Physiologically Based Ferritin Thresholds to Redefine Early Pregnancy Iron Screening: A Cross-Sectional Study

Physiologically Based Ferritin Thresholds to Redefine Early Pregnancy Iron Screening: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Physiologically Based Ferritin Thresholds to Redefine Early Pregnancy Iron Screening: A Cross-Sectional Study Huan Nguyen Pham, Vy Thi Thao Nguyen, Phuc Nguyen Huu Pham, Nghiem Xuan Huynh, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8652241/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Iron deficiency in early pregnancy is common yet frequently missed because current screening strategies rely on ferritin cutoffs designed to detect iron deficiency anemia. We aimed to establish physiologically based ferritin functional reference limits (FRLs) in healthy, non-anemic pregnant women to improve early identification of iron deficiency. Methods The study was conducted at a maternity hospital in Ho Chi Minh City, Vietnam, enrolling first-trimester pregnant women. Participants were included if they had hemoglobin ≥ 11 g/dL, no evidence of infection, body-mass index < 30 kg/m², no microcytosis or hypochromia. The primary outcome was the ferritin FRLs, defined using restricted cubic spline modelling of blood indices. Diagnostic performance was assessed against TSAT. Findings 452 patients had complete data for validation. The final suggested ferritin FRL is 59 ng/mL, identified more women with non-anemic iron deficiency compared with traditional cutoffs. Validation against TSAT showed excellent rule-out performance with negative predictive value of 95.8%. A four-zone classification for iron deficiency emerged, including absolute deficiency (< 15 ng/mL), deficiency (15–26 ng/mL), indeterminate status (26–59 ng/mL), and physiologic sufficiency (≥ 59 ng/mL). Interpretation Physiologically derived ferritin thresholds identify early iron deficiency more effectively than conventional cutoffs and provide a grounded framework for screening in first trimester. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Iron requirements increase markedly during pregnancy to support fetal development and maternal erythropoiesis. While iron deficiency anemia is routinely screened, non-anemic iron deficiency (NAID) remains under-recognized despite its association with maternal fatigue, preterm birth, and impaired neurodevelopment in offspring.[ 1 , 2 ] The traditional ferritin cutoff of 30 ng/mL prioritizes detection of iron deficiency anemia but may fail to capture functional iron deficiency[ 3 ], estimated to range from 14% [ 4 ] to 20.7%,[ 5 ] that precedes anemia. Recent studies propose using functional reference limits (FRLs) derived from physiological relationships between ferritin and erythrocyte indices rather than fixed, pathophysiological thresholds.[ 6 , 7 ] Given that there are needs of evidence to support screening for iron deficiency without anemia,[ 8 ] we therefore aimed to define physiologically based ferritin thresholds in early pregnancy using restricted cubic spline modelling of ferritin against erythrocyte indices, and to assess the application of FRLs in screening of iron deficiency versus traditional reference intervals. MATERIALS AND METHODS Study design This study was conducted in Outpatient clinic, Hung Vong Hospital, Vietnam. Data was collected from March to August 2025. The study was approved by Ethical Committee of Hung Vuong Hospital, Ho Chi Minh City, Vietnam with registration CS/HV/24/42. This was an observational cross-sectional study conducted during routine first-trimester antenatal care and no intervention or randomization was performed. The study was registered on ClinicalTrials.gov as an observational study (NCT06990373). Participants Participants were eligible if they had a singleton pregnancy in the first trimester (≤ 13 weeks 6 days), systolic blood pressure < 140 mmHg and diastolic pressure < 90 mmHg, and a body temperature between 35.0°C and 37.5°C at the time of examination. Exclusion criteria included history of diabetes, hypertension, hemoglobinopathies, leukemia, other non-hematologic cancers, systemic lupus erythematosus, or gastrointestinal disorders affecting iron absorption (e.g., celiac disease, Crohn’s, ulcerative colitis, prior gastric surgery); current use of acid-suppressing drugs; using moderate to high dose of elemental iron (> 30 mg) supplements; active smoking. Post-enrolment exclusion criteria are hemoglobin concentration ≥ 11 g/dL, positive tests for Treponema pallidum, hepatitis B surface antigen, human immunodeficiency virus (HIV), or hepatitis C virus (HCV); and mean corpuscular volume (MCV) < 80 fL or mean corpuscular hemoglobin (MCH) < 27 pg (except for those with normal electrophoresis). All participants provided informed consent. Procedures Between March and September 2025, data were prospectively collected at Hung Vuong Hospital (Ho Chi Minh City, Vietnam) during routine first-trimester antenatal visits (≤ 13 weeks 6 days). Trained staff conducted interviews, physical exams, and drew venous blood following standard protocols. Demographic and clinical data-including age, gestational age, parity, BMI, blood pressure, and temperature-were recorded via patient interviews and electronic records. Laboratory measurements included hemoglobin (HGB), serum iron, ferritin, unsaturated iron-binding protein capacity (UIBC), total iron-binding capacity (TIBC) calculated from UIBC and serum iron, and calculated transferrin saturation (TSAT). Hemoglobin was measured using DxH 900 automated hematology analyzer, while serum iron, TIBC and ferritin were analyzed using standardized colorimetric, measured on Cobas c502, and chemiluminescent immunoassays, measured on Cobas e801 (Roche Diagnostics, Rotkreuz, Switzerland), in the hospital’s central laboratory. TSAT was derived as the ratio of serum iron to TIBC multiplied by 100. The primary outcome was the determination of functional reference limit and reference intervals (2.5th to 97.5th percentiles) of ferritin. The secondary outcome was the prevalence of non-anemic iron deficiency (NAID), defined as Hb ≥ 11 g/dL in conjunction with traditional cutoff such as ferritin < 30 ng/mL or TSAT 200 ng/mL, were excluded from the study. Statistical analysis Data analysis was performed using Jupyter Notebook with pandas, statsmodels, scipy, and matplotlib libraries. Continuous variables were summarized as means ± standard deviations, and categorical variables as counts and percentages. Reference intervals (2.5th − 97.5th percentiles) for ferritin were estimated using the non-parametric percentile method following CLSI EP28-A3c guidelines.[ 9 ] Outliers (ferritin 200 ng/mL; TSAT 45%) were excluded. Functional reference limits (FRLs) were determined by modelling the relationship between serum ferritin and erythrocyte indices (MCV, MCH, and HGB) via restricted cubic spline regression, excluding individuals with ferritin > 200 ng/mL to prevent skew from iron overload.[ 10 ] The FRL was identified as the lowest ferritin value at which the 95% bootstrap confidence interval of the spline’s first derivative encompassed zero across a continuous range, signifying that further increases in ferritin no longer produced measurable changes in erythrocyte indices. Each model was resampled 500 times using non-parametric bootstrapping to obtain robust estimates and 95% confidence intervals. RESULTS Of 596 enrolled participants, 43 with anemia were excluded, leaving 553 for screening. Among these 553, we excluded 25 with infection (HIV, HBV, HCV, or Treponema pallidum ), 6 with hypertension, 15 with BMI ≥ 30, 6 with hemoglobinopathies on electrophoresis, and 40 with microcytosis/hypochromia (MCV < 80 fL or MCH < 27 pg) without electrophoresis results. After selecting qualified patients, 9 samples failed quality control, yielding a final analytic cohort of 452 patients (Fig. 1 ). The characteristics of patients are described in Table 1 . Overall, a total of 452 first-trimester pregnant patients were enrolled. The mean maternal age was 29.8 ± 5.5 years, and the mean gestational age at enrolment was 11.1 ± 1.7 weeks. Participants had an average weight of 54.2 ± 7.5 kg and height of 156.7 ± 5.4 cm. The mean systolic and diastolic blood pressures were 113.9 ± 9.0 mmHg and 71.0 ± 7.2 mmHg, respectively. Approximately half of the cohort were nulliparous (49.1%), while 50.9% were multigravida. These characteristics reflect a generally healthy and homogeneous group of patients representing early normal pregnancy. Table 1 Patient characteristics Baseline Characteristics Mean ± SD Age (years) 29.81 ± 5.51 Gestational age (weeks) 11.10 ± 1.66 Weight (kg) 54.16 ± 7.49 Height (cm) 156.72 ± 5.37 Systolic blood pressure 113.94 ± 9.03 Diastolic blood pressure 71.00 ± 7.23 Parity Counts (Percentage) Primigravida 222 (49.1%) Multigravida 230 (50.9%) Functional reference limits (FRLs) for ferritin were determined using restricted cubic spline (RCS) models relating ferritin to MCV, MCH, and HGB. Five knots were placed at the 5th, 25th, 50th, 75th, and 95th percentiles of ferritin, following recommended practice for flexible non-linear modelling.[ 11 ] To improve robustness, MCV, MCH, and HGB were standardized (z-scores) and averaged into a composite index, following established recommendations for constructing composite indicators.[ 12 ] We then estimated the derivative (slope with respect to ferritin) on a fine grid of ferritin values. To account for sampling variability, we performed non-parametric bootstrapping (500 iterations) for internal validation, refitted the spline model in each bootstrap sample, and recomputed the derivative curve. At each ferritin grid point, the 2.5th and 97.5th percentiles of the bootstrapped derivatives defined the 95% confidence interval (CI). The FRL was defined as the lowest ferritin concentration at which this 95% CI of the derivative encompassed zero for a sustained run of points, indicating a plateau of response. All four models converged on a ferritin inflection point near 55–60 ng/mL, marking the plateau of erythrocyte indices that reflects physiologic iron adequacy (Fig. 2 ). For MCV, the functional reference limit (FRL) was estimated at 55.3 ng/mL (95% CI, 41.9–81.0), with the response curve flattening between approximately 40 and 80 ng/mL. The MCH analysis produced a similar FRL of 56.2 ng/mL (95% CI, 46.9–123.6), although the plateau extended over a broader ferritin range, consistent with greater variability at higher concentrations. The HGB analysis produced a FRL of 59.1 with tighter confidence interval (95%CI 56.0–88.9). When MCV, MCH and HGB were combined into a standardized composite z-score, the resulting FRL was 58.6 ng/mL (95% CI, 56.2–96.4), nearly identical to the single-marker estimates but with narrower confidence limits than those observed for HGB alone. For comparison, the reference interval derived from the study indicated a lower limit of 26.1 ng/mL (90% CI, 19.1–33.1) and an upper limit of 187.1 ng/mL (90% CI, 180.2–194.1). To validate the use of FRL, we tabulated the FRL with TSAT of 16% as the reference definition. Application of the FRL derived from MCV, MCH, HGB and the composite index (MCV, MCH, and HGB) against TSAT yielded nearly identical classification performance (Table 2 ). The FRLs were consistent across HGB and composite at around 59 ng/mL, with each identifying 272 true negatives and only 12 false negatives but also misclassifying approximately 85 false positives. The negative predictive value (NPV) was very high (95.8%), indicating that individuals above the FRL threshold were very unlikely to be iron-deficient. Clinically, a ferritin FRL around 59 ng/mL offers strong reassurance for ruling out iron deficiency (high NPV). Table 2 Diagnostic performance of FRLs in predicting iron deficiency Model n FRL (95%CI) True Positive False Positive True Negative False Negative Sens% (95%CI) Specs% (95%CI) PPV (95%CI) NPV (95%CI) MCV ~ Ferritin 389 55.3 (41.9–81.0) 17 74 283 15 53.1% (36.4–69.1) 79.3% (74.8–83.2) 18.7% (12.0–27.9) 95.0% (91.9–96.9) MCH ~ Ferritin 389 56.2 (46.9–123.6) 17 75 282 15 53.1% (36.4–69.1) 79.0% (74.5–82.9) 18.5% (11.9–27.6) 94.9% (91.8–96.9) HGB ~ Ferritin 389 59.1 (56.0–89.9) 20 85 272 12 62.5% (45.3–77.1) 76.2% (71.5–80.3) 19.0% (12.7–27.6) 95.8% (92.8–97.6) Composite (MCV + MCH) 389 58.6 (56.2–96.4) 20 85 272 12 62.5% (45.3–77.1) 76.2% (71.5–80.3) 19.0% (12.7–27.6) 95.8% (92.8–97.6) The prevalence of non-anemic iron deficiency (NAID) varied depending on the applied cutoff criteria (Fig. 3 ). Using the FRL for ferritin derived from above (59 ng/mL), the prevalence of NAID was 23.2%. Conventional thresholds yielded substantially lower estimates: ferritin < 30 ng/mL identified only 5.1%, and ferritin < 15 ng/mL just 1.5% of the study population. When using transferrin saturation (TSAT) as a cutoff, the prevalence was 7.3% at the < 16% cutoff and 15.5% at the < 20% cutoff. Combining ferritin < 100 ng/mL with TSAT < 16% increased prevalence to 6.2%, while combining ferritin < 100 ng/mL with TSAT < 20% increased prevalence to 10.2%. Overall, the FRL cutoff identified a larger proportion of pregnant patients with iron deficiency compared to traditional ferritin or TSAT thresholds while iron overload also accounts for a substantial proportion of the population (13.9%). Using restricted cubic-spline modeling of hematologic indices (MCV, MCH, and hemoglobin), we identified physiologically distinct ferritin zones that reflect the transition from iron-deficient to iron-sufficient erythropoiesis. Three data-driven thresholds emerged (Fig. 4 ): the established absolute iron-deficiency cutoff at < 15 ng/mL, the lower reference interval (RI) limit at 26 ng/mL, and the functional reference limit (FRL) at 59 ng/mL, representing the point beyond which ensure iron sufficiency throughout pregnancy. These thresholds delineate four meaningful physiologic categories: absolute iron deficiency (< 15 ng/mL), iron deficiency (15–26 ng/mL), possible iron deficiency (26–59 ng/mL), where TSAT confirmation is recommended, and iron sufficiency (≥ 59 ng/mL). DISCUSSION This study defines the first physiologically based ferritin functional reference limit for non-anemic pregnant women, achieved by removing contributors that spuriously increase ferritin concentrations, including infection and obesity.[ 13 ] Using red cell indices as biological correlates, we identified a consistent threshold around 55–60 ng/mL at which erythrocyte indices plateau, marking adequate iron supply for erythropoiesis. These values are substantially higher than the traditional ferritin cutoff of 30 ng/mL used in clinical practice or other physiologically based ferritin threshold in studies by Addo OY et al.[ 7 ] or Z.Mei et al.,[ 14 ] probably because our study excluded anemic patients and those with hemoglobinopathy states. The findings support a re-evaluation of ferritin thresholds for screening, and a threshold to determine iron deficiency in non-anemic patient. Our derived lower limit of the ferritin reference interval (~ 26 ng/mL; 2.5th percentile) closely aligns with physiologically based thresholds recently other studies. For example, in a US cohort of pregnant patients, the functional inflection-point ferritin threshold in the first trimester was identified as 25.8 µg/L (95% CI 18.1–28.5) using restricted cubic-spline modelling of ferritin versus hemoglobin and soluble transferrin receptor.[ 15 ] Likewise, in apparently healthy non-pregnant patients of reproductive age, the physiologically based threshold was estimated at < 24.8 µg/L (95% CI 24.4–25.2).[ 7 ] The closeness of our lower reference limit (~ 26 ng/mL) to these physiologic cut-points strengthens the plausibility that our interval is capturing the threshold of iron deficiency in early pregnancy. In other words, patients with ferritin values below 26 ng/mL may already be entering the zone of iron-deficient erythropoiesis or sub-optimal iron status, even in the absence of anemia. Our data thus support the notion that the conventionally accepted ferritin cut‐off (e.g., 30 ng/mL) may underestimate the onset of functional deficiency, and that a threshold in the mid-20s ng/mL aligns with both population‐based percentiles and physiological modelling of iron supply versus demand. To further consolidate the threshold, we validated our FRL against TSAT. We employed a cutoff of 16%, which has a sensitivity of 20% and a specificity of 93% [ 16 ] for identifying iron deficiency. Indeed, when tested against TSAT < 16%, the FRL demonstrated good specificity (76.2%) and an extremely high negative predictive value (95.8%), supporting its clinical utility as a rule-out boundary for iron deficiency. Therefore, we propose a two-step algorithm: (1) screen with ferritin; (2) for values 26–59 ng/mL, an additional step of measuring TSAT is needed. This strategy balances sensitivity and specificity and mitigates ferritin’s acute-phase confounding.[ 17 ] From a clinical standpoint, our findings support a decisive shift away from long-standing ferritin cutoffs toward thresholds that are functionally and physiologically validated. Importantly, clinical decision limits must be distinguished from statistical reference limits. Ferritin values below 15 ng/mL—the red zone in Fig. 4 —correspond to absolute iron deficiency, a state well documented in bone-marrow studies, and warrant prompt therapeutic replacement.[ 16 ] Between 15 ng/mL and the lower reference limit identified in our study (~ 26 ng/mL; orange zone), patients likely exhibit early-stage or functional iron deficiency and should receive moderate to high iron dose supplementation to prevent progression. The intermediate yellow zone (26–59 ng/mL) represents an area of physiological uncertainty: inflammation-associated ferritin elevation in pregnancy complicates interpretation, and confirmatory markers such as TSAT should be obtained before decisions are made. By contrast, ferritin values ≥ 59 ng/mL (green zone) fall above the functional reference limit (FRL) where erythropoiesis is no longer iron-limited; this zone therefore represents a clinically reassuring status of iron sufficiency. Taken together, these physiologically grounded zones provide a more nuanced and actionable framework than traditional cutoffs and directly inform a modern strategy for early-pregnancy iron screening. Guideline recommendations for iron screening in pregnancy remain inconsistent. Current US Preventive Services Task Force [ 18 ] concluded that there was insufficient evidence for universal screening for iron deficiency in non-anemic pregnant adults. By contrast, the International Federation of Gynecology and Obstetrics recommends routine screening for iron deficiency in pregnancy using ferritin and/or TSAT whenever inflammation is suspected, regardless of anemia status. Recommendations from European Hematology Association stated that iron deficiency should be identified as early as possible in the first trimester [ 19 ] but a threshold to determine iron deficiency is not defined. New expert consensus in 2025 also agreed that ferritin level of 50 ng/mL could be that initial screening for iron deficiency in adults,[ 20 ] same with McCarthy’s recommendation of 60 ng/mL.[ 5 ] The finding of an FRL above the conventional cutoff aligns with evidence that current diagnostic thresholds may underestimate iron deficiency in pregnancy, suggested by many guidelines.[ 21 – 23 ] Randomized studies in non-anemic, low-ferritin patients (ferritin < 50 ng/mL) have demonstrated improved hematologic indices and reduced fatigue following oral or intravenous iron therapy[ 24 ] and pregnant individuals with ferritin higher than 70 ng/mL do not develop iron deficiency or iron deficiency anemia later on.[ 20 ] Thus, while the conventional 30 ng/mL cutoff prioritizes sensitivity to detect iron deficiency anemia, our functionally derived limit may better reflect iron adequacy for optimal pregnancy outcomes. Therefore, the proposed approach aligns with latest guidelines to prevent third-trimester iron deficiency or iron deficiency anemia. Several limitations should be acknowledged when interpreting our findings. Although the physiologic modeling using restricted cubic splines provides a biologically informed framework for defining ferritin thresholds, the analysis remains observational and cannot establish causality. Our cohort reflects a specific population of healthy, non-anemic pregnant Asian women in early gestation, and the derived thresholds—particularly the functional reference limit (FRL)—may not be directly generalizable to populations with different dietary patterns, genetic backgrounds, inflammatory burdens. Furthermore, ferritin is an acute-phase reactant, and although individuals with infection, subclinical inflammation cannot be fully ruled out. While TSAT was used as an adjunct measure, other iron biomarkers such as soluble transferrin receptor, reticulocyte hemoglobin content, or hepcidin were not included. These markers could have provided additional mechanistic validation of the physiological zones and strengthened the interpretation of iron status under pregnancy-related inflammation. Finally, the study did not include longitudinal follow-up to determine whether individuals classified in each ferritin zone subsequently developed iron deficiency or anemia later in pregnancy. However, the strong concordance between our physiologic ferritin thresholds and TSAT confirms that the proposed zone-based approach functions as a reliable rule-out and classification method. In conclusion, establishing a ferritin FRL of 59 ng/mL enhances specificity in ruling out iron deficiency, reclassifies nearly 23% of first-trimester patients as iron-deficient—four times the prevalence detected by traditional criteria—and aligned more closely with transferrin saturation (TSAT < 16%) than with legacy ferritin thresholds. Equally important, ferritin screening can also help identify iron overload, a condition increasingly recognized for its association with adverse maternal outcomes such as gestational diabetes mellitus [ 25 ] and potential oxidative stress–related effects on fetal growth and development.[ 26 ] Integrating ferritin measurement into routine first-trimester antenatal screening may therefore serve a dual purpose in preventing both deficiency and excess iron. Future studies should validate these threshold strategies across diverse obstetric populations and evaluate their impact on maternal metabolic health and neonatal outcomes. Declarations Funding: The research was funded by Hung Vuong Hospital, with the support provided under research number CS/HV/24/42. Additional Information: We have no conflicts of interest to disclose . Disclosure of ethical statements The protocol for this research project has been approved by a suitably constituted Ethics Committee of the institution, Ethical Committee of Hung Vuong Hospital, and it conforms to the provisions of the Declaration of Helsinki. Informed consent was obtained from all participants. Author Contribution Huan Nguyen Pham: Conceptualization, Methodology, Formal analysis, Writing – original draft; Vy Thi Thao Nguyen: Data curation, Validation, Visualization, Writing – original draft; Phuc Nguyen Huu Pham: Data curation, Formal analysis; Nghiem Xuan Huynh: Investigation, Resources, Supervision; Hang Thi Phan: Funding acquisition, Project administration, Supervision, Writing – review & editing; Dung Ngoc Yen Dang: Investigation, Data curation, Validation; Vinh Thanh Tran: Supervision, Writing – review & editing; Nien Vinh Lam: Project administration, Supervision. Data Availability The data that support the findings of this study are available from the corresponding author (Huan Nguyen Pham; email: [email protected] ) upon reasonable request. References Balendran, S. and C. Forsyth, Non-anaemic iron deficiency. Aust Prescr, 2021. 44 (6): p. 193–196. Al-Naseem, A., et al., Iron deficiency without anaemia: a diagnosis that matters. Clin Med (Lond), 2021. 21 (2): p. 107–113. Truong, J., et al., The origin of ferritin reference intervals: a systematic review. Lancet Haematol, 2024. 11 (7): p. e530–e539. Auerbach, M., et al., Prevalence of iron deficiency in first trimester, nonanemic pregnant women. J Matern Fetal Neonatal Med, 2021. 34 (6): p. 1002–1005. McCarthy, E.K., et al., Longitudinal evaluation of iron status during pregnancy: a prospective cohort study in a high-resource setting. Am J Clin Nutr, 2024. 120 (5): p. 1259–1268. Sezgin, G., T.P. Loh, and C. Markus, Functional reference limits: a case study of serum ferritin. Journal of Laboratory Medicine, 2021. 45 (2): p. 69–77. Addo, O.Y., et al., Physiologically based serum ferritin thresholds for iron deficiency among women and children from Africa, Asia, Europe, and central America: a multinational comparative study. Lancet Glob Health, 2025. 13 (5): p. e831–e842. Moyle, K.A., A practical review of iron deficiency in pregnancy. Semin Fetal Neonatal Med, 2025: p. 101611. CLSI. Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory; Approved Guideline—Third Edition. Wayne, PA: Clinical and Laboratory Standards Institute, 2010. CLSI document EP28-A3c . in WHO guideline on use of ferritin concentrations to assess iron status in individuals and populations . 2020: Geneva. Gauthier, J., Q.V. Wu, and T.A. Gooley, Cubic splines to model relationships between continuous variables and outcomes: a guide for clinicians. Bone Marrow Transplant, 2020. 55 (4): p. 675–680. Centre, J.R., Handbook on constructing composite indicators: methodology and user guide . 2008: OECD publishing. Tran, T.N., et al., A Cross-Sectional Study of Serum Ferritin Levels in Vietnamese Adults with Metabolic Syndrome. Diabetes Metab Syndr Obes, 2022. 15 : p. 1517–1523. Mei, Z., et al., Physiologically based serum ferritin thresholds for iron deficiency in children and non-pregnant women: a US National Health and Nutrition Examination Surveys (NHANES) serial cross-sectional study. Lancet Haematol, 2021. 8 (8): p. e572–e582. Mei, Z., et al., Physiologically based trimester-specific serum ferritin thresholds for iron deficiency in US pregnant women. Blood Adv, 2024. 8 (14): p. 3745–3753. Hallberg, L., et al., Screening for iron deficiency: an analysis based on bone-marrow examinations and serum ferritin determinations in a population sample of women. Br J Haematol, 1993. 85 (4): p. 787–98. Mor, G., et al., Inflammation and pregnancy: the role of the immune system at the implantation site. Ann N Y Acad Sci, 2011. 1221 (1): p. 80–7. Force, U.S.P.S.T., et al., Screening and Supplementation for Iron Deficiency and Iron Deficiency Anemia During Pregnancy: US Preventive Services Task Force Recommendation Statement. JAMA, 2024. 332 (11): p. 906–913. Iolascon, A., et al., Recommendations for diagnosis, treatment, and prevention of iron deficiency and iron deficiency anemia. Hemasphere, 2024. 8 (7): p. e108. Benson, A.E., et al., Management of iron deficiency in children, adults, and pregnant individuals: evidence-based and expert consensus recommendations. Lancet Haematol, 2025. 12 (5): p. e376–e388. Daru, J., et al., Serum ferritin thresholds for the diagnosis of iron deficiency in pregnancy: a systematic review. Transfus Med, 2017. 27 (3): p. 167–174. Pavord, S., et al., UK guidelines on the management of iron deficiency in pregnancy. Br J Haematol, 2020. 188 (6): p. 819–830. Troike, K.M. and A.J. McShane, Re-evaluating ferritin thresholds to diagnose iron deficiency. Clin Biochem, 2025. 140 : p. 111020. Vaucher, P., et al., Effect of iron supplementation on fatigue in nonanemic menstruating women with low ferritin: a randomized controlled trial. CMAJ, 2012. 184 (11): p. 1247–54. Xie, Y., et al., Serum ferritin levels and risk of gestational diabetes mellitus: A cohort study. Sci Rep, 2025. 15 (1): p. 7525. Sammallahti, S., et al., Maternal early-pregnancy ferritin and offspring neurodevelopment: A prospective cohort study from gestation to school age. Paediatr Perinat Epidemiol, 2022. 36 (3): p. 425–434. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8652241","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":587245609,"identity":"bed7455a-347b-449b-9b2b-394bad29523d","order_by":0,"name":"Huan Nguyen Pham","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBACAwjFLAciDzwgRYsxWEsCKVoSG0AUUVrMJZKfPS7MsU6fH3b4IdAWOzndBgJaLGekmRvP3Jaeu/F2mgFQS7Kx2QFCDruRYCbNu+1w7sbZCSAtBxK3EdaS/g2kJd1wdvoHYrXkgG1JkJfOIdaWM2/KpIF+MdwgnVNwIMGAGL8cT98mXbjNWl5+dvrmDx8q7OQIagEBZrBesEoDIpTDtcg3EKl6FIyCUTAKRh4AAEkLRc7AYJ5RAAAAAElFTkSuQmCC","orcid":"","institution":"Hung Vuong Hospital","correspondingAuthor":true,"prefix":"","firstName":"Huan","middleName":"Nguyen","lastName":"Pham","suffix":""},{"id":587245610,"identity":"0bc66950-888b-4815-89e8-996e25be7c96","order_by":1,"name":"Vy Thi Thao Nguyen","email":"","orcid":"","institution":"Ho Chi Minh City Medicine and Pharmacy University","correspondingAuthor":false,"prefix":"","firstName":"Vy","middleName":"Thi Thao","lastName":"Nguyen","suffix":""},{"id":587245611,"identity":"6f933277-1592-44f3-8e5b-35d45b03a200","order_by":2,"name":"Phuc Nguyen Huu 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Yen","lastName":"Dang","suffix":""},{"id":587245619,"identity":"6de905f2-06d8-44fb-9e65-d7ec760b1bdf","order_by":6,"name":"Vinh Thanh Tran","email":"","orcid":"","institution":"Ho Chi Minh City Medicine and Pharmacy University","correspondingAuthor":false,"prefix":"","firstName":"Vinh","middleName":"Thanh","lastName":"Tran","suffix":""},{"id":587245620,"identity":"0971ddcc-f243-4c9e-b2fc-54305b339f51","order_by":7,"name":"Nien Vinh Lam","email":"","orcid":"","institution":"Ho Chi Minh City Medicine and Pharmacy University","correspondingAuthor":false,"prefix":"","firstName":"Nien","middleName":"Vinh","lastName":"Lam","suffix":""}],"badges":[],"createdAt":"2026-01-20 18:52:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8652241/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8652241/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102745543,"identity":"3e9a67f2-26c5-4064-8977-329358c6abe3","added_by":"auto","created_at":"2026-02-16 08:51:35","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":119608,"visible":true,"origin":"","legend":"\u003cp\u003eFlow of participant recruitment and exclusion\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8652241/v1/465701d7c5962587b04b1fca.jpeg"},{"id":102745915,"identity":"7f816331-90e5-4bcc-b06f-75707b8ad2d0","added_by":"auto","created_at":"2026-02-16 08:54:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":233163,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional reference limits of MCV, MCH, HGB, Composite\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8652241/v1/18d2cb8bad7bc4f2efac45ce.png"},{"id":102745820,"identity":"98b2888c-a116-4c5b-be41-1f57a647632b","added_by":"auto","created_at":"2026-02-16 08:54:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50146,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of Iron overload and Non-Anemic Iron Deficiency using different cutoffs\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8652241/v1/7e96d037943a03bb6aba1f2b.png"},{"id":102745742,"identity":"3f292572-63f6-4c61-97a0-cbb8ba0e0469","added_by":"auto","created_at":"2026-02-16 08:53:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":28091,"visible":true,"origin":"","legend":"\u003cp\u003ePhysiologically based ferritin thresholds for iron status\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8652241/v1/589371835d2f0f25a55d7a69.png"},{"id":102751677,"identity":"ff5cb556-6097-465f-962e-43b89994bec6","added_by":"auto","created_at":"2026-02-16 09:27:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":993108,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8652241/v1/e86729fc-6931-48cf-a267-3b08c285c4b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physiologically Based Ferritin Thresholds to Redefine Early Pregnancy Iron Screening: A Cross-Sectional Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIron requirements increase markedly during pregnancy to support fetal development and maternal erythropoiesis. While iron deficiency anemia is routinely screened, non-anemic iron deficiency (NAID) remains under-recognized despite its association with maternal fatigue, preterm birth, and impaired neurodevelopment in offspring.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] The traditional ferritin cutoff of 30 ng/mL prioritizes detection of iron deficiency anemia but may fail to capture functional iron deficiency[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], estimated to range from 14% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] to 20.7%,[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] that precedes anemia. Recent studies propose using functional reference limits (FRLs) derived from physiological relationships between ferritin and erythrocyte indices rather than fixed, pathophysiological thresholds.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Given that there are needs of evidence to support screening for iron deficiency without anemia,[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] we therefore aimed to define physiologically based ferritin thresholds in early pregnancy using restricted cubic spline modelling of ferritin against erythrocyte indices, and to assess the application of FRLs in screening of iron deficiency versus traditional reference intervals.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis study was conducted in Outpatient clinic, Hung Vong Hospital, Vietnam. Data was collected from March to August 2025. The study was approved by Ethical Committee of Hung Vuong Hospital, Ho Chi Minh City, Vietnam with registration CS/HV/24/42. This was an observational cross-sectional study conducted during routine first-trimester antenatal care and no intervention or randomization was performed. The study was registered on ClinicalTrials.gov as an observational study (NCT06990373).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eParticipants were eligible if they had a singleton pregnancy in the first trimester (\u0026le;\u0026thinsp;13 weeks 6 days), systolic blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;140 mmHg and diastolic pressure\u0026thinsp;\u0026lt;\u0026thinsp;90 mmHg, and a body temperature between 35.0\u0026deg;C and 37.5\u0026deg;C at the time of examination. Exclusion criteria included history of diabetes, hypertension, hemoglobinopathies, leukemia, other non-hematologic cancers, systemic lupus erythematosus, or gastrointestinal disorders affecting iron absorption (e.g., celiac disease, Crohn\u0026rsquo;s, ulcerative colitis, prior gastric surgery); current use of acid-suppressing drugs; using moderate to high dose of elemental iron (\u0026gt;\u0026thinsp;30 mg) supplements; active smoking. Post-enrolment exclusion criteria are hemoglobin concentration\u0026thinsp;\u0026ge;\u0026thinsp;11 g/dL, positive tests for Treponema pallidum, hepatitis B surface antigen, human immunodeficiency virus (HIV), or hepatitis C virus (HCV); and mean corpuscular volume (MCV)\u0026thinsp;\u0026lt;\u0026thinsp;80 fL or mean corpuscular hemoglobin (MCH)\u0026thinsp;\u0026lt;\u0026thinsp;27 pg (except for those with normal electrophoresis). All participants provided informed consent.\u003c/p\u003e\n\u003ch3\u003eProcedures\u003c/h3\u003e\n\u003cp\u003eBetween March and September 2025, data were prospectively collected at Hung Vuong Hospital (Ho Chi Minh City, Vietnam) during routine first-trimester antenatal visits (\u0026le;\u0026thinsp;13 weeks 6 days). Trained staff conducted interviews, physical exams, and drew venous blood following standard protocols. Demographic and clinical data-including age, gestational age, parity, BMI, blood pressure, and temperature-were recorded via patient interviews and electronic records. Laboratory measurements included hemoglobin (HGB), serum iron, ferritin, unsaturated iron-binding protein capacity (UIBC), total iron-binding capacity (TIBC) calculated from UIBC and serum iron, and calculated transferrin saturation (TSAT). Hemoglobin was measured using DxH 900 automated hematology analyzer, while serum iron, TIBC and ferritin were analyzed using standardized colorimetric, measured on Cobas c502, and chemiluminescent immunoassays, measured on Cobas e801 (Roche Diagnostics, Rotkreuz, Switzerland), in the hospital\u0026rsquo;s central laboratory. TSAT was derived as the ratio of serum iron to TIBC multiplied by 100. The primary outcome was the determination of functional reference limit and reference intervals (2.5th to 97.5th percentiles) of ferritin. The secondary outcome was the prevalence of non-anemic iron deficiency (NAID), defined as Hb\u0026thinsp;\u0026ge;\u0026thinsp;11 g/dL in conjunction with traditional cutoff such as ferritin\u0026thinsp;\u0026lt;\u0026thinsp;30 ng/mL or TSAT\u0026thinsp;\u0026lt;\u0026thinsp;20% and newly established reference limits. To minimize potential biases, patients with iron overload, defined as ferritin level\u0026thinsp;\u0026gt;\u0026thinsp;200 ng/mL, were excluded from the study.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis was performed using Jupyter Notebook with pandas, statsmodels, scipy, and matplotlib libraries. Continuous variables were summarized as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, and categorical variables as counts and percentages. Reference intervals (2.5th \u0026minus;\u0026thinsp;97.5th percentiles) for ferritin were estimated using the non-parametric percentile method following CLSI EP28-A3c guidelines.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Outliers (ferritin\u0026thinsp;\u0026lt;\u0026thinsp;15 ng/mL or \u0026gt;\u0026thinsp;200 ng/mL; TSAT\u0026thinsp;\u0026lt;\u0026thinsp;16% or \u0026gt;\u0026thinsp;45%) were excluded. Functional reference limits (FRLs) were determined by modelling the relationship between serum ferritin and erythrocyte indices (MCV, MCH, and HGB) via restricted cubic spline regression, excluding individuals with ferritin\u0026thinsp;\u0026gt;\u0026thinsp;200 ng/mL to prevent skew from iron overload.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] The FRL was identified as the lowest ferritin value at which the 95% bootstrap confidence interval of the spline\u0026rsquo;s first derivative encompassed zero across a continuous range, signifying that further increases in ferritin no longer produced measurable changes in erythrocyte indices. Each model was resampled 500 times using non-parametric bootstrapping to obtain robust estimates and 95% confidence intervals.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOf 596 enrolled participants, 43 with anemia were excluded, leaving 553 for screening. Among these 553, we excluded 25 with infection (HIV, HBV, HCV, or \u003cem\u003eTreponema pallidum\u003c/em\u003e), 6 with hypertension, 15 with BMI\u0026thinsp;\u0026ge;\u0026thinsp;30, 6 with hemoglobinopathies on electrophoresis, and 40 with microcytosis/hypochromia (MCV\u0026thinsp;\u0026lt;\u0026thinsp;80 fL or MCH\u0026thinsp;\u0026lt;\u0026thinsp;27 pg) without electrophoresis results. After selecting qualified patients, 9 samples failed quality control, yielding a final analytic cohort of 452 patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe characteristics of patients are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Overall, a total of 452 first-trimester pregnant patients were enrolled. The mean maternal age was 29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5 years, and the mean gestational age at enrolment was 11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 weeks. Participants had an average weight of 54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5 kg and height of 156.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4 cm. The mean systolic and diastolic blood pressures were 113.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0 mmHg and 71.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 mmHg, respectively. Approximately half of the cohort were nulliparous (49.1%), while 50.9% were multigravida. These characteristics reflect a generally healthy and homogeneous group of patients representing early normal pregnancy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.16\u0026thinsp;\u0026plusmn;\u0026thinsp;7.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156.72\u0026thinsp;\u0026plusmn;\u0026thinsp;5.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113.94\u0026thinsp;\u0026plusmn;\u0026thinsp;9.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.00\u0026thinsp;\u0026plusmn;\u0026thinsp;7.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCounts (Percentage)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimigravida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222 (49.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultigravida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230 (50.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFunctional reference limits (FRLs) for ferritin were determined using restricted cubic spline (RCS) models relating ferritin to MCV, MCH, and HGB. Five knots were placed at the 5th, 25th, 50th, 75th, and 95th percentiles of ferritin, following recommended practice for flexible non-linear modelling.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] To improve robustness, MCV, MCH, and HGB were standardized (z-scores) and averaged into a composite index, following established recommendations for constructing composite indicators.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] We then estimated the derivative (slope with respect to ferritin) on a fine grid of ferritin values. To account for sampling variability, we performed non-parametric bootstrapping (500 iterations) for internal validation, refitted the spline model in each bootstrap sample, and recomputed the derivative curve. At each ferritin grid point, the 2.5th and 97.5th percentiles of the bootstrapped derivatives defined the 95% confidence interval (CI). The FRL was defined as the lowest ferritin concentration at which this 95% CI of the derivative encompassed zero for a sustained run of points, indicating a plateau of response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll four models converged on a ferritin inflection point near 55\u0026ndash;60 ng/mL, marking the plateau of erythrocyte indices that reflects physiologic iron adequacy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For MCV, the functional reference limit (FRL) was estimated at 55.3 ng/mL (95% CI, 41.9\u0026ndash;81.0), with the response curve flattening between approximately 40 and 80 ng/mL. The MCH analysis produced a similar FRL of 56.2 ng/mL (95% CI, 46.9\u0026ndash;123.6), although the plateau extended over a broader ferritin range, consistent with greater variability at higher concentrations. The HGB analysis produced a FRL of 59.1 with tighter confidence interval (95%CI 56.0\u0026ndash;88.9). When MCV, MCH and HGB were combined into a standardized composite z-score, the resulting FRL was 58.6 ng/mL (95% CI, 56.2\u0026ndash;96.4), nearly identical to the single-marker estimates but with narrower confidence limits than those observed for HGB alone. For comparison, the reference interval derived from the study indicated a lower limit of 26.1 ng/mL (90% CI, 19.1\u0026ndash;33.1) and an upper limit of 187.1 ng/mL (90% CI, 180.2\u0026ndash;194.1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo validate the use of FRL, we tabulated the FRL with TSAT of 16% as the reference definition. Application of the FRL derived from MCV, MCH, HGB and the composite index (MCV, MCH, and HGB) against TSAT yielded nearly identical classification performance (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The FRLs were consistent across HGB and composite at around 59 ng/mL, with each identifying 272 true negatives and only 12 false negatives but also misclassifying approximately 85 false positives. The negative predictive value (NPV) was very high (95.8%), indicating that individuals above the FRL threshold were very unlikely to be iron-deficient. Clinically, a ferritin FRL around 59 ng/mL offers strong reassurance for ruling out iron deficiency (high NPV).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance of FRLs in predicting iron deficiency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRL\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrue Positive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFalse Positive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTrue Negative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFalse Negative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSens%\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSpecs%\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV\u0026thinsp;~\u0026thinsp;Ferritin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.3\u003c/p\u003e \u003cp\u003e(41.9\u0026ndash;81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53.1%\u003c/p\u003e \u003cp\u003e(36.4\u0026ndash;69.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e79.3%\u003c/p\u003e \u003cp\u003e(74.8\u0026ndash;83.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.7%\u003c/p\u003e \u003cp\u003e(12.0\u0026ndash;27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95.0%\u003c/p\u003e \u003cp\u003e(91.9\u0026ndash;96.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCH\u0026thinsp;~\u0026thinsp;Ferritin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.2\u003c/p\u003e \u003cp\u003e(46.9\u0026ndash;123.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53.1%\u003c/p\u003e \u003cp\u003e(36.4\u0026ndash;69.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e79.0%\u003c/p\u003e \u003cp\u003e(74.5\u0026ndash;82.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.5%\u003c/p\u003e \u003cp\u003e(11.9\u0026ndash;27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e94.9%\u003c/p\u003e \u003cp\u003e(91.8\u0026ndash;96.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB\u0026thinsp;~\u0026thinsp;Ferritin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.1\u003c/p\u003e \u003cp\u003e(56.0\u0026ndash;89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62.5% (45.3\u0026ndash;77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e76.2%\u003c/p\u003e \u003cp\u003e(71.5\u0026ndash;80.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.0%\u003c/p\u003e \u003cp\u003e(12.7\u0026ndash;27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95.8%\u003c/p\u003e \u003cp\u003e(92.8\u0026ndash;97.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComposite (MCV\u0026thinsp;+\u0026thinsp;MCH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.6\u003c/p\u003e \u003cp\u003e(56.2\u0026ndash;96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62.5% (45.3\u0026ndash;77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e76.2%\u003c/p\u003e \u003cp\u003e(71.5\u0026ndash;80.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.0%\u003c/p\u003e \u003cp\u003e(12.7\u0026ndash;27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95.8%\u003c/p\u003e \u003cp\u003e(92.8\u0026ndash;97.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe prevalence of non-anemic iron deficiency (NAID) varied depending on the applied cutoff criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Using the FRL for ferritin derived from above (59 ng/mL), the prevalence of NAID was 23.2%. Conventional thresholds yielded substantially lower estimates: ferritin\u0026thinsp;\u0026lt;\u0026thinsp;30 ng/mL identified only 5.1%, and ferritin\u0026thinsp;\u0026lt;\u0026thinsp;15 ng/mL just 1.5% of the study population. When using transferrin saturation (TSAT) as a cutoff, the prevalence was 7.3% at the \u0026lt;\u0026thinsp;16% cutoff and 15.5% at the \u0026lt;\u0026thinsp;20% cutoff. Combining ferritin\u0026thinsp;\u0026lt;\u0026thinsp;100 ng/mL with TSAT\u0026thinsp;\u0026lt;\u0026thinsp;16% increased prevalence to 6.2%, while combining ferritin\u0026thinsp;\u0026lt;\u0026thinsp;100 ng/mL with TSAT\u0026thinsp;\u0026lt;\u0026thinsp;20% increased prevalence to 10.2%. Overall, the FRL cutoff identified a larger proportion of pregnant patients with iron deficiency compared to traditional ferritin or TSAT thresholds while iron overload also accounts for a substantial proportion of the population (13.9%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing restricted cubic-spline modeling of hematologic indices (MCV, MCH, and hemoglobin), we identified physiologically distinct ferritin zones that reflect the transition from iron-deficient to iron-sufficient erythropoiesis. Three data-driven thresholds emerged (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e): the established absolute iron-deficiency cutoff at \u0026lt;\u0026thinsp;15 ng/mL, the lower reference interval (RI) limit at 26 ng/mL, and the functional reference limit (FRL) at 59 ng/mL, representing the point beyond which ensure iron sufficiency throughout pregnancy. These thresholds delineate four meaningful physiologic categories: absolute iron deficiency (\u0026lt;\u0026thinsp;15 ng/mL), iron deficiency (15\u0026ndash;26 ng/mL), possible iron deficiency (26\u0026ndash;59 ng/mL), where TSAT confirmation is recommended, and iron sufficiency (\u0026ge;\u0026thinsp;59 ng/mL).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study defines the first physiologically based ferritin functional reference limit for non-anemic pregnant women, achieved by removing contributors that spuriously increase ferritin concentrations, including infection and obesity.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Using red cell indices as biological correlates, we identified a consistent threshold around 55\u0026ndash;60 ng/mL at which erythrocyte indices plateau, marking adequate iron supply for erythropoiesis. These values are substantially higher than the traditional ferritin cutoff of 30 ng/mL used in clinical practice or other physiologically based ferritin threshold in studies by Addo OY et al.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] or Z.Mei et al.,[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] probably because our study excluded anemic patients and those with hemoglobinopathy states. The findings support a re-evaluation of ferritin thresholds for screening, and a threshold to determine iron deficiency in non-anemic patient.\u003c/p\u003e \u003cp\u003eOur derived lower limit of the ferritin reference interval (~\u0026thinsp;26 ng/mL; 2.5th percentile) closely aligns with physiologically based thresholds recently other studies. For example, in a US cohort of pregnant patients, the functional inflection-point ferritin threshold in the first trimester was identified as 25.8 \u0026micro;g/L (95% CI 18.1\u0026ndash;28.5) using restricted cubic-spline modelling of ferritin versus hemoglobin and soluble transferrin receptor.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eLikewise, in apparently healthy non-pregnant patients of reproductive age, the physiologically based threshold was estimated at \u0026lt;\u0026thinsp;24.8 \u0026micro;g/L (95% CI 24.4\u0026ndash;25.2).[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] The closeness of our lower reference limit (~\u0026thinsp;26 ng/mL) to these physiologic cut-points strengthens the plausibility that our interval is capturing the threshold of iron deficiency in early pregnancy. In other words, patients with ferritin values below 26 ng/mL may already be entering the zone of iron-deficient erythropoiesis or sub-optimal iron status, even in the absence of anemia. Our data thus support the notion that the conventionally accepted ferritin cut‐off (e.g., 30 ng/mL) may underestimate the onset of functional deficiency, and that a threshold in the mid-20s ng/mL aligns with both population‐based percentiles and physiological modelling of iron supply versus demand.\u003c/p\u003e \u003cp\u003eTo further consolidate the threshold, we validated our FRL against TSAT. We employed a cutoff of 16%, which has a sensitivity of 20% and a specificity of 93% [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] for identifying iron deficiency. Indeed, when tested against TSAT\u0026thinsp;\u0026lt;\u0026thinsp;16%, the FRL demonstrated good specificity (76.2%) and an extremely high negative predictive value (95.8%), supporting its clinical utility as a rule-out boundary for iron deficiency. Therefore, we propose a two-step algorithm: (1) screen with ferritin; (2) for values 26\u0026ndash;59 ng/mL, an additional step of measuring TSAT is needed. This strategy balances sensitivity and specificity and mitigates ferritin\u0026rsquo;s acute-phase confounding.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFrom a clinical standpoint, our findings support a decisive shift away from long-standing ferritin cutoffs toward thresholds that are functionally and physiologically validated. Importantly, clinical decision limits must be distinguished from statistical reference limits. Ferritin values below 15 ng/mL\u0026mdash;the red zone in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026mdash;correspond to absolute iron deficiency, a state well documented in bone-marrow studies, and warrant prompt therapeutic replacement.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Between 15 ng/mL and the lower reference limit identified in our study (~\u0026thinsp;26 ng/mL; orange zone), patients likely exhibit early-stage or functional iron deficiency and should receive moderate to high iron dose supplementation to prevent progression. The intermediate yellow zone (26\u0026ndash;59 ng/mL) represents an area of physiological uncertainty: inflammation-associated ferritin elevation in pregnancy complicates interpretation, and confirmatory markers such as TSAT should be obtained before decisions are made. By contrast, ferritin values\u0026thinsp;\u0026ge;\u0026thinsp;59 ng/mL (green zone) fall above the functional reference limit (FRL) where erythropoiesis is no longer iron-limited; this zone therefore represents a clinically reassuring status of iron sufficiency. Taken together, these physiologically grounded zones provide a more nuanced and actionable framework than traditional cutoffs and directly inform a modern strategy for early-pregnancy iron screening.\u003c/p\u003e \u003cp\u003e Guideline recommendations for iron screening in pregnancy remain inconsistent. Current US Preventive Services Task Force [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] concluded that there was insufficient evidence for universal screening for iron deficiency in non-anemic pregnant adults. By contrast, the International Federation of Gynecology and Obstetrics recommends routine screening for iron deficiency in pregnancy using ferritin and/or TSAT whenever inflammation is suspected, regardless of anemia status. Recommendations from European Hematology Association stated that iron deficiency should be identified as early as possible in the first trimester [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] but a threshold to determine iron deficiency is not defined. New expert consensus in 2025 also agreed that ferritin level of 50 ng/mL could be that initial screening for iron deficiency in adults,[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] same with McCarthy\u0026rsquo;s recommendation of 60 ng/mL.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] The finding of an FRL above the conventional cutoff aligns with evidence that current diagnostic thresholds may underestimate iron deficiency in pregnancy, suggested by many guidelines.[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] Randomized studies in non-anemic, low-ferritin patients (ferritin\u0026thinsp;\u0026lt;\u0026thinsp;50 ng/mL) have demonstrated improved hematologic indices and reduced fatigue following oral or intravenous iron therapy[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and pregnant individuals with ferritin higher than 70 ng/mL do not develop iron deficiency or iron deficiency anemia later on.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Thus, while the conventional 30 ng/mL cutoff prioritizes sensitivity to detect iron deficiency anemia, our functionally derived limit may better reflect iron adequacy for optimal pregnancy outcomes. Therefore, the proposed approach aligns with latest guidelines to prevent third-trimester iron deficiency or iron deficiency anemia.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged when interpreting our findings. Although the physiologic modeling using restricted cubic splines provides a biologically informed framework for defining ferritin thresholds, the analysis remains observational and cannot establish causality. Our cohort reflects a specific population of healthy, non-anemic pregnant Asian women in early gestation, and the derived thresholds\u0026mdash;particularly the functional reference limit (FRL)\u0026mdash;may not be directly generalizable to populations with different dietary patterns, genetic backgrounds, inflammatory burdens. Furthermore, ferritin is an acute-phase reactant, and although individuals with infection, subclinical inflammation cannot be fully ruled out. While TSAT was used as an adjunct measure, other iron biomarkers such as soluble transferrin receptor, reticulocyte hemoglobin content, or hepcidin were not included. These markers could have provided additional mechanistic validation of the physiological zones and strengthened the interpretation of iron status under pregnancy-related inflammation. Finally, the study did not include longitudinal follow-up to determine whether individuals classified in each ferritin zone subsequently developed iron deficiency or anemia later in pregnancy. However, the strong concordance between our physiologic ferritin thresholds and TSAT confirms that the proposed zone-based approach functions as a reliable rule-out and classification method.\u003c/p\u003e \u003cp\u003eIn conclusion, establishing a ferritin FRL of 59 ng/mL enhances specificity in ruling out iron deficiency, reclassifies nearly 23% of first-trimester patients as iron-deficient\u0026mdash;four times the prevalence detected by traditional criteria\u0026mdash;and aligned more closely with transferrin saturation (TSAT\u0026thinsp;\u0026lt;\u0026thinsp;16%) than with legacy ferritin thresholds. Equally important, ferritin screening can also help identify iron overload, a condition increasingly recognized for its association with adverse maternal outcomes such as gestational diabetes mellitus [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and potential oxidative stress\u0026ndash;related effects on fetal growth and development.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Integrating ferritin measurement into routine first-trimester antenatal screening may therefore serve a dual purpose in preventing both deficiency and excess iron. Future studies should validate these threshold strategies across diverse obstetric populations and evaluate their impact on maternal metabolic health and neonatal outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The research was funded by Hung Vuong Hospital, with the support provided under research number CS/HV/24/42.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information:\u0026nbsp;\u003c/strong\u003eWe have no conflicts of interest to disclose\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of ethical statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol for this research project has been approved by a suitably constituted Ethics Committee of the institution, Ethical Committee of Hung Vuong Hospital, and it conforms to the provisions of the Declaration of Helsinki. Informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuan Nguyen Pham: Conceptualization, Methodology, Formal analysis, Writing \u0026ndash; original draft; Vy Thi Thao Nguyen: Data curation, Validation, Visualization, Writing \u0026ndash; original draft; Phuc Nguyen Huu Pham: Data curation, Formal analysis; Nghiem Xuan Huynh: Investigation, Resources, Supervision; Hang Thi Phan: Funding acquisition, Project administration, Supervision, Writing \u0026ndash; review \u0026amp;amp; editing; Dung Ngoc Yen Dang: Investigation, Data curation, Validation; Vinh Thanh Tran: Supervision, Writing \u0026ndash; review \u0026amp;amp; editing; Nien Vinh Lam: Project administration, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author (Huan Nguyen Pham; email: [email protected]) upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBalendran, S. and C. Forsyth, \u003cem\u003eNon-anaemic iron deficiency.\u003c/em\u003e Aust Prescr, 2021. \u003cstrong\u003e44\u003c/strong\u003e(6): p. 193\u0026ndash;196.\u003c/li\u003e\n\u003cli\u003eAl-Naseem, A., et al., \u003cem\u003eIron deficiency without anaemia: a diagnosis that matters.\u003c/em\u003e Clin Med (Lond), 2021. \u003cstrong\u003e21\u003c/strong\u003e(2): p. 107\u0026ndash;113.\u003c/li\u003e\n\u003cli\u003eTruong, J., et al., \u003cem\u003eThe origin of ferritin reference intervals: a systematic review.\u003c/em\u003e Lancet Haematol, 2024. \u003cstrong\u003e11\u003c/strong\u003e(7): p. e530\u0026ndash;e539.\u003c/li\u003e\n\u003cli\u003eAuerbach, M., et al., \u003cem\u003ePrevalence of iron deficiency in first trimester, nonanemic pregnant women.\u003c/em\u003e J Matern Fetal Neonatal Med, 2021. \u003cstrong\u003e34\u003c/strong\u003e(6): p. 1002\u0026ndash;1005.\u003c/li\u003e\n\u003cli\u003eMcCarthy, E.K., et al., \u003cem\u003eLongitudinal evaluation of iron status during pregnancy: a prospective cohort study in a high-resource setting.\u003c/em\u003e Am J Clin Nutr, 2024. \u003cstrong\u003e120\u003c/strong\u003e(5): p. 1259\u0026ndash;1268.\u003c/li\u003e\n\u003cli\u003eSezgin, G., T.P. Loh, and C. Markus, \u003cem\u003eFunctional reference limits: a case study of serum ferritin.\u003c/em\u003e Journal of Laboratory Medicine, 2021. \u003cstrong\u003e45\u003c/strong\u003e(2): p. 69\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eAddo, O.Y., et al., \u003cem\u003ePhysiologically based serum ferritin thresholds for iron deficiency among women and children from Africa, Asia, Europe, and central America: a multinational comparative study.\u003c/em\u003e Lancet Glob Health, 2025. \u003cstrong\u003e13\u003c/strong\u003e(5): p. e831\u0026ndash;e842.\u003c/li\u003e\n\u003cli\u003eMoyle, K.A., \u003cem\u003eA practical review of iron deficiency in pregnancy.\u003c/em\u003e Semin Fetal Neonatal Med, 2025: p. 101611.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eCLSI. Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory; Approved Guideline\u0026mdash;Third Edition.\u003c/em\u003e Wayne, PA: Clinical and Laboratory Standards Institute, 2010. \u003cstrong\u003eCLSI document EP28-A3c\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003ein \u003cem\u003eWHO guideline on use of ferritin concentrations to assess iron status in individuals and populations\u003c/em\u003e. 2020: Geneva.\u003c/li\u003e\n\u003cli\u003eGauthier, J., Q.V. Wu, and T.A. Gooley, \u003cem\u003eCubic splines to model relationships between continuous variables and outcomes: a guide for clinicians.\u003c/em\u003e Bone Marrow Transplant, 2020. \u003cstrong\u003e55\u003c/strong\u003e(4): p. 675\u0026ndash;680.\u003c/li\u003e\n\u003cli\u003eCentre, J.R., \u003cem\u003eHandbook on constructing composite indicators: methodology and user guide\u003c/em\u003e. 2008: OECD publishing.\u003c/li\u003e\n\u003cli\u003eTran, T.N., et al., \u003cem\u003eA Cross-Sectional Study of Serum Ferritin Levels in Vietnamese Adults with Metabolic Syndrome.\u003c/em\u003e Diabetes Metab Syndr Obes, 2022. \u003cstrong\u003e15\u003c/strong\u003e: p. 1517\u0026ndash;1523.\u003c/li\u003e\n\u003cli\u003eMei, Z., et al., \u003cem\u003ePhysiologically based serum ferritin thresholds for iron deficiency in children and non-pregnant women: a US National Health and Nutrition Examination Surveys (NHANES) serial cross-sectional study.\u003c/em\u003e Lancet Haematol, 2021. \u003cstrong\u003e8\u003c/strong\u003e(8): p. e572\u0026ndash;e582.\u003c/li\u003e\n\u003cli\u003eMei, Z., et al., \u003cem\u003ePhysiologically based trimester-specific serum ferritin thresholds for iron deficiency in US pregnant women.\u003c/em\u003e Blood Adv, 2024. \u003cstrong\u003e8\u003c/strong\u003e(14): p. 3745\u0026ndash;3753.\u003c/li\u003e\n\u003cli\u003eHallberg, L., et al., \u003cem\u003eScreening for iron deficiency: an analysis based on bone-marrow examinations and serum ferritin determinations in a population sample of women.\u003c/em\u003e Br J Haematol, 1993. \u003cstrong\u003e85\u003c/strong\u003e(4): p. 787\u0026ndash;98.\u003c/li\u003e\n\u003cli\u003eMor, G., et al., \u003cem\u003eInflammation and pregnancy: the role of the immune system at the implantation site.\u003c/em\u003e Ann N Y Acad Sci, 2011. \u003cstrong\u003e1221\u003c/strong\u003e(1): p. 80\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eForce, U.S.P.S.T., et al., \u003cem\u003eScreening and Supplementation for Iron Deficiency and Iron Deficiency Anemia During Pregnancy: US Preventive Services Task Force Recommendation Statement.\u003c/em\u003e JAMA, 2024. \u003cstrong\u003e332\u003c/strong\u003e(11): p. 906\u0026ndash;913.\u003c/li\u003e\n\u003cli\u003eIolascon, A., et al., \u003cem\u003eRecommendations for diagnosis, treatment, and prevention of iron deficiency and iron deficiency anemia.\u003c/em\u003e Hemasphere, 2024. \u003cstrong\u003e8\u003c/strong\u003e(7): p. e108.\u003c/li\u003e\n\u003cli\u003eBenson, A.E., et al., \u003cem\u003eManagement of iron deficiency in children, adults, and pregnant individuals: evidence-based and expert consensus recommendations.\u003c/em\u003e Lancet Haematol, 2025. \u003cstrong\u003e12\u003c/strong\u003e(5): p. e376\u0026ndash;e388.\u003c/li\u003e\n\u003cli\u003eDaru, J., et al., \u003cem\u003eSerum ferritin thresholds for the diagnosis of iron deficiency in pregnancy: a systematic review.\u003c/em\u003e Transfus Med, 2017. \u003cstrong\u003e27\u003c/strong\u003e(3): p. 167\u0026ndash;174.\u003c/li\u003e\n\u003cli\u003ePavord, S., et al., \u003cem\u003eUK guidelines on the management of iron deficiency in pregnancy.\u003c/em\u003e Br J Haematol, 2020. \u003cstrong\u003e188\u003c/strong\u003e(6): p. 819\u0026ndash;830.\u003c/li\u003e\n\u003cli\u003eTroike, K.M. and A.J. McShane, \u003cem\u003eRe-evaluating ferritin thresholds to diagnose iron deficiency.\u003c/em\u003e Clin Biochem, 2025. \u003cstrong\u003e140\u003c/strong\u003e: p. 111020.\u003c/li\u003e\n\u003cli\u003eVaucher, P., et al., \u003cem\u003eEffect of iron supplementation on fatigue in nonanemic menstruating women with low ferritin: a randomized controlled trial.\u003c/em\u003e CMAJ, 2012. \u003cstrong\u003e184\u003c/strong\u003e(11): p. 1247\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eXie, Y., et al., \u003cem\u003eSerum ferritin levels and risk of gestational diabetes mellitus: A cohort study.\u003c/em\u003e Sci Rep, 2025. \u003cstrong\u003e15\u003c/strong\u003e(1): p. 7525.\u003c/li\u003e\n\u003cli\u003eSammallahti, S., et al., \u003cem\u003eMaternal early-pregnancy ferritin and offspring neurodevelopment: A prospective cohort study from gestation to school age.\u003c/em\u003e Paediatr Perinat Epidemiol, 2022. \u003cstrong\u003e36\u003c/strong\u003e(3): p. 425\u0026ndash;434.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8652241/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8652241/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIron deficiency in early pregnancy is common yet frequently missed because current screening strategies rely on ferritin cutoffs designed to detect iron deficiency anemia. We aimed to establish physiologically based ferritin functional reference limits (FRLs) in healthy, non-anemic pregnant women to improve early identification of iron deficiency.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study was conducted at a maternity hospital in Ho Chi Minh City, Vietnam, enrolling first-trimester pregnant women. Participants were included if they had hemoglobin\u0026thinsp;\u0026ge;\u0026thinsp;11 g/dL, no evidence of infection, body-mass index\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m\u0026sup2;, no microcytosis or hypochromia. The primary outcome was the ferritin FRLs, defined using restricted cubic spline modelling of blood indices. Diagnostic performance was assessed against TSAT.\u003c/p\u003e\u003ch2\u003eFindings\u003c/h2\u003e \u003cp\u003e452 patients had complete data for validation. The final suggested ferritin FRL is 59 ng/mL, identified more women with non-anemic iron deficiency compared with traditional cutoffs. Validation against TSAT showed excellent rule-out performance with negative predictive value of 95.8%. A four-zone classification for iron deficiency emerged, including absolute deficiency (\u0026lt;\u0026thinsp;15 ng/mL), deficiency (15\u0026ndash;26 ng/mL), indeterminate status (26\u0026ndash;59 ng/mL), and physiologic sufficiency (\u0026ge;\u0026thinsp;59 ng/mL).\u003c/p\u003e\u003ch2\u003eInterpretation\u003c/h2\u003e \u003cp\u003ePhysiologically derived ferritin thresholds identify early iron deficiency more effectively than conventional cutoffs and provide a grounded framework for screening in first trimester.\u003c/p\u003e","manuscriptTitle":"Physiologically Based Ferritin Thresholds to Redefine Early Pregnancy Iron Screening: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 16:43:28","doi":"10.21203/rs.3.rs-8652241/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-27T10:11:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T20:16:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40058702555804910964863933235353480197","date":"2026-04-03T19:05:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T17:38:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190033036098800566699853537247078488628","date":"2026-02-23T15:29:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-06T12:02:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-06T12:00:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-06T07:40:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-28T03:25:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-28T03:20:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"61c2fabe-1a39-48dc-b68b-60f506644893","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62485091,"name":"Health sciences/Biomarkers"},{"id":62485092,"name":"Health sciences/Diseases"},{"id":62485093,"name":"Health sciences/Health care"},{"id":62485094,"name":"Health sciences/Medical research"},{"id":62485095,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-18T10:54:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 16:43:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8652241","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8652241","identity":"rs-8652241","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}