2, pp. 563-568 Dantas EM, Pereira FV, Queiroz JW, Dantas DL, Monteiro GR, Duggal P, Azevedo Mde F, Jeronimo SM, Araújo AC . Preeclampsia is associated with increased maternal body weight in a northeastern Brazilian population. BMC Pregnancy Childbirth. 2013; 13: 159 Marshall NE, Guild C, Cheng YW, Caughey AB, Halloran DR. Maternal superobesity and perinatal outcomes. Am J Obstet Gynecol. 2012; 206: 417.e1–417.e6. Tsai IH, Chen CP, Sun FJ, Wu CH, Yeh SL. Associations of the pre-pregnancy body mass index and gestational weight gain with pregnancy outcomes in Taiwanese women. Asia Pac J Clin Nutr 2012; 21: 82–87 Mahboubeh T, Zohreh S, Farzaneh S, Masoumeh AK. Early pregnancy waist-to-hip ratio and risk of preeclampsia: a prospective cohort study. Hypertension Research, 2015, 38, 80–83 Romy G, Rachel B, Eric APS, Albert H, Vincent WVJ. Maternal Age During Pregnancy Is Associated With Third Trimester Blood Pressure Level: The Generation R Study. American Journal of Hypertension , 2011; 24, 9, 1046–1053, doi.org/10.1038/ajh.2011.95 Cavazos-Rehg PA, Melissa JK, Edward LS, Kerry B, Tessa M, Margaret AO, Harini S, Jeffrey FP, Laura JB. Maternal age and risk of labor and delivery complications. Matern Child Health J. 2015;19(6):1202–1211. doi: 10.1007/s10995-014-1624-7 Yunxia W, Bihong C, Jiuju Z, Shuang Y, Chun W, Yongzhong G, Jinlai M. Risk Factors Associated with Low Apgar Scores in Pregnancies Complicated by Severe Preeclampsia: A Case–Control Study. Clin. Exp. Obstet. Gynecol. 2024; 51(12): 264 doi.org/10.31083/j.ceog5112264 Mariana CS, Costa-Filho RC, Vanessa E. Strategic Use of Intravenous Medications to Protect Target Organs in Hypertensive Emergencies Int J Cardiovasc Sci. 2025; 38:e20240117 Eustace E, Okelue EO, Maame AEB, Rafia A, Namtor NIA, Lilian B, Patience NN, Papa KAB, Onyinyechukwu BN, Caroline CO. Comparing Intravenous Labetalol and Intravenous Hydralazine for Managing Severe Gestational Hypertension. Cureus. 2023;15(7):e42332. doi: 10.7759/cureus.42332 Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYINFORMATION.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 29 Aug, 2025 Editor invited by journal 28 May, 2025 Editor assigned by journal 26 May, 2025 Submission checks completed at journal 10 May, 2025 First submitted to journal 10 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Ezemagu","email":"","orcid":"","institution":"Alex Ekwueme Federal University Ndufu-Alike","correspondingAuthor":false,"prefix":"","firstName":"Uchenna","middleName":"K.","lastName":"Ezemagu","suffix":""},{"id":509549989,"identity":"218e2a7d-871f-49de-8113-10a82db5171f","order_by":4,"name":"Paul O. Ezeonu","email":"","orcid":"","institution":"Alex Ekwueme Federal University Ndufu-Alike","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"O.","lastName":"Ezeonu","suffix":""},{"id":509549990,"identity":"0b2a447a-1017-49c3-bb50-389a739c2af7","order_by":5,"name":"Ukpai Florence Uka","email":"","orcid":"","institution":"First Moscow State Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ukpai","middleName":"Florence","lastName":"Uka","suffix":""}],"badges":[],"createdAt":"2025-05-07 12:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6612239/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6612239/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90786035,"identity":"24a0ec1a-7be7-4446-ab9b-d18c782303b2","added_by":"auto","created_at":"2025-09-08 07:08:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":844206,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6612239/v1/708f5429-e2be-4967-af73-93dd786b9017.pdf"},{"id":90784869,"identity":"d5da0bd8-9c60-4789-9e69-e149f03e54f8","added_by":"auto","created_at":"2025-09-08 06:52:26","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":47784,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYINFORMATION.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6612239/v1/25ab6221ce0e2b254aab3c68.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing the effect of gestational hypertension on sex-specific neonatal anthropometric characteristics and Apgar score","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension is a common chronic condition characterized by elevated systolic and diastolic blood pressure (Bernard et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Gestational hypertension significantly contributes to maternal morbidity and perinatal mortality (Bailey et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lewis, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). During pregnancy, elevated blood pressure (BP) may result from physiological changes and occurs in approximately 6%-15% of pregnancies (Stephanie and Andrei, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Khan \u003cem\u003eet al.\u003c/em\u003e, 2011). It is defined as a systolic blood pressure (SBP) ⩾ 140 mmHg and/or a diastolic blood pressure (DBP) ⩾ 90 mmHg (Gonzalez et al., 2019; American College of Obstetricians and Gynecologists, 2013). Hypertension is associated with numerous complications, including a\u0026thinsp;\u0026gt;\u0026thinsp;60% increased risk of stroke, accelerated biological aging, reduced uteroplacental blood flow, autonomous nervous system impairment, and preeclampsia. These complications contribute to an estimated 7.5\u0026nbsp;million deaths worldwide annually (Renata, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vesna et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Garovic et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Honigberg et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ngene and Moodley, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Umegbolu and Ogamba, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shikha et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Magee et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mancia et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eExpectant women with gestational hypertension (after 20 weeks of gestation) experience increase risk of labour induction, cesarean delivery, pregnancy loss, prolonged neonatal care (\u0026gt;\u0026thinsp;48 hrs), small-for-gestational age (SGA), preterm delivery, maternal death, and other various adverse obstetric outcomes (Renata, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Magee et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These complications can be mitigate through timely treatment following diagnosis, with the goal of stabilizing blood pressure to a Systolic BP of 120 mmHg and a Diastolic BP of 80 mmHg (Shekhar et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition, close monitoring is essential to prevent complications that could lead to maternal mortality. In developing nations, several factors such as limited access to healthcare services, delayed referrals, poor documentation, inadequate team communication, financial constraints, cultural reluctant to seek care, physicians shortages, lack of health insurance, limited internet services and insufficient equipment or education negatively impact the quality of health care received and increase risk of hypertension in pregnancy (Phipps et al., 2018; Sharma et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hansen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Eastabrook et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eExcessive gestational weight gain, particularly mothers with a BMI\u0026thinsp;\u0026gt;\u0026thinsp;30, is a well-established risk factor for hypertensive disorders in pregnancy (Momoka et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Miller et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with maternal age and obesity identified as the primary contributors (Lisa et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Renata, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, a large waist circumference, indicative of excess abdominal fat is associated with an increased risk of hypertension (Ariana et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ezemagu \u003cem\u003eet al.\u003c/em\u003e, 2020). Conversely, uncontrolled hypertension combined with an excessive weight gain during pregnancy can result in preterm delivery (\u0026lt;\u0026thinsp;37 weeks of gestation) (Ariana et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe effects of hypertension on neonatal outcomes have been documented in the previous studies (Ariana et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Marta et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, Ariana et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported that preterm infants born to hypertensive mothers are at an increased risk of cardiovascular disease, insulin resistance, asthma, and neuromotor and cognitive abnormalities. However, there is limited evidence in the literature regarding neonatal anthropometric characteristics. Furthermore, most existing studies on maternal hypertension on neonates, focus on measurement protocol (American College of Obstetricians and Gynecologists, 2013), preventive and management approaches (Marta et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that primarily address hypertensive mothers. This presents a significant gap in understanding the effects of hypertension on neonates. Furthermore, the literature lacks evidence on the relationship between neonatal anthropometric characteristics and maternal hypertension, particularly with respect to neonatal sex. Therefore, the aim of this study was to investigate the effect of maternal hypertension on neonatal anthropometric characteristics with consideration of neonatal sex differences. Filling the above mentioned gaps in the literature is important to improve the understanding of the relevance on the effects of hypertension on neonates while accounting for sex.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eThe study involves 164 women, comprising 82 Cases (age: 30.11\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87 years, height: 1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 m, weight: 84.53\u0026thinsp;\u0026plusmn;\u0026thinsp;15.20 kg) and 82 controls (age: 30.65\u0026thinsp;\u0026plusmn;\u0026thinsp;5.04 years, height: 1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 m, weight: 84.35\u0026thinsp;\u0026plusmn;\u0026thinsp;12.60 kg), conducted between June 1 and December 1, 2021. The sample size was determined using the method described by Jaykaram and Tamoghna (2013) for cases and control. Ethical approval was obtained from the Ethics and Research committee of AE-FUTHA with a reference number: AE-FUTHA/REC/VOL 3/2021/241 in accordance with the Declaration of Helsinki (2013). Informed consent was voluntarily signed before participating in the study. Inclusion criteria for cases were pregnant women diagnosed with pregnancy- induced hypertension without any sever complications. For controls, all pregnant women without hypertension were included. Women with severe complications or who did not provide consent were excluded from the study.\u003c/p\u003e\u003cp\u003eResearch Design\u003c/p\u003e\u003cp\u003eThe study implemented case-control design and collected data in a labor ward of the Obstetrics and Gynecology department at Alex Ekwueme Federal University Teaching Hospital. Abakaliki. Two types of data were obtained in different ways: first, individual information including maternal blood pressure (confirmation of hypertension), anthropometric characteristics and responses to structured questionnaires on socio-demographic characteristics, all obtained by nurses prior to the delivery stage. Second, neonatal anthropometric data were collected with the assistance of clinicians and nurses immediately following delivery.\u003c/p\u003e\u003cp\u003eData Collection\u003c/p\u003e\u003cp\u003eHypertension in pregnancy was diagnosed using a spengler cuff-mounted sphygmomanometer with a stethoscope (Max\u0026thinsp;+\u0026thinsp;3, England). The cuff is correctly sized and placed snugly on the upper arm, with the stethoscope positioned over the brachial artery. Thereafter, the cuff was Inflated and the pressure was slowly released while listening to Korotkoff sounds to determine the systolic and diastolic blood pressure readings, as described by Turner et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is confirmed if the systolic BP (SBP) was \u0026ge;\u0026thinsp;140 mmHg and/or diastolic BP (DBP) was \u0026ge;\u0026thinsp;90 mmHg (O\u0026rsquo;Brien et al., 2013).\u003c/p\u003e\u003cp\u003eDirect standard anthropometric measurements were conducted using a stadiometer with an integrated weight scale (model RGZ-160, Jiangsu Suhong Medical Instruments Co., Ltd, Shanghai, China) and non-stretchable anthropometric tape were used to measure the maternal body height, weight, BMI, and circumferences (Christen et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ezemagu et al., 2020). The percentage body fat of each mother was calculated using the formular by Swainson et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). An infant weighing scale (Bassinet: model 180) was used to measure neonatal body weight, following standardized criteria by Rustagi et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Birth length and head circumference were measured using a non-stretchable anthropometric tape and recorded to the nearest 0.1 cm. The average of three consecutive measurements was considered.\u003c/p\u003e\u003cp\u003eDelivery outcomes, including mode of delivery (spontaneous vagina and Cesarean) and Apgar score were recorded in a standard form adopted in Obstetric by two qualified nurses trained in midwifery who served as research assistant. The data were thoroughly checked and verified by gynecologists involved in the study.\u003c/p\u003e\u003cp\u003eStructured questionnaires include items on marital status, place of residence, education status, occupation status, and alcohol consumption intake were administered to the mothers in the antenatal ward. The reliability and validity of the instruments were assessed before the commencement of data collection.\u003c/p\u003e\u003cp\u003eA Case was defined as a pregnant woman diagnosed with hypertensive after 20 weeks of pregnancy (as per standard WHO definition), with a systolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;140 mmHg and a diastolic pressure\u0026thinsp;\u0026gt;\u0026thinsp;90 mmHg, without any signs of eclampsia. A Control was defined as a pregnant woman who was not diagnosed with hypertension during pregnancy and was selected from the same facility.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eMicrosoft office 2007 (Microsoft Corporation, Redmond, WA, USA) and SPSS version 23.0 (SPSS Inc. Chicago, IL) were used to organize and analyze data. Descriptive statistics were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and as confidence intervals (95% CI). Normal distribution was checked via Shapiro-Wilk test, and subsequent statistical tests were selected accordingly. Chi-square determined the association between socio-demographic factors and delivery outcomes of hypertensive and normotensive mothers. Difference between maternal and neonatal anthropometrics, and maternal BP of hypertensive and normotensive mothers were tested via multivariate analysis of variance (MANOVA). The relationship between neonatal anthropometric characteristics, Apgar score and maternal BP was determined through spearman correlation. For regression analysis to estimate delivery outcomes based on individual blood pressure and anthropometric characteristics, co-linearity between independent variables was excluded. Effect sizes were provided as correlation coefficient (r), partial eta squared (η\u0026sup2;), and coefficient of determination (adjusted R\u0026sup2;) for correlation, difference, and regression analysis, respectively. For all tests, statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eChi-square results were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In maternal anthropometrics and blood pressure (4 out of 13 variables) show significant difference while for neonatal anthropometrics and Apgar score (1 out of 4 variables) shows significant difference in the two groups in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Significant correlation was confirmed between Apgar score and maternal SBP and DBP in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eAfter removing co-linearity, the final regression model (adjusted R\u0026sup2;=0.035, p\u0026thinsp;=\u0026thinsp;0.077) derived the following equation: \u003cem\u003emode of delivery\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.638 \u003cem\u003eApgar score\u003c/em\u003e \u0026ndash; 0.016 \u003cem\u003eSBP (mmHg)\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.001 \u003cem\u003eDBP\u003c/em\u003e (\u003cem\u003emmHg\u003c/em\u003e) (Apgar score: 1\u0026thinsp;=\u0026thinsp;normal, 0\u0026thinsp;=\u0026thinsp;abnormal)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChi-square of socio-demographic characteristics and delivery outcomes of hypertensive and normotensive mothers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHypertensive mothers (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNormotensive mothers (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChi-Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"16\" rowspan=\"17\"\u003e\u003cp\u003eGeneral socio-demographic characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale neonates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44(53.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51(62.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e18.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale neonates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38(46.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31(37.80)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eResidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14(17.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5(6.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e15.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSemi-Urban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5(6.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5(6.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63(76.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72(87.80)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eEducational status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14(17.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8(9.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e21.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17(20.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15(18.29)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51(62.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59(71.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCivil servant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29(35.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27(32.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e23.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.806\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFarming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10(12.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12(14.63)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLabour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6(7.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8(9.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTrading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31(37.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23(28.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStudent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6(7.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12(14.63)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAlcoholic consumption intake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10(12.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3(3.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72(87.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e79(96.34)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExperience of any form of disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19(23.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5(6.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e48.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63(76.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77(93.90)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDelivery outcomes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMode of delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVagina spontaneous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40(48.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43(52.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e5.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCesarean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42(51.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39(47.56)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eApgar score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56(68.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77(93.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26(31.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5(6.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifference of anthropometrics and blood pressure of hypertensive and normotensive mothers, and neonatal anthropometrics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"14\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eHypertension (cases)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eNormotensive (control)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eη\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eGeneral maternal anthropometries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.11\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e29.06\u0026ndash;31.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.65\u0026thinsp;\u0026plusmn;\u0026thinsp;5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e29.56\u0026ndash;31.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e2.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGestational age (weeks)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e38.64\u0026ndash;39.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e38.07\u0026ndash;38.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeight (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.63\u0026ndash;1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e1.66\u0026ndash;1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.53\u0026thinsp;\u0026plusmn;\u0026thinsp;15.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e81.24\u0026ndash;87.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.35\u0026thinsp;\u0026plusmn;\u0026thinsp;12.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e81.65\u0026ndash;87.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.79\u0026thinsp;\u0026plusmn;\u0026thinsp;4.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e29.83\u0026ndash;31.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e29.45\u0026ndash;31.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWaist circumference (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e89.93\u0026ndash;91.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e90.26\u0026thinsp;\u0026plusmn;\u0026thinsp;4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e89.33\u0026ndash;91.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHip circumference (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.24\u0026thinsp;\u0026plusmn;\u0026thinsp;5.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e95.13\u0026ndash;97.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95.20\u0026thinsp;\u0026plusmn;\u0026thinsp;5.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e94.10\u0026ndash;96.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.96\u0026ndash;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e0.96\u0026ndash;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e2.720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e% body fat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.65\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e36.72\u0026ndash;38.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.86\u0026thinsp;\u0026plusmn;\u0026thinsp;4.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e38.95\u0026ndash;38.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeight of fundus (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e37.17\u0026ndash;38.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e37.10\u0026ndash;38.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMaternal Blood pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e141.51\u0026thinsp;\u0026plusmn;\u0026thinsp;31.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e134.75\u0026ndash;148.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e109.04\u0026thinsp;\u0026plusmn;\u0026thinsp;10.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e106.80\u0026ndash;111.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e2.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.62\u0026thinsp;\u0026plusmn;\u0026thinsp;20.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e93.22\u0026ndash;102.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e84.55\u0026ndash;88.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.373\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePulse pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.89\u0026thinsp;\u0026plusmn;\u0026thinsp;17.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e40.10\u0026ndash;47.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.37\u0026thinsp;\u0026plusmn;\u0026thinsp;9.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e20.35\u0026ndash;24.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.779\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eNeonatal anthropometries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.03\u0026ndash;3.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e3.06\u0026ndash;3.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.451\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLength (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e48.10\u0026ndash;49.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e47.81\u0026ndash;49.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.486\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHead circumference (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e34.39\u0026ndash;35.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e34.40\u0026ndash;35.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApgar score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e7.00\u0026ndash;7.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e8.32\u0026ndash;8.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e2.273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation between maternal gestational hypertension, maternal and neonatal anthropometrics and Apgar score\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003ePulse pressure\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eMaternal Anthropometrics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGestational age (weeks)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeight (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.421**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.323*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.446**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.467**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.446**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.398**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.368*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.414**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.347*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWaist circumference (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.439**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHip circumference (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.411**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-0.430**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-0.453**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e-0.407**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e% body fat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.309*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.345*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.307*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeight of fundus (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eNeonatal anthropometrics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLength (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.968\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHead circumference (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApgar score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-0.209*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-0.207*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.050\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe determination of maternal blood pressure is a valuable tool for evaluating and predicting birth outcomes (Gunderson et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This observational hospital-based case-control study investigated the impact of gestational hypertension on neonatal anthropometric characteristics and delivery outcomes. Additionally, the study provides insight into the influence of maternal anthropometric characteristics and blood pressure on the neonatal Apgar score. Major findings indicated significant differences in maternal age, WHR, percentage body fat, and systolic blood pressure between hypertensive and normotensives mothers. The result confirmed significant association between residence, educational status and experience of disease condition among the two groups. In addition, among all variables, the Apgar score of neonates correlated with the systolic and diastolic blood pressure of hypertensive mothers. Other neonatal anthropometries negatively correlated with systolic and diastolic BP of hypertensive mothers. The study developed a regression model to pinpoint the most influential maternal characteristics and determine key factors associated with delivery outcomes.\u003c/p\u003e\u003cp\u003eThe study identified key socio-demographic factors associated with gestational hypertension, including place of residence, educational status and experience of any form diseases during pregnancy. For instance, women with relatively higher education levels have a lower risk of gestational hypertension, likely due to greater knowledge and awareness of the condition from early pregnancy, particularly in resource-limited settings like Sub-Saharan Africa. This aligns with the findings of Kosar et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) who observed that educational interventions contribute to increase knowledge of hypertensive disorder during pregnancy. It was currently observed in the present study that attaining a higher level of education facilitates regular antenatal visits, improves the ability to recognize symptoms related to hypertension, and increase compliance with established obstetric protocol. Furthermore, minimal misconceptions were observed, supporting women understanding of the treatment and management of hypertension, thereby reducing birth complications. This is in agreement with Tamma et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who found that a high burden of misconceptions negatively affects expectant women\u0026rsquo;s perception of hypertension, leading them to disengage from treatment and management protocols. Based on these findings, the study recommends the incorporation of telemedicine for more frequent assessments and timely referral of pregnant women with gestational hypertension to facilities equipped to provide optimal delivery outcomes. Additionally, clinicians should be trained to provide culturally and linguistically competent care to build trust among pregnant patients, particularly as it pertains to gestational hypertension.\u003c/p\u003e\u003cp\u003eA holistic approach integrating place of residence into intervention strategies appears reasonable for effective health planning. Residence status contributes meaningfully to birth outcomes and is relevant in assessing newborn health. Conversely, newborns of mothers residing in rural areas are more likely to experience complications, underscoring the role of residence in healthcare accessibility, quality of care, food security and socio-economic disparity (Nicholls-Dempsey et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mainous et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The current study observed a strong association between maternal residence and the occurrence of maternal hypertension. As currently observed, expectant women who visited the hospital at the period of this study predominantly resided in urban areas, which facilitated access to obstetric services and awareness of self-monitored blood pressure, contributing to high prevalence of spontaneous vagina delivery. Furthermore, expectant mothers residing in urban areas receive more adequate education on the treatment and management of hypertension during pregnancy than their rural counterparts.\u003c/p\u003e\u003cp\u003eThe present study observed a relationship between maternal anthropometries and blood pressure among hypertensive mothers. Consequently, the findings reveal an intricate relationship between maternal anthropometric characteristics, blood pressure status, and delivery outcomes, emphasizing the need for close motoring of blood pressure level during pregnancy. The study identifies a significant relationship between BMI and maternal hypertension, highlighting an important finding in understanding risk factors associated with gestational hypertension. For instance, BMI a widely accepted anthropometric index that measures general obesity has been closely linked to the risks of hypertension disorders and preeclampsia during pregnancy (Dantas et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Marshall et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tsai et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). A high BMI, indicating obesity, is consequently associated with adverse birth outcomes. Additionally, other maternal anthropometric factors in the current study, such as WHR and percentage body fat were found to correlate with gestational hypertension. The strong association between WHR and gestational hypertension may be attributed to accumulation of visceral and intra-abdominal fat in expectant mothers. This type of fat is a sensitive predictive characteristic, closely linked to an increased risk of hypertension during pregnancy. This is corroborated by the study of Mahboubeh et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), who observed that WHR has high sensitivity in predicting the risk of pre-eclampsia, primarily due to accumulation of intra-abdominal fat. A more moderate WHR may facilitate positive pregnancy outcome thereby reducing risk of maternal and neonatal mortality.\u003c/p\u003e\u003cp\u003eAlthough some maternal anthropometrics did not significantly correlate with gestational hypertension in the current study, it is important to note that these characteristics remain crucial for pregnancy outcomes. The association between maternal age and hypertension is well documented. For instance, Romy et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) observed an increased risk of gestational hypertensive disorders with advancing maternal age. However, in the current study, maternal age fell within the reproductive age range, with hypertensive women exhibiting significantly higher maternal age. Furthermore, women of reproductive age have higher odds of preterm delivery, chorioamnionitics and endometritirs (Cavazos-Rehg et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the present study, Apgar score correlated with maternal blood pressure among hypertensive mothers, with 56% of neonates recording a normal\u0026thinsp;\u0026gt;\u0026thinsp;7 Apgar score. Supporting this finding, studies have shown that gestational hypertension significantly associated with lower Apgar score in newborns compared to those born to normotensive women (Yunxia et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), often resulting from the premature termination of pregnancy. The current linear regression model revealed that, gestational hypertension accounted for 3.5% of the variance in Apgar score. Most other newborn anthropometric characteristics, such as body weight and length, and head circumference did not provide additional information beyond what was captured by sex, as they co-correlated with sex while showed weaker correlation with gestational hypertension. Ardently, intravenous labetalol was observed to be prescribed as a safe first-line antihypertensive medication, alongside clinician\u0026rsquo;s recommendations for the consumption of non-processed foods with normal sodium content. This is similar to the studies that recommended intravenous hydralazine, intravenous labetalol, and calcium channel blockers as effective antihypertensive medications (Mariana et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Eustace et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shekhar et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLimitation\u003c/p\u003e\u003cp\u003eThis study was hospital-based and limited to a single visit per participant, follow up of the subjects was not possible. Additionally, it was conducted in only one region, which may limit generalizability of the findings to other settings. In addition, relying only on a single interviewer could have introduced bias into the data collection process. The study did not investigate confounding factors such as physical activity, diet, cholesterol, pre-pregnancy body mass index, pre-pregnancy diabetes, and history of autoimmune disease which may be associated with the variations in gestational hypertension and should be considered when interpreting the findings.\u003c/p\u003e\u003cp\u003ePractical Implication\u003c/p\u003e\u003cp\u003eThey are several ways of applying the key findings to reduce the risk of gestation hypertension and optimize delivery outcomes. For instance, the significant relationship between Apgar score and gestational hypertension in the present study indicates that close monitoring and early intervention of maternal gestational hypertension can play a crucial role in ensuring normal Apgar score of in newborn. Additionally, this study lays the groundwork for further exploration of hypertension factors and promotes an integrative perspective on maternal health that incorporates anthropometries and sociocultural aspects. Understanding the predictors of pregnancy-induced hypertension is crucial in clinical practice, as it will facilitate the prioritization of interventions, implementation policies and allocation of resources accordingly.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study provides insight into the role of hypertensive maternal anthropometries and socio-demographic factors on neonatal anthropometrics and Apgar score, revealing the significant correlation between maternal BMI, WHR and % body fat, and Apgar score with gestational hypertension. These findings offer crucial guidance for clinicians in tailoring programs that emphasize the importance of early screening, effective management system, and frequent monitoring during pregnancy to improve both maternal and fetal outcomes. The present analysis strongly supports the idea that regulation of maternal blood pressure with particular attention to gestational hypertension will contribute to optimal fetal outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interests\u003c/h2\u003e\u003cp\u003eThere are no conflicts of interests to be reported.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eAuthors declare no funding\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: UGC and EO; Methodology: UGC, EO, UKE and POE; Statistical analysis: UGC; Data collection: EO, POE, CA and UFU; Manuscript draft preparation: UGC, UKE and POE; Manuscript writing: UGC, UKE and POE; Review and editing: UGC and POE; Supervision: UGC. Approval to submit to your journal: All authors\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe wish to acknowledge the staff of Gynecology and Obstetrics department for their support and guidance throughout the period of the study\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this manuscript [and its supplementary information files]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBernard CMY, Benjamin OR, Yue F, Man-Fung T. Korean Cycle Journal. 2020; 50(6):e7010\u003c/li\u003e\n \u003cli\u003eBailey PE, Andualem W, Brun M. 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Pregnancy-related mortality and severe maternal morbidity in rural Appalachia: established risks and the need to know more. \u003cem\u003eJ Rural Health.\u003c/em\u003e 2020;36:3\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eEastabrook G, Brown M., Sargent I. The origins and end-organ consequence of pre-eclampsia. \u003cem\u003eBest Pract Res Clin Obstet Gynaecol.\u003c/em\u003e 2011;25:435\u0026ndash;47\u003c/li\u003e\n \u003cli\u003eMomoka I, Hyo K, Tomoko Y, Misa S, Tsuyoshi M, Tsuyoshi H, Fumihiro I, Daisuke S, Toma F, Shun Y, Keiya F, Yasuhisa N. Association between Gestational Weight Gain and Risk of Hypertensive Disorders of Pregnancy among Women with Obesity: A Multicenter Retrospective Cohort Study in Japan. Nutrients. 2023;15(11):2428. doi: 10.3390/nu15112428\u003c/li\u003e\n \u003cli\u003eMiller MJ, Butler P, Gilchriest J, Taylor A, Lutgendorf MA. Implementation of a standardized nurse initiated protocol to manage severe hypertension in pregnancy. J Matern Fetal Neonatal Med. 2020;33(6):1008-1014.\u003c/li\u003e\n \u003cli\u003eLisa K, Meabh M, Kelly-Ann E. Global trends in prevalence of maternal overweight and obesity: A systematic review and meta-analysis of routinely collected data retrospective cohorts. International Journal of Population Data Science. 2024;9(2)\u003c/li\u003e\n \u003cli\u003eAriana T, Apoorva S, Maria CG. Hypertensive Disorders of Pregnancy: A Literature Review \u0026ndash; Pathophysiology, Current Management, Future Perspectives, and Healthcare Disparities. Journal of Asian Pacific Society of Cardiology. 2024; doi.org/10.15420/usc.2023.01\u003c/li\u003e\n \u003cli\u003eEzemagu UK, Uzomba GC, Ubochi C, Ogbu R, Egba F, Olisa O. Maternal and neonatal anthropometric analysis: Determining birth outcomes in low-risk pregnancies at Alex Ekwueme Federal University Teaching Hospital, Abakaliki. Int J Gynaecol Obstet. 2021;154(2):324-330. doi: 10.1002/ijgo.13527.\u003c/li\u003e\n \u003cli\u003eMarta DR de M, Paulo RM, Karina NC , Maria RCGN. Hypertension induced by pregnancy and neonatal outcome: Results from a retrospective cohort study in preterm under 34 weeks. PLoS One. 2021 18;16(8):e0255783. doi: 10.1371/journal.pone.0255783\u003c/li\u003e\n \u003cli\u003eJaykaran C, Tamoghna B. How to Calculate Sample Size for Different Study Designs in Medical Research_.html. Indian Journal of Psychological Medicine. 2013; 35, 121-126. doi.org/10.4103/0253-7176.116232\u003c/li\u003e\n \u003cli\u003eTurner JM, Catherine S, Noel B. Sphygmomanometer calibration Why, how and how often? Australian Family Physician. 2024; 36(10):834-8\u003c/li\u003e\n \u003cli\u003eO\u0026apos;Brien E, Gianfranco P, George S, Roland A, Laurie B, Grzegorz B, Denis C, Alejandro de la S, Peter de L, Eamon D, Robert F, John G, Geoffrey AH, Yutaka I, Kazuomi K, Empar L, Jean-Michel M, Giuseppe M, Thomas M, Martin M, Gbenga O, Takayoshi O, Stefano O, Paolo P, Josep R, Luis MR, Andrew S, Jan AS, Gert van M, Paolo V, Bernard W, Jiguang W, Alberto Z, Yuqing Z. European Society of Hypertension position paper on ambulatory blood pressure monitoring. J Hypertens. 2013 Sep;31(9):1731-68.doi: 10.1097/HJH.0b013e328363e964.\u003c/li\u003e\n \u003cli\u003eChristen RE, Niren RM, Anil AC. Anthropometric measurements as predictors of nutritional status in black South African women during pregnancy. The Journal of Obstetric and Gynecology Research. 2025; 5(1). e16184 doi.org/10.1111/jog.16184\u003c/li\u003e\n \u003cli\u003eSwainson MG, Batterham AM, Tsakirides C, Rutherford ZH, Hind K (2017) Prediction of whole-body fat percentage and visceral adipose tissue mass from five anthropometric variables. PLoS ONE 12(5): e0177175. doi.org/10.1371/journal.pone.0177175\u003c/li\u003e\n \u003cli\u003eRustagi N, Prasuna J, Taneja D. Anthropometric surrogates for screening of low birth weight newborns: a community-based study. \u003cem\u003eAsia Pacific J Public Health\u003c/em\u003e. 2012;24(2):343\u0026ndash;351. doi: 10.1177/1010539510384717\u003c/li\u003e\n \u003cli\u003eGunderson EP, Mara G, Baiyang S, Nancy G, Alan SG, James MR, Nguyen-Huynh MN, Wei T, Stacey EA. Early Pregnancy Systolic Blood Pressure Patterns Predict Early- and Later-Onset Preeclampsia and Gestational Hypertension Among Ostensibly Low-to-Moderate Risk Groups. 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Associations of the pre-pregnancy body mass index and gestational weight gain with pregnancy outcomes in Taiwanese women. \u003cem\u003eAsia Pac J Clin Nutr\u003c/em\u003e 2012; 21: 82\u0026ndash;87\u003c/li\u003e\n \u003cli\u003eMahboubeh T, Zohreh S, Farzaneh S, Masoumeh AK. Early pregnancy waist-to-hip ratio and risk of preeclampsia: a prospective cohort study. Hypertension Research, 2015, 38, 80\u0026ndash;83\u003c/li\u003e\n \u003cli\u003eRomy G, Rachel B, Eric APS, Albert H, Vincent WVJ. Maternal Age During Pregnancy Is Associated With Third Trimester Blood Pressure Level: The Generation R Study. \u003cem\u003eAmerican Journal of Hypertension\u003c/em\u003e, 2011; 24, 9, 1046\u0026ndash;1053, doi.org/10.1038/ajh.2011.95\u003c/li\u003e\n \u003cli\u003eCavazos-Rehg PA, Melissa JK, Edward LS, Kerry B, Tessa M, Margaret AO, Harini S, Jeffrey FP, Laura JB. Maternal age and risk of labor and delivery complications. Matern Child Health J. 2015;19(6):1202\u0026ndash;1211. doi: 10.1007/s10995-014-1624-7\u003c/li\u003e\n \u003cli\u003eYunxia W, Bihong C, Jiuju Z, Shuang Y, Chun W, Yongzhong G, Jinlai M. Risk Factors Associated with Low Apgar Scores in Pregnancies Complicated by Severe Preeclampsia: A Case\u0026ndash;Control Study. Clin. Exp. Obstet. Gynecol. 2024; 51(12): 264 doi.org/10.31083/j.ceog5112264\u003c/li\u003e\n \u003cli\u003eMariana CS, Costa-Filho RC, Vanessa E. Strategic Use of Intravenous Medications to Protect Target Organs in Hypertensive Emergencies Int J Cardiovasc Sci. 2025; 38:e20240117\u003c/li\u003e\n \u003cli\u003eEustace E, Okelue EO, Maame AEB, Rafia A, Namtor NIA, Lilian B, Patience NN, Papa KAB, Onyinyechukwu BN, Caroline CO. Comparing Intravenous Labetalol and Intravenous Hydralazine for Managing Severe Gestational Hypertension. Cureus. 2023;15(7):e42332. doi: 10.7759/cureus.42332\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gestational hypertension, maternal BMI, WHR, educational status, Apgar score, regression","lastPublishedDoi":"10.21203/rs.3.rs-6612239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6612239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study aimed to investigate the effect of gestational hypertension on neonatal anthropometric characteristics and Apgar scores, with particular consideration of sex differences among neonates. Maternal and neonatal anthropometric characteristics were obtained using direct standard anthropometric protocol. Diagnosis of gestational hypertension was established based on standard clinical criteria, while socio-demographic data were collected structured questionnaire. Clinicians recorded the mode of delivery and Apgar scores of the newborns at birth. Chi-square tests, ANOVA, correlation, and stepwise-forward regression analyses were performed at a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (*). Place of residence and educational status shows significant associations with gestational hypertension. Maternal age, WHR, percentage body fat and Apgar score differ between the two groups (η\u0026sup2; = 0.004\u0026ndash;0.049*). Gestational hypertension correlated with maternal weight, height, BMI, WHR, % body fat and Apgar score of the newborn (r= -0.430\u0026ndash;0.467). A final regression model predicted gestational hypertension based on Apgar score (adjusted R\u0026sup2;= 0.035, p\u0026thinsp;=\u0026thinsp;0.077). The study contributes to understanding the role of gestational hypertension in influencing the Apgar score of newborns, emphasizing the importance of maternal blood pressure regulation for favourable on neonatal outcomes. Understanding the effect of gestational hypertension on neonatal features has implications for the close monitoring and early intervention in the treatment and management of maternal blood pressure during pregnancy.\u003c/p\u003e","manuscriptTitle":"Optimizing the effect of gestational hypertension on sex-specific neonatal anthropometric characteristics and Apgar score","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 06:52:22","doi":"10.21203/rs.3.rs-6612239/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-08-29T08:03:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-28T06:21:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-26T07:10:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-10T19:35:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-10T19:34:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a2a5a4f-900a-4166-a2b2-412d7e84b7c7","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54118353,"name":"Health sciences/Anatomy"},{"id":54118354,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-09-08T06:52:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-08 06:52:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6612239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6612239","identity":"rs-6612239","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}