Lifestyle, biological and genetic determinants of brain iron accumulation in aging
Jonatan Gustavsson, Zuzana Ištvánfyová, Farshad Falahati, Goran Papenberg, Francesca Mangialasche, Grégoria Kalpouzos- Psychiatry and Mental health
- Cellular and Molecular Neuroscience
- Geriatrics and Gerontology
- Neurology (clinical)
- Developmental Neuroscience
- Health Policy
- Epidemiology
Abstract
Background
Optimal iron levels are crucial for brain cells’ functioning. However, brain iron overload occurs in both normal aging and neurodegenerative disorders, including Alzheimer’s disease (AD). High iron concentration triggers oxidative stress and neuroinflammation, both involved the AD pathophysiology. Despite growing evidence of the toxicity of iron overload, why iron accumulates remains unknown. A few studies have linked higher brain iron content to alcohol use, body mass index, cardiovascular health, and smoking. However, no study has investigated the factors behind iron accumulation longitudinally.
Method
We investigated the associations between potential determinants such as blood markers of iron metabolism, cardiovascular health (Framingham’s risk score; FRS), lifestyle factors (smoking, alcohol use, physical activity), ApoE (any e4) status, and iron content and iron accumulation using longitudinal data (n = 208 healthy volunteers, age = 20‐79, mean follow‐up time = 2.75 years) from the IronAge study. Iron was assessed with Magnetic Resonance Imaging using quantitative susceptibility mapping for iron‐rich or age‐sensitive regions (caudate (CA), putamen (PU), pallidum (PA), dorsolateral prefrontal cortex (DLPFC)). We used structural equation modelling to assess the relationships between determinants and iron accumulation.
Result
Higher blood ferritin was related to higher iron content and iron accumulation in DLPFC (B = 0.181 and 0.173), CA (B = 0.158 and 0.225) and PU (B = 0.127 and 0.263). Higher blood haemoglobin was related to higher iron content in DLPFC (β = 0.177) and CA (β = 0.178), as well as to iron accumulation in PA (β = 0.220). Higher blood transferrin was related to lower iron content and iron accumulation in CA (B = ‐0.131 and ‐0.147) and PU (B = ‐0.160 and ‐0.232). Lower FRS was related to greater iron accumulation in DLPFC among younger adults (β = ‐0.379, age<40). Increased FRS was related to greater iron accumulation (β = 0.194). More physical activity was related to higher iron content in PA among younger adults (β = 0.231, age<40). Lastly, older (age>40) ApoE e4 carriers had higher iron content in PA (β = 0.219). No associations were found with alcohol or smoking. These results were independent of, sex, education, atrophy, white‐matter lesions, and age unless age‐stratified results.
Conclusion
Our study identified modifiable and non‐modifiable factors of brain iron accumulation in adulthood. The former can be targeted to prevent accelerated iron accumulation.