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A clinical predictive score of high liver iron content in metabolic hyperferritinemia: a retrospective cohort pilot study

BMC Gastroenterology volume 25, Article number: 331 (2025) Cite this article

Abstract

Background

In metabolic hyperferritinemia, most patients do not require bloodletting as the liver iron content is mildly increased. We aimed to develop a clinical predictive score of high liver iron content in metabolic hyperferritinemia to guide the prescription of magnetic resonance imaging of the liver.

Methods

We conducted a single-center retrospective cohort study including consecutive patients with metabolic hyperferritinemia who underwent a liver iron content evaluation at diagnosis. Excessive alcohol consumption was an exclusion criterion. A multivariate analysis followed by a 1000 bootstrap replicate analysis with an expectation–maximization algorithm was used to identify the predictive factors of high liver iron content. A ROC curve analysis was built to study the performance of the score based on the odds-ratio provided by the multivariate analysis.

Results

217 patients (180 men, mean age 57 years old) were included. Fifty-five patients (25%) had high liver iron content (≥ 100 µmol/g). In univariate analysis, a family history of hyperferritinemia requiring phlebotomies was associated with high LIC, as well as an increase of transferrin saturation > 45% (p < 0.001). In multivariate regression, a family history of hyperferritinemia (OR 6.15, CI95 [2.11–17.92]), increased ferritin level ≥ 600 µg/L (OR 5.53, CI95 [1.43–21.42]) and increased transferrin saturation ≥ 45% (OR 2.63, CI95 [1.32–5.23]) were significantly associated with high liver iron content. A 15-point predictive score (area-under-the-curve 0.72, CI95 [0.64–0.79], p < 0.001) was built, providing an OR of 4.17 (CI95 [2.15–8.07], p < 0.001) for high liver iron content (sensitivity 60%, specificity 97%, negative predictive value 84%).

Conclusion

in this pilot study, ferritin ≥ 600 µg/L, transferrin saturation ≥ 45% and a family history of hyperferritinemia requiring bloodletting provided a simple clinical score to predict high liver iron content in metabolic adult hyperferritinemia. The bootstrap analysis confirmed the robustness of our model.

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Introduction

Iron is an essential micronutrient involved in DNA synthesis, and mitochondrial and protein function [1]. Within heme, iron ensures oxygen exchange but these electron transfer properties are responsible for iron excess toxicity: free iron promotes Fenton’s reaction, resulting in the production of oxygen radicals that may induce cellular and DNA damage [2]. Iron is mainly stored in the liver within ferritin, whose serum concentration reflects the patient’s iron stores in patients with hemochromatosis. The estimated prevalence of elevated serum ferritin is 3% in the French general population [3, 4] and rises to 8% in the HEIRS study in the USA [5]. Patients with metabolic features (notably with type 2 diabetes) show high serum ferritin concentrations in up to 47% of patients [4].

Serum ferritin concentration can increase because of enhanced ferritin synthesis during systemic inflammation [6], or linked to alcohol consumption, or because of the increase release from muscle (myolysis), erythrocyte (hemolysis) or liver (hepatic cytolysis) [7]. Excessive prolonged alcohol consumption can induce a real iron overload linked to reduced hepcidin synthesis, increasing dietary iron absorption [8] but the most frequent causes of iron overload are genetic hemochromatosis [9], dysmetabolic iron overload syndrome (DIOS) [10], and chronic liver disease [11]. The International Diabetes Federation diagnosis criteria for metabolic syndrome are shown in supplementary Table 1 [12]. Assessing liver iron concentration helps to stratify the risk of organ damage. Although in hemochromatosis ferritin accurately reflects liver iron stores, there is no evidence that increasing serum ferritin levels accurately reflect increasing body iron stores in secondary iron overload disorders such as DIOS [13, 14, 15]. In DIOS, iron is mainly stored in macrophages and may increase the risk of cirrhosis in patients with steatohepatitis [16].

Atomic absorption photometry on liver biopsy to quantify iron load [17] is now replaced by non-invasive measurement with Magnetic Resonance Imaging (MRI) providing reliable measurements [18, 19] using proton transverse relaxation rates (R2) [20]. Numerous tools have been approved and are now easily available to provide precise liver iron concentration estimations [21]. LIC is increased beyond 36 µmol/g [22, 23]. The recent international consensus classified metabolic hyperferritinemia (MH) into 3 grades according to serum ferritin and hepatic iron stores evaluated as the risk of liver damage increases with iron load [23, 24, 25]. DIOS is the stage 3 of MH, defined by a very high ferritin levels (> 1000 µg/L), and increased LIC > 74 µmol/g. In the vast majority of MH, liver iron concentration is slightly increased [11] and clinical management focuses on lifestyle and dietary behavior to control cardiovascular risk. Due to the large heterogeneity in the effects of iron deposition, there is no clear indication for recommending iron depletion in MH. The most recent international consensus recommend discussing bloodletting after an individual assessment in the absence of studies demonstrating a clear benefit in MH [23]. Indeed, a randomized trial did not show any benefit of bloodletting in patients with MH, even though the median LIC was 85 µM/g [26].

To date, no established criteria exist to guide the prescription of hepatic MRI to identify patients with significant iron overload (DIOS). Liver MRI is expensive and not widely available. As such, we searched for clinical and biological characteristics associated with a very high LIC (≥ 100 µmol/g) in patients with metabolic hyperferritinemia to propose a comprehensive predictive score of severe iron overload.

Methods

We conducted a single center retrospective cohort study in a referral department of a tertiary care university hospital. We included adult patients (≥ 18 years old) referred by their general practitioner for metabolic hyperferritinemia between 1 January 2017 and 21 February 2024. Patients were retrieved using the electronic database (ICD-10 E83.1 “Disorders of iron metabolism”). Inclusion criteria were: MRI for LIC assessment and metabolic hyperferritinemia according to the criteria defined in the international consensus [23]: ferritin > 300 µg/L (men) or 200 µg/L (women) associated with:

  • Evidence of fatty liver disease assessed by MRI.

  • Or type 2 diabetes mellitus.

  • Or obesity (BMI > 30 kg/m²).

  • Or at least two features of altered metabolism associated with insulin resistance (BMI > 25 kg/m², increase of waist circumference (> 102 cm in men or 88 cm in women), increased triglyceride levels (> 1.5 g/L), decreased HDL cholesterol levels (< 0.45 g/L in men or 0.55 in women), arterial hypertension (> 130 − 85 mmHg or use of antihypertensive drugs), increase of Homeostatic Model Assessment of insulin sensitivity-index (HOMA-IR > 2.7).

Exclusion criteria were: type 1 hereditary hemochromatosis (homozygous p.Cys282Tyr mutation on the HFE gene), increased serum ferritin due to systemic inflammation (C reactive protein higher than the upper limit of normal range, hemolysis, hepatic cytolysis (elevation higher than twice the standard laboratory values of alanine or aspartate aminotransferase), excessive alcohol consumption (> 60 g per day in men, > 40 g per day in women), erythropoiesis failure (sickle cell disease, intermediate or major thalassemia), red blood cell transfusions or iron therapy in the past 5 years.

Data collection

The data were extracted from the clinical medical reports using a standardized electronic form. In addition to the diagnosis criteria of the metabolic hyperferritinemia characteristics, we collected: daily alcohol consumption, smoking habits and familial history of hyperferritinemia requiring therapeutic phlebotomies. Moderate alcohol consumption as a cofactor of iron load in MH was defined in line with the consensus on MH [23] as a consumption of 30–59 g/day in men and 20–39 g/day in women.

Biological data: serum ferritin, serum iron, transferrin saturation, C reactive protein, aminotransferase, gammaglutamyl peptidase, alkaline phosphatase, glycaemia, hemoglobin A1C, haptoglobin, lactate dehydrogenase, bilirubin, full blood count and genetic testing (HFE mutations (p.Cys282Tyr, p.His63Asp, p.Ser65Cys), BMP6 mutations and FTL mutations).

The MRI assessment of the LIC used the Signal Intensity ratio method described by Gandon et al. [27]. Hepatic steatosis grade and splenic iron content assessed by MRI was collected when available.

Outcomes and statistical analysis

Our main objective was to build a predictive score of significant iron overload (threshold LIC ≥ 100 µmol/g). The secondary objectives of the study were to identify the factors associated with significant iron overload and their degree of correlation with liver iron content.

Categorical variables were described in terms of numbers and percentages, while quantitative variables were expressed in terms of mean and standard deviation or median and interquartile range, based on their statistical distribution. Normality was assessed with the Shapiro-Wilk test. Comparisons between groups (LIC ≥ 100 µmol/g vs. <100) were made using the Chi2 test or, where appropriate, Fisher’s exact test for categorical variables. Quantitative variables were studied using Student’s t-test or the Mann-Whitney test if necessary. The equality of variances was studied using the Fisher-Snedecor test.

For each criterion, odds ratios with a confidence interval were used to assess the correlation degree to the LIC. We used logistic multiple regression analyses for a dichotomous dependent variable (high LIC ≥ 100 µmol/g vs. < 100) to determine the factors predicting high LIC. Covariates were selected based on their clinical relevance and the results of univariate analysis. Particular attention was paid to the study of multicollinearity using the VIF index and the Farrar and Glauber test. Model quality indicators (log likelihood, Akaike information criterion, Bayesian information criterion, ROC curve analysis) were studied to select the most discriminant and parsimonious model. The performance of the predictive model was estimated using ROC curve analysis. A p-value < 0.05 was considered significant. A 1000 bootstrap replicate analysis with an expectation–maximization algorithm was used to obtain the predictive factors. Finally, a score was proposed based on the estimates obtained by the multivariate logistic model; the diagnostic capabilities of this score were described for a threshold set based on the indicators usually reported in the literature (Youden, Liu, efficiency). Statistical analysis was performed using Stata 15 (StataCorp, College Station, TX, USA).

Ethics

The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice recommendations, and approved by the local Ethics Committee who waived the need for informed consent (International Review Board 00013412, “CHU de Clermont-Ferrand IRB #1”, IRB number 2023-CF029) with compliance with the French policy of individual data information and protection.

Results

Baseline characteristics

From 1 January 2017 to 21 February 2024, 302/521 consecutive patients referred for iron metabolism disorders in our department fulfilled the MH diagnosis criteria. After exclusion of differential diagnoses, we included 217 patients (flowchart is available as supplementary Fig. 1) in the final analysis (180 men (83%), 58 [Q1Q3: 49–67] years old). High blood pressure (31%) and dyslipidemia (30%) were the most frequent metabolic syndrome features. Women had significantly lower BMI (24.7 [Q1Q3: 22.4–29.4] vs.27.7 [Q1Q3: 25-30.1], p = 0.01), lower waist circumference (96.5 ± 12.5 vs. 101.8 ± 10.8, p = 0.02), lower serum ferritin (603 [Q1Q3: 481–814] vs. 900 [Q1Q3: 696–1167], p < 0.001) and lower liver iron content (55 [Q1Q3: 33–80] vs. 70 [50–100], p = 0.05) than men. No patients were undergoing therapeutic phlebotomies at inclusion. Thirty-seven patients complied with the grade 3 MH diagnosis criteria (ferritin ≥ 1000 µg/L with LIC ≥ 100 µmol/g) [28].

Characteristics associated to high LIC

Fifty-five (25.4%) patients had elevated LIC ≥ 100 µmol/g. Among them, 29/55 (53%) had a ferritin < 1000 µg/L while 53 patients (33%) had a ferritin > 1000 µg/L in the subgroup of LIC < 100 µmol/g.

In univariate analysis (Table 1), a family history of hyperferritinemia requiring phlebotomies (p = 0.001), and elevated transferrin saturation (p < 0.001), were significantly associated with increased LIC. No significant difference was found in ferritin level (960 [Q1Q3: 751–1200] µg/L in patients with LIC ≥ 100 µmol/g vs. 827 [Q1Q3: 624–1125] µg/L in patients with LIC < 100 µmol/g, p = 0.08). Increased ferritin ≥ 1000 µg/L was not significantly associated to high LIC regardless of the LIC threshold (p = 0.06 for LIC ≥ 100 µmol/g, p = 0.14 for LIC ≥ 74 µmol/g).

Table 1 Characteristics associated to high liver iron content
Full size table

We found no statistically significant differences in iron metabolism mutations between patients with high LIC and the other patients (supplementary Table 2) Patients with a family history of hyperferritinemia requiring phlebotomies had more frequent mutations (65% vs. 31%, p = 0.002), indicating collinearity between these two variables. Splenic iron content was higher in patients with increased LIC (p = 0.01) but this data was available for only 21% of our cohort.

In logistic multivariate regression, family history of hyperferritinemia was significantly associated with high LIC (OR 6.15, CI95 [2.11–17.92]), as well as increased ferritin level ≥ 600 µg/L (OR 5.53, CI95 [1.43–21.42]) and an increase of transferrin saturation ≥ 45% (OR 2.63, CI95 [1.32–5.23]) (Table 2). When ferritin and transferrin saturation were studied as quantitative variables, a non-significant trend associated with high LIC was found (p = 0.09 and p = 0.002) after adjustment for age, gender and family history of hyperferritinemia. The presence of minor genetic mutations was not associated with high LIC after adjustment for gender and ferritin level (OR 1.9, CI95 [0.9–4], p = 0.08).

Table 2 Multivariate regression of characteristics associated with high liver iron content
Full size table

Predictive score

The Table 3 shows the score using the predictive factors identified in the multivariate regression analysis, weighted according to the odds-ratio. We chose to use only the features that were immediately available to the practitioner during the medical appointment to propose a simple clinical score.

Table 3 Predictive clinical score of high liver iron content in metabolic hyperferritinemia
Full size table

For the detection of high LIC, the AUC of the ROC curve of this 15-point score was 0.72 (CI95 [0.64–0.79]). The increase of 1 point of the score is associated with a likelihood ratio of 1.38, CI95 [1.20–1.57] (p < 0.001) of high LIC (≥ 100 µmol/g). Table 4 shows the sensitivity, specificity and predictive values of the score. Figure 1 shows the ROC curve. A cut-off threshold ≥ 9 provided the best compromise between sensitivity (59.6%) and specificity (96.7%), with an OR of 4.17 (CI95 [2.15–8.07], p < 0.001, a correct classification rate of 70.2%, a negative predictive value of 84%) at the inflection point of the curve.

Table 4 Statistical performance of high liver iron content clinical score
Full size table
Fig. 1
figure 1

receiver operative curve of predictive high liver iron content score

AUC: area under the curve

ROC: receiver operative curve

Full size image

Discussion

Metabolic hyperferritinemia patients are classified according to the severity of iron overload, assessed by the MRI LIC [23]. The authors of the consensus suggested considering iron depletion for grade 3 patients (LIC ≥ 74 µmol/g). Iron overload toxicity in DIOS is controversial: iron overload may worsen metabolic parameters through the increase of insulin resistance. In DIOS, iron load is mostly macrophagic [29] which was interestingly highlighted in our cohort by the correlation with splenic iron content. The toxicity of iron overloaded macrophage is supported by in vitro studies in sickle cell disease [30] and in non-alcoholic fatty liver disease [31]. An increased risk of liver damages was found in patients with fatly liver disease and iron overload compared to patient with fatty liver disease without iron overload [24]. In metabolic hyperferritinemia, a polarization toward a pro-inflammatory macrophage profile was found [16] in animal studies, but conflicting results were found in a human cohort study: despite an altered M1/M2 balance, no pro-inflammatory profile of macrophages was found [29]. Thus, the effect of therapeutic phlebotomies in MH remains highly debated. A randomized trial conducted prior to this international consensus evaluated the effect of bloodletting in patients with DIOS. While therapeutic phlebotomies were efficient in decreasing iron load, the main objective, which was to demonstrate an improvement of insulin sensitivity, was not achieved. Worse, during phlebotomies, some patients increased their weight and altered their insulin resistance (HOMA index) despite dietary and lifestyle advice [26]. A subgroup of patients improved their alanine aminotransferase levels, suggesting a hepatoprotective effect of bloodletting, but after adjustment for weight loss, this protective effect was not confirmed, suggesting that body weight was the principal factor of hepatic toxicity. However, the 1-year follow-up was not designed to evaluate the potential protective effect of bloodletting on cirrhosis and cancer. Interestingly, most included patients met the definitions of grade 3 metabolic hyperferritinemia (DIOS), thus we decided to study a higher threshold of ≥ 100 µmol/g. Moreover, tolerance of therapeutic phlebotomies can be poor in patients with MH due do underlying cardiac comorbidities, resulting in dizziness, hypotension and vagal malaise.

Liver MRI is an expensive test and is not always available in routine clinical practice. To guide the MRI prescription, we searched for easily available clinical and biological features associated with high LIC. While several identified factors associated to high LIC are consistent with those cited in the consensus conference for assessing the iron load (ferritin, transferrin saturation), this is the first time that this association is statistically demonstrated in MH. Elsewhere, the presence of a family history of hyperferritinemia requiring therapeutic phlebotomies was strongly correlated to LIC. To our best knowledge, this characteristic had never been reported before. We found mutations in HFE gene among 34% patients of the cohort, without significant differences between patients with and without high LIC, and identified a collinearity between mutations and familial history of hyperferritinemia. These findings are in line with a recent study demonstrating that compound HFE heterozygosity is very prevalent among patients with hyperferritinemia treated with phlebotomies [32].

Our study has several limitations. First, due to the retrospective design of our study, MRI assessment of LIC was performed in multiple centers and no centralized evaluation was available. However, the signal intensity ratio is the most widely available method to quantify iron deposition as it is free-of-access and feasible on every machine in the world, requiring no specific material. Second, some common clinical characteristics of MH were not associated with the LIC such as BMI or waist circumference, but this could be due to multi-collinearity between these variables. Moreover, these parameters were part of the metabolic syndrome diagnosis criteria and as such, we used them as inclusion criteria. Using male gender, ferritin level and transferrin saturation combined with a family history of hyperferritinemia requiring bloodletting, we build a predictive score of high LIC. Our score provides mild sensitivity (59%) but high specificity (> 90%), but the bootstrap replicate analysis showed that our model was robust. Certain missing data may have hampered the statistical power of our study: missing data are inherent to the retrospective design of the study, and a validation cohort will be required to validate the utility of this score in routine clinical practice. The fair negative predictive value (84%) may help physicians to better select patients for MRI LIC assessment, in line with recent international recommendations [33].

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to local regulatory constraints but are available from the corresponding author on reasonable request.

Abbreviations

AUC:

Area Under the Curve

BMI:

Body Mass Index

CI:

Confidence Interval

DIOS:

Dysmetabolic Iron Overload Syndrome

DNA:

Deoxyribonucleic Acid

HEIRS:

Hemochromatosis and Iron Overload Screening Study

HOMA:

Homeostatic Model Assessment of insulin sensitivity

LIC:

liver iron content

MH:

Metabolic Hyperferritinemia

MRI:

Magnetic Resonance Imaging

OR:

Odd Ratio

ROC:

Receiver Operative Curve

USA:

United States of America

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Acknowledgements

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Funding

The present study received no funding.

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Authors and Affiliations

  1. Médecine Interne, Hôpital Estaing, Centre Hospitalier Universitaire de Clermont-Ferrand, 1 Place Lucie et Raymond Aubrac, Clermont-Ferrand, 63100, France

    Mohamed Lotfi Boughzala, Marc Ruivard & Hervé Lobbes

  2. Département de Biostatistiques, Direction de la Recherche Clinique et de l’Innovation, Centre Hospitalier Universitaire de Clermont-Ferrand, 28 Place Henri Dunant, Clermont-Ferrand, 63000, France

    Bruno Pereira

  3. Institut Pascal, Thérapies Guidées par l’Image, UMR 6602 CNRS, Université Clermont Auvergne, 28 Place Henri Dunant, Clermont-Ferrand, 63000, France

    Marc Ruivard & Hervé Lobbes

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Contributions

HL proposed and designed this study. MB and HL collected the data and contributed to the original draft writing. BP conducted the statistical analysis. MB, BP, MR and HL contributed to the interpretation of data and to the review and edition of the final manuscript.

Corresponding author

Correspondence to Hervé Lobbes.

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Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice recommendations, and approved by the local Ethics Committee who waived the need for informed consent (International Review Board 00013412, “CHU de Clermont-Ferrand IRB #1”, IRB number 2023-CF029) with compliance with the French policy of individual data information and protection.

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Boughzala, M.L., Pereira, B., Ruivard, M. et al. A clinical predictive score of high liver iron content in metabolic hyperferritinemia: a retrospective cohort pilot study. BMC Gastroenterol 25, 331 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12876-025-03891-2

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12876-025-03891-2

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BMC Gastroenterology

ISSN: 1471-230X

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