| Title: | Ferritin and transferrin predict common carotid intima-media thickness in females: a machine-learning informed individual participant data meta-analysis |
| Journal: | BMC Cardiovascular Disorders |
| Published: | 14 Apr 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41981480/ |
| DOI: | https://doi.org/10.1186/s12872-026-05796-8 |
| URL: | http://dx.doi.org/10.1186/s12872-026-05796-8 |
Publication 18005
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Abstract
BackgroundIron overload promotes atherosclerosis in mice and causes vascular dysfunction in humans with Hemochromatosis. However, data are controversial on whether systemic iron availability within physiological limits affects the pathogenesis of atherosclerosis. We, therefore, performed an individual participant data (IPD) meta-analysis and studied the association between serum iron biomarkers with common carotid intima-media thickness (CC-IMT); in addition, since sex influences iron metabolism and vascular diseases, we studied if there are sex-specific differences.MethodsWe pooled the IPD and analysed the data on adults (age≥18y) by orthogonal approaches: machine learning (ML) and a single-stage meta-analysis. For ML, we tuned a gradient-boosted tree regression model (XGBoost) and subsequently, we interpreted the features using variable importance. For the single-stage metaanalysis, we examined the association between iron biomarkers and CC-IMT using spline-based linear mixed models, accounting for sex interactions and study-specific effects. To confirm robustness, we repeated analyses on imputed data using multivariable regression adjusted for key covariates identified through machine learning. Further, subgroup analyses were performed in children and adolescents (age<18y). In addition, to evaluate causality, we used UK Biobank data to examine associations between the hemochromatosis (HFE) genotypes (C282Y/H63D) and mean CC-IMT in ~ 42,500 participants with carotid ultrasound data, using sex-stratified linear regression (adjusted for age, assessment centre, and genetic principal components).ResultsWe included IPD from 21 studies (N = 10,807). The application of the ML model showed moderate predictive performance and identified iron biomarkers (transferrin, ferritin, transferrin saturation, and iron) as key features for IMT prediction. Multivariable analyses showed non-linear sex-specific relationships for ferritin and transferrin with CC-IMT, both only among females at specific ranges. Ferritin showed a significant positive association [Ferritin > 233 ng/mL: β = 0.04, 95% CI (0.002, 0.08), p = 0.037], while transferrin showed negative associations at specific ranges [ Transferrrin 231-263 mg/dL: β=-0.21, 95% CI (-0.43, 0.003), p = 0.054; Transferrrin > 263 mg/dL: β=-0.73, 95% CI (-1.48, 0.01), p = 0.05]; No significant associations were found between CC-IMT in those with HFE genotypes in either sex in the UK Biobank.ConclusionOur observational data show that iron biomarkers - ferritin and transferrin are non-linearly associated with CC-IMT specifically in females, while a significant causal association between the HFE genotype and CC-IMT could not be demonstrated in the UK Biobank data. We conclude that our observational findings may reflect residual confounding, reverse causation, or other non-causal mechanisms rather than a direct causal relationship.OtherNo financial support was received for this meta-analysis. The protocol for this study is registered in the PROSPERO database ( CRD42020155429; https://www.crd.york.ac.uk/).</p>
23 Authors
- Anand Ruban Agarvas
- Richard Sparla
- Janice L Atkins
- Claudia Altamura
- Todd Anderson
- Ebru Asicioglu
- Judit Bassols
- Abel López-Bermejo
- Hana Marie Dvořáková
- José Manuel Fernández-Real
- Christoph Hochmayr
- Michael Knoflach
- Jovana Kusic Milicevic
- Silvia Lai
- José María Moreno-Navarrete
- Dariusz Pawlak
- Krystyna Pawlak
- Petr Syrovatka
- Dorota Formanowicz
- Pavel Kraml
- Jose M Valdivielso
- Luca Valenti
- Martina U. Muckenthaler
1 Application
| Application ID | Title |
|---|---|
| 14631 | Genetic and environmental influences on ageing well |
Enabling scientific discoveries that improve human health