Association of Serum Ferritin With Hypertensive Disorders of Pregnancy: A Longitudinal Analysis From a Large Retrospective Cohort

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Evidence on whether trimester-specific SF relates to the risk and severity of hypertensive disorders of pregnancy (HDP) remains limited, and clinicians lack gestational reference intervals to guide interpretation. Objective: To investigate trimester-specific changes in SF levels during pregnancy, evaluate the association between abnormal SF concentrations and the risk and severity of HDP, and establish gestational reference intervals to inform clinical interpretation. Methods: This retrospective cohort study included 28,607 pregnant women who received prenatal care at a single medical institution, of whom 2,501 developed HDP. Longitudinal changes of SF were analyzed using nonlinear mixed-effects (NLME) models. Multivariable logistic regression estimated adjusted odds ratios (aORs) for HDP across trimester-specific SF categories. Associations with severity were examined for gestational hypertension (GH) and severe preeclampsia (SPE). Trimester-specific SF reference intervals were derived in a healthy subgroup using the Hoffman method. Results: Compared with the CON group, women with HDP had higher SF, most notably in the second and third trimesters. In the first trimester, both low and high SF were associated with higher HDP risk, although only low SF remained after adjustment (aOR 1.389, 95% CIs 1.027–1.879). Elevated SF showed stronger associations with HDP in the second (aOR 1.655, 95% CIs 1.401–1.956) and third trimesters (aOR 2.077, 95% CIs 1.565–2.756). The highest SF category was further associated with GH (aOR 1.786, 95% CIs 1.177–2.709) and SPE (aOR 3.672, 95% CIs 2.456–5.490), suggesting a dose–response pattern. Trimester-specific reference intervals were: first 14.97–215.83 μg/L; second 6.67–63.17 μg/L; third 5.92–52.40 μg/L. Conclusion: SF demonstrates trimester-specific associations with the risk and severity of HDP. Low first-trimester SF aligns with iron deficiency signals, whereas higher SF in mid-to-late pregnancy marks higher HDP risk. These results support individualized interpretation and monitoring of SF rather than routine one-size-fits-all supplementation. Serum ferritin Hypertensive disorders of pregnancy Iron metabolism Reference intervals Retrospective Cohort Figures Figure 1 Figure 2 Introduction Hypertensive disorders of pregnancy (HDP) refer to a spectrum of conditions characterized by new-onset or preexisting hypertension during pregnancy [ ] . Affecting ~ 10% of pregnancies globally [ ] , HDP remain a leading cause of maternal and perinatal morbidity and mortality [ - ] . Despite improvements in antenatal surveillance, prophylaxis, and timely delivery, population-level risk remains considerable. The pathobiology is multifactorial and incompletely defined, limiting targeted treatment [ ] . Consequently, early, reliable, and scalable risk stratification is an unmet need and highlights the value of clinically interpretable biomarkers. Iron is essential for placental and fetal development [ ] , and maternal iron metabolism adapts dynamically across gestation to meet increasing physiologic demands [ ] . Serum ferritin (SF), widely used to assess iron stores, is routinely measured in pregnancy to evaluate maternal iron status [ ] . Low SF, an indicator of iron deficiency (ID), is associated with anemia and reduced oxygen-carrying capacity, which may weaken placental function [ ] . Although routine iron supplementation is recommended during pregnancy to satisfy increased maternal iron demands [ ] , elevated SF levels may indicate iron overload or inflammatory activity, leading to mitochondrial dysfunction, lipid peroxidation, and ferroptosis within placental tissues [ ] , impairing the function of trophoblasts and vascular endothelial cells and contributing to placental malperfusion. Evidence linking SF with adverse pregnancy outcomes has grown, including recurrent miscarriage [ , ] , gestational diabetes mellitus (GDM) [ , ] , and HDP, particularly preeclampsia (PE). However, SF’s clinical utility in predicting and monitoring HDP remains uncertain. While some studies have identified elevated SF levels in early pregnancy as an independent risk factor for HDP [ ] , others have reported no significant association [ ] . Many studies assessed SF at a single time point, seldom considered disease severity, and rarely provided trimester-specific reference intervals, limiting bedside interpretation. We conducted a retrospective cohort study to (1) delineate trimester-specific SF trajectories, (2) quantify associations with HDP risk and severity, and (3) derive gestational reference intervals in a healthy subgroup. Aligning measurements with gestational timing and severity, we provide clinically interpretable evidence to support individualized interpretation and monitoring rather than one-size-fits-all supplementation. Material and methods We conducted a single-center retrospective cohort study within the China Birth Cohort Study (CBCS) at Beijing Obstetrics and Gynecology Hospital, Capital Medical University, from February 2018 to December 2021. Of 47,392 women who established antenatal records and received routine follow-up, we excluded those who (1) withdrew during pregnancy; (2) had multiple gestations; (3) had pre-existing chronic conditions (including chronic hypertension, cardiovascular disease, anemia, hepatic disease, or renal disease); (4) used antihypertensive medications before pregnancy; (5) experienced pregnancy loss before 20 weeks’ gestation; or (6) had missing key variables. After applying the exclusion criteria, 28,607 pregnant women were included, comprising 2,501 individuals diagnosed with HDP and 26,106 normotensive controls (Fig. 1 ) . This study was approved by the Ethics Committee of Beijing Obstetrics and Gynecology Hospital, Capital Medical University (Approval No. 2018-KY-003-02). Written informed consent was obtained from all participants prior to enrollment. Demographic and obstetric characteristics were collected using standardized electronic case report forms in the CBCS electronic data capture system. The baseline variables included maternal age, pre-pregnancy body mass index (BMI), parity, history of HDP, mode of conception, ethnicity, education level, annual household income, employment status, smoking and alcohol consumption, and folic acid/ multivitamin supplementation. Data on HDP history and supplement use were obtained through participant self-report and verified with medical records. Maternal age was categorized as < 35 or ≥ 35 years. Pre-pregnancy BMI was calculated as weight (kg) divided by height squared (m²), and classified into four categories: underweight (< 18.5), normal weight (18–23.9), overweight (24–27.9), and obese (≥ 28). Education level was categorized as tertiary education or below. Mode of conception was classified as natural conception or assisted reproductive technology. SF concentrations were measured at three gestational stages: first trimester (≤ 13 + 6 weeks), second trimester (14–27 + 6 weeks), and third trimester (≥ 28 + 0 weeks). Fasting venous blood (≥ 8 hours) was collected at each trimester and processed within 24 hours. SF levels were quantified using a chemiluminescence immunoassay on the ADVIA Centaur XP analyzer (Siemens Healthcare Diagnostics, Tarrytown, NY, USA), following the manufacturer procedures. Reference ranges for SF during the pregnancy were established based on data from this study population. HDP are commonly classified into four categories: gestational hypertension(GH), pre-eclampsia–eclampsia(PE-E), chronic hypertension, and chronic hypertension with superimposed pre-eclampsia. In this study, women diagnosed with chronic hypertension were excluded from the analysis to focus specifically on hypertensive conditions that newly develop during pregnancy. GH was defined as new-onset systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg, on at least two occasions ≥ 4 hours apart, after 20 weeks of gestation in a previously normotensive woman, without proteinuria. PE was defined as new-onset hypertension (as above) after 20 weeks plus either: (1) proteinuria (≥ 300 mg/24 h), or (2) in the absence of proteinuria, signs of maternal organ dysfunction (hepatic, renal, cardiovascular, hematologic, or neurologic) or uteroplacental involvement. SPE was defined as preeclampsia accompanied by one or more of the following clinical features: severe hypertension (SBP ≥ 160 mm Hg and/or DBP ≥ 110 mm Hg on repeat measurement ≥ 4 hours apart), persistent headache or visual disturbances, heavy proteinuria (> 2.0 g/24 h), elevated liver transaminases; serum creatinine > 106 µmol/L, thrombocytopenia, evidence of hemolysis, hypoproteinemia with serous effusions, pulmonary edema or heart failure, and/or fetal complications. Statistical analyses Continuous variables were summarized as means ± standard deviations (SD) or median (IQR) and categorical variables as n (%). Group comparisons used independent-samples t-tests or the Mann–Whitney U test for continuous variables, and chi-square (χ²) tests for categorical variables. For comparisons involving more than two groups, one-way analysis of variance (ANOVA) or the Kruskal–Wallis H test was used. To analyze longitudinal trends in SF levels throughout pregnancy among women with and without HDP, a nonlinear mixed-effects (NLME) model was employed, accounting for intra-individual correlation and gestational age variability. Associations between trimester-specific SF categories and HDP were estimated with multivariable logistic regression, reported as adjusted odds ratios (aORs) with 95% CIs. Multinomial logistic regression assessed severity of HDP using normotensive pregnancies as the reference. Restricted cubic spline (RCS) regression models were applied to evaluate potential nonlinear dose–response relationships for SF as a continuous exposure. Additionally, we applied the indirect Hoffmann method to establish trimester-specific reference intervals for SF in healthy women. All statistical analyses were performed using R software (version 3.6.2) and SPSS software (version 26.0). A two-sided p-value of < 0.05 was considered statistically significant. Results Baseline Characteristics of the Study Population Among the 28,607 pregnancies, 2,501 (8.74%) were diagnosed with HDP. Compared with CON group, women with HDP were older and had a higher pre-pregnancy BMI. They were more often nulliparous, conceived through assisted reproductive technology, and reported a history of HDP. Education and household income were lower in the HDP group, whereas ethnicity and employment were similar between groups ( Table 1 ) . Regarding health behaviors, smoking during pregnancy was significantly more prevalent in the HDP group. No significant differences were found in alcohol consumption, or in folic acid and multivitamin supplementation. Notably, SF levels were significantly higher in the HDP group. Table 1 General baseline information. CON Group (n = 26106) HDP Group (n = 2501) p value Age (y) 31.66 ± 3.85 32.18 ± 4.15 P<0.001 <35 years old 20706(79.3%) 1834(73.3%) ≥ 35 years old 5400(20.7%) 667(26.7%) Pre-pregnancy BMI (kg/m 2 ) 21.54 ± 2.99 23.8 ± 4.08 P<0.001 <18 kg/m 2 2076(8.0%) 88(3.5%) 18–24 kg/m 2 19424(74.4%) 1359(54.3%) 24–28 kg/m 2 3740(14.3%) 699(27.9%) ≥ 28 kg/m 2 866(3.3%) 355(14.2%) parity P=0.001 Nulliparous 13861 (53.1%) 1413 (56.5%) Multiparous 12245 (46.9%) 1088 (43.5%) HDP history P<0.001 YES 145(0.6%) 88(3.5%) NO 25961(99.4%) 2413(96.5%) Mode of conception P<0.001 Natural conception 24661 (94.5%) 2225 (89%) Assisted reproductive technology 1445 (5.5%) 276 (11%) Ethnicity P = 0.279 Han 24123 (92.4%) 2326 (93%) Others 1983 (7.6%) 175 (7%) Education level P = 0.001 College diploma or above 24518 (93.9%) 2306 (92.2%) Below college diploma 1588 (6.1%) 195 (7.8%) Annual household income P<0.001 ≥ 200,000 CNY 16382 (62.8%) 1409 (56.3%) < 200,000 CNY 9724 (37.2%) 1092 (43.7%) Employment status P = 0.286 YES 24232 (92.8%) 2307 (92.2%) NO 1874 (7.2%) 194 (7.8%) Smoking P = 0.003 YES 777 (3%) 101 (4%) NO 25329 (97%) 2400 (96%) Drinking P = 0.272 YES 1277 (4.9%) 110 (4.4%) NO 24829 (95.1%) 2391 (95.6%) Folic acid P = 0.796 YES 2873 (11%) 271 (10.8%) NO 23233 (89%) 2230 (89.2%) Multivitamin P = 0.807 YES 5985 (22.9%) 568 (22.7%) NO 20121 (77.1%) 1933 (77.3%) SF measured in first trimester (µg/L) 69.37 ± 49.3 74.07 ± 68.3 P = 0.006 SF measured in second trimester(µg/L) 24.80 ± 20.57 29.92 ± 28.14 P<0.001 SF measured in third trimester (µg/L) 19.82 ± 13.24 24.49 ± 21.2 P<0.001 Hb measured in first trimester (g/L) 130.12 ± 8.8 131.85 ± 9.2 P<0.001 Abbreviations: HDP, Hypertensive Disorders Of Pregnancy; SF, serum ferritin;BMI, body mass index ； Hb,hemoglobin; *P-values < 0.05 was considered statistically significant Longitudinal Changes of SF During Pregnancy in HDP and CON Groups We first analyzed the distribution of SF concentrations across different gestational stages. SF declined across gestation, with the steepest decrease from the first to the second trimester (Figure S1). To investigate the dynamic changes in SF levels between the HDP and CON groups further, we constructed a NLME model incorporating a random intercept for each participant. After adjusting for key maternal characteristics, including maternal age, prepregnancy BMI, Hb, parity, education level, mode of conception, and HDP history, the NLME model confirmed that SF levels remained consistently elevated in the HDP group across all three trimesters ( Fig. 2 ). Unadjusted trajectories showed similar patterns (Figure S2) . Association Between First-Trimester SF Levels and Risk of HDP To examine the association between early-pregnancy SF and the subsequent HDP, first-trimester SF was categorized as: ≤5th percentile (≤ 15.635 µg/L), 5th–95th percentile (reference), and > 95th percentile (> 165 µg/L). In the unadjusted model, both low and high SF levels were associated with an increased risk of HDP. After adjustment for potential confounders, only low SF remained associated (aOR = 1.389, 95%CIs: 1.027–1.879, P = 0.033),, whereas high SF was not (aOR = 1.154, 95% CIs: 0.864–1.541, P = 0.333) ( Table 2 ) . To assess potential non-linear trends,RCS regression was conducted. Both univariable and multivariable models revealed a modest U-shaped association between first-trimester SF levels and HDP risk (non-linear P = 0.0287 and 0.0456, respectively) (Figure S3) . Table 2 Associations between SF with HDP. SF Non-adjust Adjust 1st OR(95%CI) P value aOR(95%CI) P value ≤ 5th 1.352(1.026–1.786) 0.032* 1.389 (1.027–1.879) 0.033* 5−95th Ref 1 Ref 1 >95th 1.349(1.024–1.7879) 0.034* 1.154 (0.864–1.541) 0.333 2nd 1.008(1.006–1.01) <0.001* 1.005(1.004–1.007) 95th 1.954(1.667–2.29) <0.001* 1.655 (1.401–1.956) <0.001* 3rd 1.016(1.012–1.02) <0.001* 1.011(1.007–1.016) 95th 2.756(2.121–3.582) <0.001* 2.077(1.565–2.756) <0.001* Adjust: Age, Prepregnancy BMI, Mode of conception,HDP History,Parity, Education level,Hb. OR, odds ratio; aOR, adjusted odds ratio. Progressive Association Between SF and HDP Risk Across Pregnancy Stages As pregnancy advanced, elevated SF levels showed a notably stronger relationship with HDP risk. As shown in Tables 2 and Figure S4-S5 , in the second trimester, elevated SF levels were significantly associated with increased HDP risk (aOR = 1.655, 95% CIs: 1.401–1.956, P < 0.001) , and this association became even stronger in the third trimester (aOR = 2.077, 95% CIs: 1.565–2.756, P < 0.001) . These findings suggest that SF may play a more critical role in the progression of HDP as gestation advances. RCS indicated a monotonic increase in risk with higher SF across mid-to-late pregnancy. Based on these results, we further focused on third trimester SF and its relationship with HDP severity in subsequent analyses. Association of Third-Trimester SF with HDP Severity One-way ANOVA showed significant differences in SF levels across the four groups (P < 0.0001), with the highest levels observed in the SPE group (Figure S6) . Multinomial logistic regression analysis revealed that individuals with SF levels above the 95th percentile had a significantly increased risk of developing GH (aOR = 1.786, 95% CIs: 1.177–2.709, P = 0.006) and SPE (aOR 3.672, 95% CIs 2.456–5.490), but not with PE ( Table 3 ). Low SF (≤ 5th percentile) was not independently associated with any subtype. These findings suggest that the relationship between SF levels and HDP risk strengthens as pregnancy progresses. Table 3 Associations between SF at third trimester with GH, PE and SPE. Non-adjust Adjust OR(95%CI) P value aOR(95%CI) P value GH ≤ 5th 0.76(0.413–1.401) 0.38 0.78 (0.415–1.466) 0.44 5−95th Ref 1 Ref 1 >95th 2.202(1.479–3.277) 95th 1.384 (0.723–2.649) 0.327 0.968 (0.497–1.888) 0.925 SPE ≤ 5th 0.761 (0.334–1.733) P = 0.516 0.937 (0.403–2.179) 0.879 5−95th Ref 1 Ref 1 >95th 5.156 (3.54–7.51) <0.001 3.672 (2.456–5.49) <0.001 Adjust: Age, prepregnancy BMI, Mode of conception,HDP History, parity, Education level,Hb. OR, odds ratio; aOR, adjusted odds ratio. Trimester-Specific Reference Intervals for SF in Healthy Pregnant Women Because trimester-specific reference intervals for SF during pregnancy are not routinely available, we derived gestational reference intervals using the indirect Hoffmann method in a predefined healthy subgroup without HDP, GDM, inflammation, or anemia. The results showed that the reference intervals for SF were 14.97–215.83 µg/L in the first trimester, 6.67–63.17 µg/L in the second trimester, and 5.92–52.4 µg/L in the third trimester. Discussion Principal Findings In this large retrospective cohort study, we analyzed trimester-specific changes in SF levels and their association with HDP. SF declined with gestation yet remained consistently higher among women who developed HDP. A U-shaped relationship was observed in early pregnancy, with low SF associated with increased HDP risk. The association strengthened in later trimesters, particularly for SPE. These findings highlight SF as a dynamic biomarker for HDP risk and severity, supporting individualized monitoring and iron management. Results in the Context of What Is Known In our study, the incidence of HDP was 8.74%, which falls within the commonly reported range of 5% to 10% in China [ ] . HDP prevalence varies globally, with higher rates observed in Africa. Our findings are consistent with previous studies identifying advanced maternal age, obesity, and prior HDP as significant risk factors [ , ] . However, the association between iron status and HDP remains inconclusive and requires further investigation. Our results provide novel insights by demonstrating that SF concentrations decline progressively during pregnancy [ ] , consistent with physiological hemodilution and increased iron demands. Traditional SF thresholds (< 15 µg/L) may underestimate the prevalence of ID during pregnancy. To more accurately assess the relationship between iron status and HDP, we used a percentile-based approach, stratifying SF into extreme percentiles (< 5th and ≥ 95th) rather than relying on a fixed threshold [ ] . This method omay improve risk stratification relative to a single universal cut-point. The association between SF levels and HDP risk remains inconsistent in the literature. A large retrospective cohort study found a significant positive association between elevated first-trimester SF and HDP risk, suggesting excessive iron stores as a potential risk factor [ ] , although other studies have failed to identify a significant relationship [ , ] .Our study found that lower first-trimester SF was more strongly associated with subsequent HDP development, which aligns with previous studies linking iron deficiency to increased HDP risk [ ] . This highlights the importance of early iron monitoring to identify at-risk pregnancies. As pregnancy progresses, particularly in the second and third trimesters, elevated SF levels are significantly correlated with an increased risk of HDP Prior literature variably links SF with HDP phenotypes. Our severity-stratified, trimester-aware analysis aligns with GH showing biphasic signals: low first-trimester (ID) SF and elevated mid/late SF both associated with GH. For PE, a more complex intermediate phenotype, associations were modest and clearest in the second trimester [ ] ; third-trimester elevations were not independent, consistent with reports that SF tracks inflammation [ , ] and Mendelian randomization arguing against causality [ , ] . SPE showed the strongest, consistent links to elevated SF in mid/late gestation. Together with published data, these results support phenotype- and gestational-stage–specific interpretation of SF and argue against a single universal threshold, informing risk triage and monitoring. We derived trimester-specific SF reference intervals using an internal healthy population and the indirect Hoffman method on a single analyzer. To limit pathologic upward shift, we excluded pregnancies with HDP, GDM, anemia, and recorded inflammatory conditions. Notably, the lower limit of SF in the first trimester (14.97 µg/L) closely approximates the diagnostic threshold for ID (15 µg/L), supporting the validity of our findings. Given the significant differences in SF levels between early, mid-, and late pregnancy, trimester-specific reference intervals are warranted to guide iron monitoring and clinical decision-making. Future research should incorporate a broader range of pregnancy-related complications to further refine the clinical applicability of SF reference standards. Clinical Implications Clinically, our findings support gestational stage–specific, dynamic interpretation of SF. In early pregnancy, low SF typically indicates iron deficiency and may coincide with impaired placentation. In mid-to-late gestation, higher SF aligns with oxidative and inflammatory activity—plausible pathways include Fenton-reaction reactive oxygen species and ferroptosis—processes that can injure placental and vascular function. Because SF is an acute-phase reactant and values are influenced by hemodilution, a single universal threshold is inappropriate. We recommend assessing iron status at booking and repeating SF at ~ 28–32 weeks or when risk flags emerge, interpreting results alongside conventional risk factors and fetal growth. Persistently elevated late-pregnancy SF may signal placental dysfunction. Integrating SF with inflammatory and ferroptosis biomarkers could further refine risk-adapted care. Strengths and limitations This study benefits from a large retrospective cohort, longitudinal data collection, trimester-specific SF measurements, and detailed classification of HDP subtypes, enabling a nuanced analysis of temporal and phenotypic associations. However, several limitations should be acknowledged. First, this was a single-center study, which may limit generalizability. Second, causal inference is restricted due to the observational design. Third, inflammatory markers such as C-reactive protein (CRP) were not available, limiting interpretation of the inflammatory context of SF fluctuations. Lastly, iron supplementation data, including dose and frequency, were not systematically recorded, and SF data completeness varied across trimesters, with only a minority of participants having measurements at all three time points. Conclusion SF shows a gestational stage–dependent relationship with HDP: in early pregnancy, low SF relates modestly to subsequent HDP, whereas in mid-to-late gestation elevated SF is more strongly associated with disease presence and severity, particularly severe preeclampsia. These data support SF as a dynamic, stage-specific risk marker to aid risk stratification rather than a standalone diagnostic or treatment threshold. Given SF’s acute-phase properties, associations likely reflect inflammatory and placental-dysfunction pathways rather than direct causality; extreme elevations may participate via oxidative/ferroptotic mechanisms. Prospective multicenter studies should define trimester-specific cutoffs, assess incremental predictive value over established risk factors, and disentangle iron overload from inflammation-driven elevation. Declarations Ethics Statement This study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was approved by the Ethics Committee of Beijing Obstetrics and Gynecology Hospital, Capital Medical University (Approval No. 2018-KY-003-02). Written informed consent was obtained from all participants prior to enrolment. Acknowledgements The authors extend their gratitude to all study participants and staff involved in participant recruitment. This study utilised data from the China Birth Cohort Study (CBCS) conducted at Beijing Obstetrics and Gynecology Hospital. Author Contributions Tian Zongyuan contributed to the study concept and design, conducted data analysis using specialised software, and drafted the manuscript. Su Shaofei and Liu Ruixia provided critical supervision and intellectual guidance throughout the research. Yin Chenghong acquired funding and supported project coordination. All authors reviewed and approved the final version of the manuscript. Declarations ： Consent to Publish declaration: not applicable. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding ： This study was supported by the National Key Research and Development Program of China (2016YFC1000101). Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to institutional and patient privacy restrictions but are available from the corresponding author on reasonable request. References Wu, P.; Green, M.; Myers, J. E. Hypertensive Disorders of Pregnancy. Stroke Vasc Neurol 2023, e071653. https://doi.org/10.1136/bmj-2022-071653. Ghossein‐Doha, Chahinda, et al. “Hypertensive Pregnancy Disorder, an Under‐recognized Women Specific Risk Factor for Heart Failure?” European Journal of Heart Failure, Nov. 2024, https://doi.org/10.1002/ejhf.3520. Countouris, M.; Mahmoud, Z.; Cohen, J. B.; Crousillat, D.; Hameed, A. B.; Harrington, C. M.; Hauspurg, A.; Honigberg, M. C.; Lewey, J.; Lindley, K.; McLaughlin, M. M.; Sachdev, N.; Sarma, A.; Shapero, K.; Sinkey, R.; Tita, A.; Wong, K. E.; Yang, E.; Cho, L.; Bello, N. A. Hypertension in Pregnancy and Postpartum: Current Standards and Opportunities to Improve Care. Circulation 2025, 151 (7), 490–507. https://doi.org/10.1161/circulationaha.124.073302. Ford, N. D.; Cox, S.; Ko, J. Y.; Ouyang, L.; Romero, L.; Colarusso, T.; Ferre, C. D.; Kroelinger, C. D.; Hayes, D. K.; Barfield, W. D. Hypertensive Disorders in Pregnancy and Mortality at Delivery Hospitalization — United States, 2017–2019. MMWR Morb. Mortal. Wkly. Rep. 2022, 71 (17), 585–591. Elovitz, M. A.; Gee, E. P. S.; Delaney-Busch, N.; Moe, A. B.; Reddy, M.; Khodursky, A.; La, J.; Abbas, I.; Mekaru, K.; Collins, H.; Siddiqui, F.; Nolan, R.; Boelig, R. C.; Kiefer, D. G.; Simmons, P. M.; Saade, G. R.; Saad, A.; Carter, E. B.; McElrath, T. F.; Quake, S. R.; DePristo, M. A.; Haverty, C.; Lee, M.; Namsaraev, E.; Berghella, V.; Collier, A. Y.; Frolova, A. I.; Park-Hwang, E.; Pacheco, L. D.; Sutton, E. F.; Jain, M.; Rood, K.; Grobman, W. A.; Biggio, J. R.; Gyamfi-Bannerman, C.; Jeyabalan, A.; Rasmussen, M. Molecular Subtyping of Hypertensive Disorders of Pregnancy. Nat Commun 2025, 16 (1). https://doi.org/10.1038/s41467-025-58157-y. Georgieff, M. K. Iron Deficiency in Pregnancy. Am J Obstet Gynecol 2020, 223 (4), 516–524. https://doi.org/10.1016/j.ajog.2020.03.006. Milman, N.; Taylor, C. L.; Merkel, J.; Brannon, P. M. Iron Status in Pregnant Women and Women of Reproductive Age in Europe. Am J Clin Nutr 2017, 106, 1655S-1662S. https://doi.org/10.3945/ajcn.117.156000. Arosio, P.; Cairo, G.; Bou-Abdallah, F. A Brief History of Ferritin, an Ancient and Versatile Protein. IJMS 2024, 26 (1), 206. https://doi.org/10.3390/ijms26010206. Evanchuk, J. L.; Kozyrskyj, A.; Hanas, N.; Goruk, S.; Vaghef-Mehrabani, E.; Archundia-Herrera, C. M.; O’Brien, K. O.; Letourneau, N. L.; Giesbrecht, G. F.; Bell, R. C.; Field, C. J. Maternal Iron Status Is Dynamic Throughout Pregnancy and Might Predict Birth Outcomes in a Sex Dependent Manner: Results from the Alberta Pregnancy Outcomes and Nutrition (APrON) Cohort Study. J Nutr 2023, 153 (9), 2585–2597. https://doi.org/10.1016/j.tjnut.2023.06.042. Milman, N.; Taylor, C. L.; Merkel, J.; Brannon, P. M. Iron Status in Pregnant Women and Women of Reproductive Age in Europe. Am J Clin Nutr 2017, 106, 1655S-1662S. https://doi.org/10.3945/ajcn.117.156000. Zhang, Y.; Lu, Y.; Jin, L. Iron Metabolism and Ferroptosis in Physiological and Pathological Pregnancy. IJMS 2022, 23 (16), 9395. https://doi.org/10.3390/ijms23169395. Georgsen, M.; Krog, M. C.; Korsholm, A.-S.; Hvidman, H. W.; Kolte, A. M.; Rigas, A. S.; Ullum, H.; Ziebe, S.; Andersen, A. N.; Nielsen, H. S.; Hansen, M. B. Serum Ferritin Level Is Inversely Related to Number of Previous Pregnancy Losses in Women with Recurrent Pregnancy Loss. Fertil Steril 2020, 115 (2), 389–396. https://doi.org/10.1016/j.fertnstert.2020.08.1410. Guo, Y.; Zhang, N.; Zhang, D.; Ren, Q.; Ganz, T.; Liu, S.; Nemeth, E. Iron Homeostasis in Pregnancy and Spontaneous Abortion. Am J Hematol 2018, 94 (2), 184–188. https://doi.org/10.1002/ajh.25341. Zhang, Z.; Li, X.; Zhou, X.; Zhang, Y.; Gan, X.; Xu, X.; Wu, H. Association of Gestational Hypertriglyceridemia, Diabetes with Serum Ferritin Levels in Early Pregnancy: A Retrospective Cohort Study. Front. Endocrinol. 2023, 14. https://doi.org/10.3389/fendo.2023.1067655. Li, N.; Yan, S.; Weng, J.; Liang, G.; Gong, Y.; Su, Y.; Wei, X.; Ren, W.; Zhen, Q.; Zhu, J.; Liu, F.; Zhang, F.; Wang, Y. Association of Mid-Pregnancy Ferritin Levels with Postpartum Glucose Metabolism in Women with Gestational Diabetes. Nutr. Diabetes 2024, 14 (1). https://doi.org/10.1038/s41387-024-00338-7. Fang, Z.; Zheng, S.; Xie, Y.; Lin, S.; Zhang, H.; Yan, J. Correlation between Serum Ferritin in Early Pregnancy and Hypertensive Disorders in Pregnancy. Front. Nutr. 2023, 10. https://doi.org/10.3389/fnut.2023.1151410. Lewandowska, M.; Sajdak, S.; Lubiński, J. Can Serum Iron Concentrations in Early Healthy Pregnancy Be Risk Marker of Pregnancy-Induced Hypertension? Nutrients 2019, 11 (5), 1086. https://doi.org/10.3390/nu11051086. Li F, Qin J, Zhang S, Chen L. Prevalence of hypertensive disorders in pregnancy in China: A systematic review and meta-analysis. Pregnancy Hypertens. 2021;24:13-21. doi:10.1016/j.preghy.2021.02.001 Jiang, L.; Tang, K.; Magee, L. A.; Dadelszen, P. von; Ekeroma, A.; Li, X.; Zhang, E.; Bhutta, Z. A. A Global View of Hypertensive Disorders and Diabetes Mellitus during Pregnancy. Nature Reviews Endocrinology 2022, 18. https://doi.org/10.1038/s41574-022-00734-y. Abdurrahman, A.; Adamu, A. N.; Ashimi, A.; Adekunle, O. O.; Bature, S. B.; Aliyu, L. D.; Akeem, O.; Abdullahi, H.; Lavin, T.; Daneji, S.; Musa, B.; Muazu, Z.; Tukur, J.; Galadanci, H. S. Predictors, Prevalence and Outcome of Hypertensive Disorders in Pregnancy in Nigerian Tertiary Health Facilities. BJOG 2024, 131 (S3), 42–54. https://doi.org/10.1111/1471-0528.17902. Mei Z, Addo OY, Jefferds MED, Flores-Ayala RC, Brittenham GM. Physiologically based trimester-specific serum ferritin thresholds for iron deficiency in US pregnant women. Blood Adv. 2024;8(14):3745-3753. doi:10.1182/bloodadvances.2024013460 Ray JG, Berger H, Park AL. Population-based study of serum ferritin in early pregnancy and adverse perinatal outcomes. Paediatr Perinat Epidemiol. 2020;34(6):706-712. doi:10.1111/ppe.12687 Fang, Z.; Zheng, S.; Xie, Y.; Lin, S.; Zhang, H.; Yan, J. Correlation between Serum Ferritin in Early Pregnancy and Hypertensive Disorders in Pregnancy. Front. Nutr. 2023, 10. https://doi.org/10.3389/fnut.2023.1151410. Taeubert, M. J.; Wiertsema, C. J.; Vermeulen, M. J.; Quezada-Pinedo, H. G.; Reiss, I. K.; Muckenthaler, M. U.; Gaillard, R. Maternal Iron Status in Early Pregnancy and Blood Pressure Throughout Pregnancy, Placental Hemodynamics, and the Risk of Gestational Hypertensive Disorders. J. Nutr. 2022, 152 (2), 525–534. https://doi.org/10.1093/jn/nxab368. Yang, L.; Wu, L.; Liu, Y.; Chen, H.; Wei, Y.; Sun, R.; Shen, S.; Zhan, B.; Yang, J.; Deng, G. Association Between Serum Ferritin Concentration and Risk of Adverse Maternal and Fetal Pregnancy Outcomes: A Retrospective Cohort Study. DMSO 2022, Volume 15, 2867–2876. https://doi.org/10.2147/dmso.s380408. Lewandowska, M.; Sajdak, S.; Lubiński, J. Can Serum Iron Concentrations in Early Healthy Pregnancy Be Risk Marker of Pregnancy-Induced Hypertension? Nutrients 2019, 11 (5), 1086. https://doi.org/10.3390/nu11051086. Møller, H. I.; Persson, G.; Klok, F. B.; Vojdeman, F. J.; Lebech, M.; Hviid, T. V. F. Investigations of Leukocyte and Inflammatory Markers in Pregnancies Complicated by Preeclampsia. J. Reprod. Immunol. 2023, 160, 104163. https://doi.org/10.1016/j.jri.2023.104163. Gutierrez-Aguirre C H, García-Lozano J A, Treviño-Montemayor O R, et al. Comparative analysis of iron status and other hematological parameters in preeclampsia [J]. Hematology, 2016, 22(1): 36–40. Aires Rodrigues de Freitas M, Vieira da Costa A, Alves de Medeiros L, et al. Are There Differences in the Anthropometric, Hemodynamic, Hematologic, and Biochemical Profiles between Late- and Early-Onset Preeclampsia? [J]. Obstetrics and Gynecology International, 2018, 2018: 1–12. Li P, Wang H, Chen T, et al. Association between iron status, preeclampsia and gestational hypertension: A bidirectional two-sample Mendelian randomization study. J Trace Elem Med Biol. 2024;86:127528. doi:10.1016/j.jtemb.2024.127528 Yang, X.; Wei, J.; Sun, L.; Zhong, Q.; Zhai, X.; Chen, Y.; Luo, S.; Tang, C.; Wang, L. Causal Relationship between Iron Status and Preeclampsia-Eclampsia: A Mendelian Randomization Analysis. Clin. Exp. Hypertens. 2024, 46 (1). https://doi.org/10.1080/10641963.2024.2321148. Additional Declarations No competing interests reported. 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15:32:40","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78301,"visible":true,"origin":"","legend":"","description":"","filename":"2eacda021f6d4d70a35fd9f13d3906e71structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8169665/v1/1402166c4ad8ad8cafc76cb9.xml"},{"id":97700297,"identity":"0c741ebd-ed93-47d7-9d48-cd626c79940e","added_by":"auto","created_at":"2025-12-08 12:18:30","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91990,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8169665/v1/de828ae94985b2e4898a6523.html"},{"id":97700282,"identity":"a8e17df8-81d4-49c6-90d3-f656de255ed3","added_by":"auto","created_at":"2025-12-08 12:18:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":117994,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the enrolled pregnancy women.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e HDP, Hypertensive Disorders Of Pregnancy; SF, serum ferritin;\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8169665/v1/d582d1e16351755488672974.png"},{"id":97894266,"identity":"d271dfe5-42c6-4a07-a0ca-3fcb95480d83","added_by":"auto","created_at":"2025-12-10 15:32:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdjusted trajectories of SF levels throughout pregnancy by HDP status. \u003c/strong\u003eModel-based predictions from a nonlinear mixed-effects model (random intercepts for individuals) , adjusted for maternal age, prepregnancy BMI, Hb, parity, education level, mode of conception, and history of HDP. Women with HDP consistently exhibited higher SF levels across all trimesters compared to the CON group (P \u0026lt; 0.001). Vertical dashed lines indicate the trimester cutoffs at 14 and 28 weeks.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8169665/v1/d6f4f745255bc4195d49b3bc.png"},{"id":98420955,"identity":"64df0de1-0d8d-4712-8dbf-48e37fd3aa25","added_by":"auto","created_at":"2025-12-17 16:20:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1333827,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8169665/v1/4c888fbb-afa0-486a-95a8-36b08bd17e90.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Serum Ferritin With Hypertensive Disorders of Pregnancy: A Longitudinal Analysis From a Large Retrospective Cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertensive disorders of pregnancy (HDP) refer to a spectrum of conditions characterized by new-onset or preexisting hypertension during pregnancy\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Affecting\u0026thinsp;~\u0026thinsp;10% of pregnancies globally\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e, HDP remain a leading cause of maternal and perinatal morbidity and mortality\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e\u003csup\u003e-\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Despite improvements in antenatal surveillance, prophylaxis, and timely delivery, population-level risk remains considerable. The pathobiology is multifactorial and incompletely defined, limiting targeted treatment\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Consequently, early, reliable, and scalable risk stratification is an unmet need and highlights the value of clinically interpretable biomarkers.\u003c/p\u003e\u003cp\u003eIron is essential for placental and fetal development\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e, and maternal iron metabolism adapts dynamically across gestation to meet increasing physiologic demands\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Serum ferritin (SF), widely used to assess iron stores, is routinely measured in pregnancy to evaluate maternal iron status\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn8\" id=\"#FNLinkFn8\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Low SF, an indicator of iron deficiency (ID), is associated with anemia and reduced oxygen-carrying capacity, which may weaken placental function\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn9\" id=\"#FNLinkFn9\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Although routine iron supplementation is recommended during pregnancy to satisfy increased maternal iron demands\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn10\" id=\"#FNLinkFn10\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e, elevated SF levels may indicate iron overload or inflammatory activity, leading to mitochondrial dysfunction, lipid peroxidation, and ferroptosis within placental tissues\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn11\" id=\"#FNLinkFn11\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e, impairing the function of trophoblasts and vascular endothelial cells and contributing to placental malperfusion.\u003c/p\u003e\u003cp\u003eEvidence linking SF with adverse pregnancy outcomes has grown, including recurrent miscarriage\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn12\" id=\"#FNLinkFn12\"\u003e\u003c/a\u003e\u003csup\u003e,\u003c/sup\u003e \u003ca class=\"FNLink\" href=\"#Fn13\" id=\"#FNLinkFn13\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e, gestational diabetes mellitus (GDM)\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn14\" id=\"#FNLinkFn14\"\u003e\u003c/a\u003e\u003csup\u003e,\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn15\" id=\"#FNLinkFn15\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e, and HDP, particularly preeclampsia (PE). However, SF\u0026rsquo;s clinical utility in predicting and monitoring HDP remains uncertain. While some studies have identified elevated SF levels in early pregnancy as an independent risk factor for HDP\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn16\" id=\"#FNLinkFn16\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e, others have reported no significant association\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn17\" id=\"#FNLinkFn17\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Many studies assessed SF at a single time point, seldom considered disease severity, and rarely provided trimester-specific reference intervals, limiting bedside interpretation.\u003c/p\u003e\u003cp\u003eWe conducted a retrospective cohort study to (1) delineate trimester-specific SF trajectories, (2) quantify associations with HDP risk and severity, and (3) derive gestational reference intervals in a healthy subgroup. Aligning measurements with gestational timing and severity, we provide clinically interpretable evidence to support individualized interpretation and monitoring rather than one-size-fits-all supplementation.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eWe conducted a single-center retrospective cohort study within the China Birth Cohort Study (CBCS) at Beijing Obstetrics and Gynecology Hospital, Capital Medical University, from February 2018 to December 2021. Of 47,392 women who established antenatal records and received routine follow-up, we excluded those who (1) withdrew during pregnancy; (2) had multiple gestations; (3) had pre-existing chronic conditions (including chronic hypertension, cardiovascular disease, anemia, hepatic disease, or renal disease); (4) used antihypertensive medications before pregnancy; (5) experienced pregnancy loss before 20 weeks\u0026rsquo; gestation; or (6) had missing key variables. After applying the exclusion criteria, 28,607 pregnant women were included, comprising 2,501 individuals diagnosed with HDP and 26,106 normotensive controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This study was approved by the Ethics Committee of Beijing Obstetrics and Gynecology Hospital, Capital Medical University (Approval No. 2018-KY-003-02). Written informed consent was obtained from all participants prior to enrollment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDemographic and obstetric characteristics were collected using standardized electronic case report forms in the CBCS electronic data capture system. The baseline variables included maternal age, pre-pregnancy body mass index (BMI), parity, history of HDP, mode of conception, ethnicity, education level, annual household income, employment status, smoking and alcohol consumption, and folic acid/ multivitamin supplementation. Data on HDP history and supplement use were obtained through participant self-report and verified with medical records. Maternal age was categorized as \u0026lt;\u0026thinsp;35 or \u0026ge;\u0026thinsp;35 years. Pre-pregnancy BMI was calculated as weight (kg) divided by height squared (m\u0026sup2;), and classified into four categories: underweight (\u0026lt;\u0026thinsp;18.5), normal weight (18\u0026ndash;23.9), overweight (24\u0026ndash;27.9), and obese (\u0026ge;\u0026thinsp;28). Education level was categorized as tertiary education or below. Mode of conception was classified as natural conception or assisted reproductive technology.\u003c/p\u003e\u003cp\u003eSF concentrations were measured at three gestational stages: first trimester (\u0026le;\u0026thinsp;13\u0026thinsp;+\u0026thinsp;6 weeks), second trimester (14\u0026ndash;27\u0026thinsp;+\u0026thinsp;6 weeks), and third trimester (\u0026ge;\u0026thinsp;28\u0026thinsp;+\u0026thinsp;0 weeks). Fasting venous blood (\u0026ge;\u0026thinsp;8 hours) was collected at each trimester and processed within 24 hours. SF levels were quantified using a chemiluminescence immunoassay on the ADVIA Centaur XP analyzer (Siemens Healthcare Diagnostics, Tarrytown, NY, USA), following the manufacturer procedures. Reference ranges for SF during the pregnancy were established based on data from this study population.\u003c/p\u003e\u003cp\u003eHDP are commonly classified into four categories: gestational hypertension(GH), pre-eclampsia\u0026ndash;eclampsia(PE-E), chronic hypertension, and chronic hypertension with superimposed pre-eclampsia. In this study, women diagnosed with chronic hypertension were excluded from the analysis to focus specifically on hypertensive conditions that newly develop during pregnancy. \u003cb\u003eGH\u003c/b\u003e was defined as new-onset systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, on at least two occasions\u0026thinsp;\u0026ge;\u0026thinsp;4 hours apart, after 20 weeks of gestation in a previously normotensive woman, without proteinuria. \u003cb\u003ePE\u003c/b\u003e was defined as new-onset hypertension (as above) after 20 weeks plus either: (1) proteinuria (\u0026ge;\u0026thinsp;300 mg/24 h), or (2) in the absence of proteinuria, signs of maternal organ dysfunction (hepatic, renal, cardiovascular, hematologic, or neurologic) or uteroplacental involvement. \u003cb\u003eSPE\u003c/b\u003e was defined as preeclampsia accompanied by one or more of the following clinical features: severe hypertension (SBP\u0026thinsp;\u0026ge;\u0026thinsp;160 mm Hg and/or DBP\u0026thinsp;\u0026ge;\u0026thinsp;110 mm Hg on repeat measurement\u0026thinsp;\u0026ge;\u0026thinsp;4 hours apart), persistent headache or visual disturbances, heavy proteinuria (\u0026gt;\u0026thinsp;2.0 g/24 h), elevated liver transaminases; serum creatinine\u0026thinsp;\u0026gt;\u0026thinsp;106 \u0026micro;mol/L, thrombocytopenia, evidence of hemolysis, hypoproteinemia with serous effusions, pulmonary edema or heart failure, and/or fetal complications.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analyses\u003c/h2\u003e\u003cp\u003eContinuous variables were summarized as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD) or median (IQR) and categorical variables as n (%). Group comparisons used independent-samples t-tests or the Mann\u0026ndash;Whitney U test for continuous variables, and chi-square (χ\u0026sup2;) tests for categorical variables. For comparisons involving more than two groups, one-way analysis of variance (ANOVA) or the Kruskal\u0026ndash;Wallis H test was used. To analyze longitudinal trends in SF levels throughout pregnancy among women with and without HDP, a nonlinear mixed-effects (NLME) model was employed, accounting for intra-individual correlation and gestational age variability. Associations between trimester-specific SF categories and HDP were estimated with multivariable logistic regression, reported as adjusted odds ratios (aORs) with 95% CIs. Multinomial logistic regression assessed severity of HDP using normotensive pregnancies as the reference. Restricted cubic spline (RCS) regression models were applied to evaluate potential nonlinear dose\u0026ndash;response relationships for SF as a continuous exposure. Additionally, we applied the indirect Hoffmann method to establish trimester-specific reference intervals for SF in healthy women. All statistical analyses were performed using R software (version 3.6.2) and SPSS software (version 26.0). A two-sided p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline Characteristics of the Study Population\u003c/h2\u003e\n \u003cp\u003eAmong the 28,607 pregnancies, 2,501 (8.74%) were diagnosed with HDP. Compared with CON group, women with HDP were older and had a higher pre-pregnancy BMI. They were more often nulliparous, conceived through assisted reproductive technology, and reported a history of HDP. Education and household income were lower in the HDP group, whereas ethnicity and employment were similar between groups \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. Regarding health behaviors, smoking during pregnancy was significantly more prevalent in the HDP group. No significant differences were found in alcohol consumption, or in folic acid and multivitamin supplementation. Notably, SF levels were significantly higher in the HDP group.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeneral baseline information.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCON Group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;26106)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHDP Group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2501)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.18\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;35 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20706(79.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1834(73.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;35 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5400(20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e667(26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-pregnancy BMI (kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;18 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2076(8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88(3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;24 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19424(74.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1359(54.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u0026ndash;28 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3740(14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e699(27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e866(3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e355(14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eparity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP=0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNulliparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13861 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1413 (56.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12245 (46.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1088 (43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDP history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145(0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88(3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25961(99.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2413(96.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMode of conception\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural conception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24661 (94.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2225 (89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssisted reproductive technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1445 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e276 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24123 (92.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2326 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1983 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;=\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege diploma or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24518 (93.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2306 (92.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelow college diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1588 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnual household income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;200,000 CNY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16382 (62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1409 (56.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;200,000 CNY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9724 (37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1092 (43.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24232 (92.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2307 (92.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1874 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;=\u0026thinsp;0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e777 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25329 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2400 (96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1277 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24829 (95.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2391 (95.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFolic acid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2873 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23233 (89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2230 (89.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivitamin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5985 (22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e568 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20121 (77.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1933 (77.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSF measured in first trimester\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026micro;g/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.37\u0026thinsp;\u0026plusmn;\u0026thinsp;49.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.07\u0026thinsp;\u0026plusmn;\u0026thinsp;68.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;=\u0026thinsp;0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSF measured in second trimester(\u0026micro;g/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.80\u0026thinsp;\u0026plusmn;\u0026thinsp;20.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.92\u0026thinsp;\u0026plusmn;\u0026thinsp;28.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSF measured in third trimester\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026micro;g/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.82\u0026thinsp;\u0026plusmn;\u0026thinsp;13.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.49\u0026thinsp;\u0026plusmn;\u0026thinsp;21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb measured in first trimester\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(g/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.12\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.85\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAbbreviations:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;HDP, Hypertensive Disorders Of Pregnancy; SF, serum ferritin;BMI, body mass index\u003c/em\u003e\u003cem\u003e；\u003c/em\u003e\u003cem\u003eHb,hemoglobin;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e*P-values \u0026lt; 0.05\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;was considered statistically significant\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eLongitudinal Changes of SF During Pregnancy in HDP and CON Groups\u003c/h3\u003e\n\u003cp\u003eWe first analyzed the distribution of SF concentrations across different gestational stages. SF declined across gestation, with the steepest decrease from the first to the second trimester \u003cstrong\u003e(Figure S1).\u003c/strong\u003e To investigate the dynamic changes in SF levels between the HDP and CON groups further, we constructed a NLME model incorporating a random intercept for each participant. After adjusting for key maternal characteristics, including maternal age, prepregnancy BMI, Hb, parity, education level, mode of conception, and HDP history, the NLME model confirmed that SF levels remained consistently elevated in the HDP group across all three trimesters \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e Unadjusted trajectories showed similar patterns \u003cstrong\u003e(Figure S2)\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003eAssociation Between First-Trimester SF Levels and Risk of HDP\u003c/h3\u003e\n\u003cp\u003eTo examine the association between early-pregnancy SF and the subsequent HDP, first-trimester SF was categorized as: \u0026le;5th percentile (\u0026le;\u0026thinsp;15.635 \u0026micro;g/L), 5th\u0026ndash;95th percentile (reference), and \u0026gt;\u0026thinsp;95th percentile (\u0026gt;\u0026thinsp;165 \u0026micro;g/L). In the unadjusted model, both low and high SF levels were associated with an increased risk of HDP. After adjustment for potential confounders, only low SF remained associated (aOR\u0026thinsp;=\u0026thinsp;1.389, 95%CIs: 1.027\u0026ndash;1.879, P\u0026thinsp;=\u0026thinsp;0.033),, whereas high SF was not (aOR\u0026thinsp;=\u0026thinsp;1.154, 95% CIs: 0.864\u0026ndash;1.541, P\u0026thinsp;=\u0026thinsp;0.333) \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. To assess potential non-linear trends,RCS regression was conducted. Both univariable and multivariable models revealed a modest U-shaped association between first-trimester SF levels and HDP risk (non-linear P\u0026thinsp;=\u0026thinsp;0.0287 and 0.0456, respectively) \u003cstrong\u003e(Figure S3)\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociations between SF with HDP.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNon-adjust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAdjust\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1st\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eaOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;5th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.352(1.026\u0026ndash;1.786)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.389 (1.027\u0026ndash;1.879)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026minus;95th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;95th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.349(1.024\u0026ndash;1.7879)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.154 (0.864\u0026ndash;1.541)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2nd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.008(1.006\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.005(1.004\u0026ndash;1.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;5th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.767(0.613\u0026ndash;0.961)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.941 (0.746\u0026ndash;1.187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026minus;95th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;95th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.954(1.667\u0026ndash;2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.655 (1.401\u0026ndash;1.956)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3rd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.016(1.012\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.011(1.007\u0026ndash;1.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;5th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.925(0.624\u0026ndash;1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.054 (0.698\u0026ndash;1.593)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026minus;95th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;95th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.756(2.121\u0026ndash;3.582)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.077(1.565\u0026ndash;2.756)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eAdjust: Age, Prepregnancy BMI, Mode of conception,HDP History,Parity, Education level,Hb.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eOR, odds ratio; aOR, adjusted odds ratio.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eProgressive Association Between SF and HDP Risk Across Pregnancy Stages\u003c/h2\u003e\n \u003cp\u003eAs pregnancy advanced, elevated SF levels showed a notably stronger relationship with HDP risk. As shown in Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cstrong\u003eand Figure S4-S5\u003c/strong\u003e, in the second trimester, elevated SF levels were significantly associated with increased HDP risk \u003cem\u003e(aOR\u0026thinsp;=\u0026thinsp;1.655, 95% CIs: 1.401\u0026ndash;1.956, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/em\u003e, and this association became even stronger in the third trimester \u003cem\u003e(aOR\u0026thinsp;=\u0026thinsp;2.077, 95% CIs: 1.565\u0026ndash;2.756, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/em\u003e. These findings suggest that SF may play a more critical role in the progression of HDP as gestation advances. RCS indicated a monotonic increase in risk with higher SF across mid-to-late pregnancy. Based on these results, we further focused on third trimester SF and its relationship with HDP severity in subsequent analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssociation of Third-Trimester SF with HDP Severity\u003c/h3\u003e\n\u003cp\u003eOne-way ANOVA showed significant differences in SF levels across the four groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with the highest levels observed in the SPE group \u003cstrong\u003e(Figure S6)\u003c/strong\u003e. Multinomial logistic regression analysis revealed that individuals with SF levels above the 95th percentile had a significantly increased risk of developing GH (aOR\u0026thinsp;=\u0026thinsp;1.786, 95% CIs: 1.177\u0026ndash;2.709, P\u0026thinsp;=\u0026thinsp;0.006) and SPE (aOR 3.672, 95% CIs 2.456\u0026ndash;5.490), but not with PE \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e Low SF (\u0026le;\u0026thinsp;5th percentile) was not independently associated with any subtype. These findings suggest that the relationship between SF levels and HDP risk strengthens as pregnancy progresses.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociations between SF at third trimester with GH, PE and SPE.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNon-adjust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAdjust\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eaOR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;5th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76(0.413\u0026ndash;1.401)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78 (0.415\u0026ndash;1.466)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026minus;95th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;95th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.202(1.479\u0026ndash;3.277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.786 (1.177\u0026ndash;2.709)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;5th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.386 (0.745\u0026ndash;2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.861 (0.971\u0026ndash;3.565)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026minus;95th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;95th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.384 (0.723\u0026ndash;2.649)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.968 (0.497\u0026ndash;1.888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;5th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.761 (0.334\u0026ndash;1.733)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.937 (0.403\u0026ndash;2.179)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026minus;95th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;95th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.156 (3.54\u0026ndash;7.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.672 (2.456\u0026ndash;5.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eAdjust: Age, prepregnancy BMI, Mode of conception,HDP History, parity, Education level,Hb.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eOR, odds ratio; aOR, adjusted odds ratio.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eTrimester-Specific Reference Intervals for SF in Healthy Pregnant Women\u003c/h3\u003e\n\u003cp\u003eBecause trimester-specific reference intervals for SF during pregnancy are not routinely available, we derived gestational reference intervals using the indirect Hoffmann method in a predefined healthy subgroup without HDP, GDM, inflammation, or anemia. The results showed that the reference intervals for SF were 14.97\u0026ndash;215.83 \u0026micro;g/L in the first trimester, 6.67\u0026ndash;63.17 \u0026micro;g/L in the second trimester, and 5.92\u0026ndash;52.4 \u0026micro;g/L in the third trimester.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePrincipal Findings\u003c/h2\u003e\u003cp\u003eIn this large retrospective cohort study, we analyzed trimester-specific changes in SF levels and their association with HDP. SF declined with gestation yet remained consistently higher among women who developed HDP. A U-shaped relationship was observed in early pregnancy, with low SF associated with increased HDP risk. The association strengthened in later trimesters, particularly for SPE. These findings highlight SF as a dynamic biomarker for HDP risk and severity, supporting individualized monitoring and iron management.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eResults in the Context of What Is Known\u003c/h2\u003e\u003cp\u003eIn our study, the incidence of HDP was 8.74%, which falls within the commonly reported range of 5% to 10% in China\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn18\" id=\"#FNLinkFn18\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. HDP prevalence varies globally, with higher rates observed in Africa. Our findings are consistent with previous studies identifying advanced maternal age, obesity, and prior HDP as significant risk factors\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn19\" id=\"#FNLinkFn19\"\u003e\u003c/a\u003e\u003csup\u003e,\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn20\" id=\"#FNLinkFn20\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. However, the association between iron status and HDP remains inconclusive and requires further investigation.\u003c/p\u003e\u003cp\u003eOur results provide novel insights by demonstrating that SF concentrations decline progressively during pregnancy\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn21\" id=\"#FNLinkFn21\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e, consistent with physiological hemodilution and increased iron demands. Traditional SF thresholds (\u0026lt;\u0026thinsp;15 \u0026micro;g/L) may underestimate the prevalence of ID during pregnancy. To more accurately assess the relationship between iron status and HDP, we used a percentile-based approach, stratifying SF into extreme percentiles (\u0026lt;\u0026thinsp;5th and \u0026ge;\u0026thinsp;95th) rather than relying on a fixed threshold\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn22\" id=\"#FNLinkFn22\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. This method omay improve risk stratification relative to a single universal cut-point.\u003c/p\u003e\u003cp\u003eThe association between SF levels and HDP risk remains inconsistent in the literature. A large retrospective cohort study found a significant positive association between elevated first-trimester SF and HDP risk, suggesting excessive iron stores as a potential risk factor\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn23\" id=\"#FNLinkFn23\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e, although other studies have failed to identify a significant relationship\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn24\" id=\"#FNLinkFn24\"\u003e\u003c/a\u003e\u003csup\u003e,\u003c/sup\u003e \u003ca class=\"FNLink\" href=\"#Fn25\" id=\"#FNLinkFn25\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e.Our study found that lower first-trimester SF was more strongly associated with subsequent HDP development, which aligns with previous studies linking iron deficiency to increased HDP risk\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn26\" id=\"#FNLinkFn26\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. This highlights the importance of early iron monitoring to identify at-risk pregnancies. As pregnancy progresses, particularly in the second and third trimesters, elevated SF levels are significantly correlated with an increased risk of HDP\u003c/p\u003e\u003cp\u003ePrior literature variably links SF with HDP phenotypes. Our severity-stratified, trimester-aware analysis aligns with GH showing biphasic signals: low first-trimester (ID) SF and elevated mid/late SF both associated with GH. For PE, a more complex intermediate phenotype, associations were modest and clearest in the second trimester\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn27\" id=\"#FNLinkFn27\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e; third-trimester elevations were not independent, consistent with reports that SF tracks inflammation\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn28\" id=\"#FNLinkFn28\"\u003e\u003c/a\u003e\u003csup\u003e,\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn29\" id=\"#FNLinkFn29\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e and Mendelian randomization arguing against causality\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn30\" id=\"#FNLinkFn30\"\u003e\u003c/a\u003e\u003csup\u003e,\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn31\" id=\"#FNLinkFn31\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. SPE showed the strongest, consistent links to elevated SF in mid/late gestation. Together with published data, these results support phenotype- and gestational-stage\u0026ndash;specific interpretation of SF and argue against a single universal threshold, informing risk triage and monitoring.\u003c/p\u003e\u003cp\u003eWe derived trimester-specific SF reference intervals using an internal healthy population and the indirect Hoffman method on a single analyzer. To limit pathologic upward shift, we excluded pregnancies with HDP, GDM, anemia, and recorded inflammatory conditions. Notably, the lower limit of SF in the first trimester (14.97 \u0026micro;g/L) closely approximates the diagnostic threshold for ID (15 \u0026micro;g/L), supporting the validity of our findings. Given the significant differences in SF levels between early, mid-, and late pregnancy, trimester-specific reference intervals are warranted to guide iron monitoring and clinical decision-making. Future research should incorporate a broader range of pregnancy-related complications to further refine the clinical applicability of SF reference standards.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eClinical Implications\u003c/h2\u003e\u003cp\u003eClinically, our findings support gestational stage\u0026ndash;specific, dynamic interpretation of SF. In early pregnancy, low SF typically indicates iron deficiency and may coincide with impaired placentation. In mid-to-late gestation, higher SF aligns with oxidative and inflammatory activity\u0026mdash;plausible pathways include Fenton-reaction reactive oxygen species and ferroptosis\u0026mdash;processes that can injure placental and vascular function. Because SF is an acute-phase reactant and values are influenced by hemodilution, a single universal threshold is inappropriate. We recommend assessing iron status at booking and repeating SF at ~\u0026thinsp;28\u0026ndash;32 weeks or when risk flags emerge, interpreting results alongside conventional risk factors and fetal growth. Persistently elevated late-pregnancy SF may signal placental dysfunction. Integrating SF with inflammatory and ferroptosis biomarkers could further refine risk-adapted care.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\u003cp\u003eThis study benefits from a large retrospective cohort, longitudinal data collection, trimester-specific SF measurements, and detailed classification of HDP subtypes, enabling a nuanced analysis of temporal and phenotypic associations. However, several limitations should be acknowledged. First, this was a single-center study, which may limit generalizability. Second, causal inference is restricted due to the observational design. Third, inflammatory markers such as C-reactive protein (CRP) were not available, limiting interpretation of the inflammatory context of SF fluctuations. Lastly, iron supplementation data, including dose and frequency, were not systematically recorded, and SF data completeness varied across trimesters, with only a minority of participants having measurements at all three time points.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSF shows a gestational stage–dependent relationship with HDP: in early pregnancy, low SF relates modestly to subsequent HDP, whereas in mid-to-late gestation elevated SF is more strongly associated with disease presence and severity, particularly severe preeclampsia. These data support SF as a dynamic, stage-specific risk marker to aid risk stratification rather than a standalone diagnostic or treatment threshold. Given SF’s acute-phase properties, associations likely reflect inflammatory and placental-dysfunction pathways rather than direct causality; extreme elevations may participate via oxidative/ferroptotic mechanisms. Prospective multicenter studies should define trimester-specific cutoffs, assess incremental predictive value over established risk factors, and disentangle iron overload from inflammation-driven elevation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was approved by the Ethics Committee of Beijing Obstetrics and Gynecology Hospital, Capital Medical University (Approval No. 2018-KY-003-02). Written informed consent was obtained from all participants prior to enrolment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their gratitude to all study participants and staff involved in participant recruitment. This study utilised data from the China Birth Cohort Study (CBCS) conducted at Beijing Obstetrics and Gynecology Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTian Zongyuan contributed to the study concept and design, conducted data analysis using specialised software, and drafted the manuscript. Su Shaofei and Liu Ruixia provided critical supervision and intellectual guidance throughout the research. Yin Chenghong acquired funding and supported project coordination. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eConsent to Publish declaration: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be\u0026nbsp;construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis study was supported by the National Key Research and Development Program of China (2016YFC1000101).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to institutional and patient privacy restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":" References","content":"\u003col\u003e\n\u003cli\u003eWu, P.; Green, M.; Myers, J. E. Hypertensive Disorders of Pregnancy. Stroke Vasc Neurol 2023, e071653. https://doi.org/10.1136/bmj-2022-071653.\u003c/li\u003e\n\u003cli\u003eGhossein‐Doha, Chahinda, et al. \u0026ldquo;Hypertensive Pregnancy Disorder, an Under‐recognized Women Specific Risk Factor for Heart Failure?\u0026rdquo; European Journal of Heart Failure, Nov. 2024, https://doi.org/10.1002/ejhf.3520.\u003c/li\u003e\n\u003cli\u003eCountouris, M.; Mahmoud, Z.; Cohen, J. B.; Crousillat, D.; Hameed, A. B.; Harrington, C. M.; Hauspurg, A.; Honigberg, M. C.; Lewey, J.; Lindley, K.; McLaughlin, M. M.; Sachdev, N.; Sarma, A.; Shapero, K.; Sinkey, R.; Tita, A.; Wong, K. E.; Yang, E.; Cho, L.; Bello, N. A. Hypertension in Pregnancy and Postpartum: Current Standards and Opportunities to Improve Care. Circulation 2025, 151 (7), 490\u0026ndash;507. https://doi.org/10.1161/circulationaha.124.073302.\u003c/li\u003e\n\u003cli\u003eFord, N. D.; Cox, S.; Ko, J. Y.; Ouyang, L.; Romero, L.; Colarusso, T.; Ferre, C. D.; Kroelinger, C. D.; Hayes, D. K.; Barfield, W. D. Hypertensive Disorders in Pregnancy and Mortality at Delivery Hospitalization \u0026mdash; United States, 2017\u0026ndash;2019. MMWR Morb. Mortal. Wkly. Rep. 2022, 71 (17), 585\u0026ndash;591.\u003c/li\u003e\n\u003cli\u003eElovitz, M. A.; Gee, E. P. S.; Delaney-Busch, N.; Moe, A. B.; Reddy, M.; Khodursky, A.; La, J.; Abbas, I.; Mekaru, K.; Collins, H.; Siddiqui, F.; Nolan, R.; Boelig, R. C.; Kiefer, D. G.; Simmons, P. M.; Saade, G. R.; Saad, A.; Carter, E. B.; McElrath, T. F.; Quake, S. R.; DePristo, M. A.; Haverty, C.; Lee, M.; Namsaraev, E.; Berghella, V.; Collier, A. Y.; Frolova, A. I.; Park-Hwang, E.; Pacheco, L. D.; Sutton, E. F.; Jain, M.; Rood, K.; Grobman, W. A.; Biggio, J. R.; Gyamfi-Bannerman, C.; Jeyabalan, A.; Rasmussen, M. Molecular Subtyping of Hypertensive Disorders of Pregnancy. Nat Commun 2025, 16 (1). https://doi.org/10.1038/s41467-025-58157-y.\u003c/li\u003e\n\u003cli\u003eGeorgieff, M. K. Iron Deficiency in Pregnancy. Am J Obstet Gynecol 2020, 223 (4), 516\u0026ndash;524. https://doi.org/10.1016/j.ajog.2020.03.006.\u003c/li\u003e\n\u003cli\u003eMilman, N.; Taylor, C. L.; Merkel, J.; Brannon, P. M. Iron Status in Pregnant Women and Women of Reproductive Age in Europe. Am J Clin Nutr 2017, 106, 1655S-1662S. https://doi.org/10.3945/ajcn.117.156000.\u003c/li\u003e\n\u003cli\u003eArosio, P.; Cairo, G.; Bou-Abdallah, F. A Brief History of Ferritin, an Ancient and Versatile Protein. IJMS 2024, 26 (1), 206. https://doi.org/10.3390/ijms26010206.\u003c/li\u003e\n\u003cli\u003eEvanchuk, J. L.; Kozyrskyj, A.; Hanas, N.; Goruk, S.; Vaghef-Mehrabani, E.; Archundia-Herrera, C. M.; O\u0026rsquo;Brien, K. O.; Letourneau, N. L.; Giesbrecht, G. F.; Bell, R. C.; Field, C. J. Maternal Iron Status Is Dynamic Throughout Pregnancy and Might Predict Birth Outcomes in a Sex Dependent Manner: Results from the Alberta Pregnancy Outcomes and Nutrition (APrON) Cohort Study. J Nutr 2023, 153 (9), 2585\u0026ndash;2597. https://doi.org/10.1016/j.tjnut.2023.06.042.\u003c/li\u003e\n\u003cli\u003eMilman, N.; Taylor, C. L.; Merkel, J.; Brannon, P. M. Iron Status in Pregnant Women and Women of Reproductive Age in Europe. Am J Clin Nutr 2017, 106, 1655S-1662S. https://doi.org/10.3945/ajcn.117.156000.\u003c/li\u003e\n\u003cli\u003eZhang, Y.; Lu, Y.; Jin, L. Iron Metabolism and Ferroptosis in Physiological and Pathological Pregnancy. IJMS 2022, 23 (16), 9395. https://doi.org/10.3390/ijms23169395.\u003c/li\u003e\n\u003cli\u003eGeorgsen, M.; Krog, M. C.; Korsholm, A.-S.; Hvidman, H. W.; Kolte, A. M.; Rigas, A. S.; Ullum, H.; Ziebe, S.; Andersen, A. N.; Nielsen, H. S.; Hansen, M. B. Serum Ferritin Level Is Inversely Related to Number of Previous Pregnancy Losses in Women with Recurrent Pregnancy Loss. Fertil Steril 2020, 115 (2), 389\u0026ndash;396. https://doi.org/10.1016/j.fertnstert.2020.08.1410.\u003c/li\u003e\n\u003cli\u003eGuo, Y.; Zhang, N.; Zhang, D.; Ren, Q.; Ganz, T.; Liu, S.; Nemeth, E. Iron Homeostasis in Pregnancy and Spontaneous Abortion. Am J Hematol 2018, 94 (2), 184\u0026ndash;188. https://doi.org/10.1002/ajh.25341.\u003c/li\u003e\n\u003cli\u003eZhang, Z.; Li, X.; Zhou, X.; Zhang, Y.; Gan, X.; Xu, X.; Wu, H. Association of Gestational Hypertriglyceridemia, Diabetes with Serum Ferritin Levels in Early Pregnancy: A Retrospective Cohort Study. Front. Endocrinol. 2023, 14. https://doi.org/10.3389/fendo.2023.1067655.\u003c/li\u003e\n\u003cli\u003eLi, N.; Yan, S.; Weng, J.; Liang, G.; Gong, Y.; Su, Y.; Wei, X.; Ren, W.; Zhen, Q.; Zhu, J.; Liu, F.; Zhang, F.; Wang, Y. Association of Mid-Pregnancy Ferritin Levels with Postpartum Glucose Metabolism in Women with Gestational Diabetes. Nutr. Diabetes 2024, 14 (1). https://doi.org/10.1038/s41387-024-00338-7.\u003c/li\u003e\n\u003cli\u003eFang, Z.; Zheng, S.; Xie, Y.; Lin, S.; Zhang, H.; Yan, J. Correlation between Serum Ferritin in Early Pregnancy and Hypertensive Disorders in Pregnancy. Front. Nutr. 2023, 10. https://doi.org/10.3389/fnut.2023.1151410.\u003c/li\u003e\n\u003cli\u003eLewandowska, M.; Sajdak, S.; Lubiński, J. Can Serum Iron Concentrations in Early Healthy Pregnancy Be Risk Marker of Pregnancy-Induced Hypertension? Nutrients 2019, 11 (5), 1086. https://doi.org/10.3390/nu11051086.\u003c/li\u003e\n\u003cli\u003eLi F, Qin J, Zhang S, Chen L. Prevalence of hypertensive disorders in pregnancy in China: A systematic review and meta-analysis. Pregnancy Hypertens. 2021;24:13-21. doi:10.1016/j.preghy.2021.02.001\u003c/li\u003e\n\u003cli\u003eJiang, L.; Tang, K.; Magee, L. A.; Dadelszen, P. von; Ekeroma, A.; Li, X.; Zhang, E.; Bhutta, Z. A. A Global View of Hypertensive Disorders and Diabetes Mellitus during Pregnancy. Nature Reviews Endocrinology 2022, 18. https://doi.org/10.1038/s41574-022-00734-y.\u003c/li\u003e\n\u003cli\u003eAbdurrahman, A.; Adamu, A. N.; Ashimi, A.; Adekunle, O. O.; Bature, S. B.; Aliyu, L. D.; Akeem, O.; Abdullahi, H.; Lavin, T.; Daneji, S.; Musa, B.; Muazu, Z.; Tukur, J.; Galadanci, H. S. Predictors, Prevalence and Outcome of Hypertensive Disorders in Pregnancy in Nigerian Tertiary Health Facilities. BJOG 2024, 131 (S3), 42\u0026ndash;54. https://doi.org/10.1111/1471-0528.17902.\u003c/li\u003e\n\u003cli\u003eMei Z, Addo OY, Jefferds MED, Flores-Ayala RC, Brittenham GM. Physiologically based trimester-specific serum ferritin thresholds for iron deficiency in US pregnant women. Blood Adv. 2024;8(14):3745-3753. doi:10.1182/bloodadvances.2024013460\u003c/li\u003e\n\u003cli\u003eRay JG, Berger H, Park AL. Population-based study of serum ferritin in early pregnancy and adverse perinatal outcomes. Paediatr Perinat Epidemiol. 2020;34(6):706-712. doi:10.1111/ppe.12687\u003c/li\u003e\n\u003cli\u003eFang, Z.; Zheng, S.; Xie, Y.; Lin, S.; Zhang, H.; Yan, J. Correlation between Serum Ferritin in Early Pregnancy and Hypertensive Disorders in Pregnancy. Front. Nutr. 2023, 10. https://doi.org/10.3389/fnut.2023.1151410.\u003c/li\u003e\n\u003cli\u003eTaeubert, M. J.; Wiertsema, C. J.; Vermeulen, M. J.; Quezada-Pinedo, H. G.; Reiss, I. K.; Muckenthaler, M. U.; Gaillard, R. Maternal Iron Status in Early Pregnancy and Blood Pressure Throughout Pregnancy, Placental Hemodynamics, and the Risk of Gestational Hypertensive Disorders. J. Nutr. 2022, 152 (2), 525\u0026ndash;534. https://doi.org/10.1093/jn/nxab368.\u003c/li\u003e\n\u003cli\u003eYang, L.; Wu, L.; Liu, Y.; Chen, H.; Wei, Y.; Sun, R.; Shen, S.; Zhan, B.; Yang, J.; Deng, G. Association Between Serum Ferritin Concentration and Risk of Adverse Maternal and Fetal Pregnancy Outcomes: A Retrospective Cohort Study. DMSO 2022, Volume 15, 2867\u0026ndash;2876. https://doi.org/10.2147/dmso.s380408.\u003c/li\u003e\n\u003cli\u003eLewandowska, M.; Sajdak, S.; Lubiński, J. Can Serum Iron Concentrations in Early Healthy Pregnancy Be Risk Marker of Pregnancy-Induced Hypertension? Nutrients 2019, 11 (5), 1086. https://doi.org/10.3390/nu11051086.\u003c/li\u003e\n\u003cli\u003eM\u0026oslash;ller, H. I.; Persson, G.; Klok, F. B.; Vojdeman, F. J.; Lebech, M.; Hviid, T. V. F. Investigations of Leukocyte and Inflammatory Markers in Pregnancies Complicated by Preeclampsia. J. Reprod. Immunol. 2023, 160, 104163. https://doi.org/10.1016/j.jri.2023.104163.\u003c/li\u003e\n\u003cli\u003eGutierrez-Aguirre C H, Garc\u0026iacute;a-Lozano J A, Trevi\u0026ntilde;o-Montemayor O R, et al. Comparative analysis of iron status and other hematological parameters in preeclampsia [J]. Hematology, 2016, 22(1): 36\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eAires Rodrigues de Freitas M, Vieira da Costa A, Alves de Medeiros L, et al. Are There Differences in the Anthropometric, Hemodynamic, Hematologic, and Biochemical Profiles between Late- and Early-Onset Preeclampsia? [J]. Obstetrics and Gynecology International, 2018, 2018: 1\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eLi P, Wang H, Chen T, et al. Association between iron status, preeclampsia and gestational hypertension: A bidirectional two-sample Mendelian randomization study. J Trace Elem Med Biol. 2024;86:127528. doi:10.1016/j.jtemb.2024.127528\u003c/li\u003e\n\u003cli\u003eYang, X.; Wei, J.; Sun, L.; Zhong, Q.; Zhai, X.; Chen, Y.; Luo, S.; Tang, C.; Wang, L. Causal Relationship between Iron Status and Preeclampsia-Eclampsia: A Mendelian Randomization Analysis. Clin. Exp. Hypertens. 2024, 46 (1). https://doi.org/10.1080/10641963.2024.2321148.\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":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Serum ferritin, Hypertensive disorders of pregnancy, Iron metabolism, Reference intervals, Retrospective Cohort","lastPublishedDoi":"10.21203/rs.3.rs-8169665/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8169665/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSerum ferritin (SF) varies across gestation and reflects iron stores. Evidence on whether trimester-specific SF relates to the risk and severity of hypertensive disorders of pregnancy (HDP) remains limited, and clinicians lack gestational reference intervals to guide interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo investigate trimester-specific changes in SF levels during pregnancy, evaluate the association between abnormal SF concentrations and the risk and severity of HDP, and establish gestational reference intervals to inform clinical interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis retrospective cohort study included 28,607 pregnant women who received prenatal care at a single medical institution, of whom 2,501 developed HDP. Longitudinal changes of SF were analyzed using nonlinear mixed-effects (NLME) models. Multivariable logistic regression estimated adjusted odds ratios (aORs) for HDP across trimester-specific SF categories. Associations with severity were examined for gestational hypertension (GH) and severe preeclampsia (SPE). Trimester-specific SF reference intervals were derived in a healthy subgroup using the Hoffman method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Compared with the CON group, women with HDP had higher SF, most notably in the second and third trimesters. In the first trimester, both low and high SF were associated with higher HDP risk, although only low SF remained after adjustment (aOR 1.389, 95% CIs 1.027–1.879). Elevated SF showed stronger associations with HDP in the second (aOR 1.655, 95% CIs 1.401–1.956) and third trimesters (aOR 2.077, 95% CIs 1.565–2.756). The highest SF category was further associated with GH (aOR 1.786, 95% CIs 1.177–2.709) and SPE (aOR 3.672, 95% CIs 2.456–5.490), suggesting a dose–response pattern. Trimester-specific reference intervals were: first 14.97–215.83 μg/L; second 6.67–63.17 μg/L; third 5.92–52.40 μg/L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e SF demonstrates trimester-specific associations with the risk and severity of HDP. Low first-trimester SF aligns with iron deficiency signals, whereas higher SF in mid-to-late pregnancy marks higher HDP risk. These results support individualized interpretation and monitoring of SF rather than routine one-size-fits-all supplementation.\u003c/p\u003e","manuscriptTitle":"Association of Serum Ferritin With Hypertensive Disorders of Pregnancy: A Longitudinal Analysis From a Large Retrospective Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 12:18:25","doi":"10.21203/rs.3.rs-8169665/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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