Socioeconomic inequality and pollution linked to accelerated brain aging

The human brain undergoes significant changes over time, with its functional and structural dynamics evolving as we age. Understanding these changes is vital for unraveling the complexities of neurodegenerative diseases like Alzheimer’s and other cognitive impairments.

Recently, research, published in the journal Nature Medicine, has made strides in this field by investigating how factors such as socioeconomic disparities, environmental pollution, and gender inequalities influence brain aging across diverse populations. Their findings not only highlight the intricacies of brain health but also underscore the urgent need for more inclusive research methodologies.

The pace of brain aging varies among individuals and populations, creating gaps between the brain’s biological age and chronological age. These gaps, referred to as brain-age gaps, are influenced by environmental and social factors.

A groundbreaking study involving over 5,300 participants from 15 countries, including several Latin American and Caribbean (LAC) nations, revealed that socioeconomic inequality and pollution significantly accelerate brain aging. In regions with higher disparities, individuals exhibited older brain ages, particularly in areas of the brain most susceptible to age-related decline.

Dr. Daniel Abasolo, Head of the Centre for Biomedical Engineering at the University of Surrey, noted, “Our research shows that in countries where inequality is higher, people’s brains tend to age faster, especially in areas of the brain most affected by aging. Factors like socioeconomic inequality, air pollution, and the impact of diseases play a big role in this faster aging process, particularly in poorer countries.”

The study employed advanced brain clocks based on deep learning of brain networks, analyzing functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data. These tools enabled researchers to quantify brain-age gaps among healthy individuals and those with neurodegenerative conditions like mild cognitive impairment (MCI), Alzheimer’s disease, and frontotemporal dementia.

The results were clear: socioeconomic inequality, pollution, and limited access to healthcare are powerful predictors of accelerated brain aging, particularly in LAC regions.

Gender differences emerged as a critical factor in brain aging. Women in LAC countries, particularly those with Alzheimer’s, exhibited significantly larger brain-age gaps than their male counterparts. This disparity highlights the compounded effects of biological sex and structural gender inequalities, such as limited access to education and healthcare.

Research also indicates that country-level gender inequality impacts cortical thickness, further emphasizing the role of societal factors in shaping brain health.

Adverse environments, characterized by poor healthcare infrastructure and social disparities, affect neural development by disrupting processes like dendritic branching and synapse formation. These findings shed light on the intersection of biological and social determinants of brain health.

Traditional brain-aging studies often rely on structural imaging techniques like magnetic resonance imaging (MRI). While effective, these methods are costly and less accessible in underrepresented regions like LAC.

This study broke new ground by integrating data from EEG, a more affordable and portable technology, with fMRI. EEG’s ability to track brain activity with millisecond precision makes it a valuable tool for studying brain network dynamics.

However, the widespread application of EEG faces challenges, including inconsistencies in data acquisition, electrode layouts, and processing methods. To address these issues, researchers used graph convolutional networks (GCN) to analyze the functional connectivity of fMRI and EEG signals.

This approach not only enhanced the accuracy of brain-age predictions but also accounted for the effects of multimodal diversity, such as geographical and sociodemographic differences.

The study’s deep learning architecture demonstrated remarkable accuracy. For example, in LAC participants, the models showed a mean directional error of 5.60 years and a root mean square error (r.m.s.e.) of 11.91 years for fMRI data.

EEG data yielded similarly precise results, with a mean directional error of 5.34 years and an r.m.s.e. of 9.82 years. These findings underscore the potential of combining EEG and fMRI with advanced computational methods to develop scalable and inclusive brain-health markers.

The study’s findings have far-reaching implications for public health and neuroscience. By identifying the factors contributing to accelerated brain aging, researchers can develop targeted interventions to mitigate risks. For example, policies aimed at reducing socioeconomic inequality and pollution could significantly improve brain health outcomes in vulnerable populations.

The research also underscores the importance of integrating diverse populations into scientific studies. Historically, most brain-aging research has focused on participants from the Global North, limiting the generalizability of findings to underrepresented regions like LAC.

This study’s inclusive approach provides a more comprehensive understanding of how macro factors—such as income inequality and environmental conditions—interact with biological processes to influence brain health.

Moreover, the study’s framework offers a pathway toward personalized medicine. By incorporating multiple dimensions of diversity, researchers can tailor interventions to individual risk profiles, enhancing the effectiveness of treatments for neurodegenerative diseases.

For example, early detection tools based on brain-age models could identify at-risk individuals and enable timely interventions to slow cognitive decline.

As the global population ages, addressing the disparities in brain health becomes increasingly urgent. This study not only advances our understanding of brain aging but also highlights the critical role of environmental and social factors in shaping cognitive health.

Policymakers and healthcare providers must consider these findings to promote healthier aging across diverse populations, ensuring that the benefits of scientific advancements are accessible to all.

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