Top Brain Boosters Blood Pressure & Weight Beat Diet

 

A groundbreaking machine learning study has pinpointed the lifestyle and health factors most strongly linked to sharp, focused brain function throughout life – with surprising results about the relative importance of diet versus physical factors. While healthy eating matters, the analysis reveals that age, blood pressure, and body weight (BMI) are significantly more powerful predictors of key cognitive abilities than diet alone.

Published in The Journal of Nutrition, the University of Illinois Urbana-Champaign research leveraged machine learning's unique ability to analyze complex, intertwined datasets. This approach moved beyond traditional statistics to evaluate how a multitude of factors – including demographics, physical health, diet quality, and exercise levels – collectively influence performance on a critical cognitive test: the flanker task.

The Flanker Task: Measuring Focus Under Pressure

The flanker task is a well-established neuroscience tool assessing attention and inhibitory control – essentially, the brain's ability to focus on the central task and ignore distractions. Participants must quickly identify the direction of a central arrow while ignoring surrounding "flanker" arrows pointing either the same way (easy) or the opposite way (hard). Success depends on processing speed and accuracy under distraction.

Machine Learning Cuts Through Complexity

Led by Professor Naiman Khan and PhD student Shreya Verma, the team fed data from 374 adults (ages 19-82) into various machine learning algorithms. This data included:

• Age, BMI, blood pressure (systolic & diastolic)
• Physical activity levels
• Dietary patterns (assessed via the Healthy Eating Index)
• Performance metrics from the flanker task

"Standard statistical approaches cannot embrace this level of complexity all at once," explained Khan. "Machine learning offers a promising avenue for analyzing large datasets with multiple variables and identifying patterns that may not be apparent through conventional approaches."

The algorithms were rigorously tested and validated to determine which factors most accurately predicted how quickly participants could respond correctly on the flanker test.

The Hierarchy of Cognitive Influencers Emerges

The machine learning model revealed a clear hierarchy of influence:

1. Age: The strongest predictor, confirming known declines in processing speed with aging.
2. Diastolic Blood Pressure: Emerged as the second most influential factor.
3. Body Mass Index (BMI): Closely followed diastolic BP.
4. Systolic Blood Pressure: Also a major predictor.
5. Diet (Healthy Eating Index): Played a smaller, but still relevant and statistically significant role.
6. Physical Activity: Emerged as a moderate predictor.

• Diet & Exercise as Compensators: While diet ranked lower than blood pressure and BMI, the analysis suggested its power might lie in interaction. Healthy eating and physical activity appeared to sometimes offset the negative cognitive effects associated with higher BMI or other detrimental factors. "Physical activity emerged as a moderate predictor... with results suggesting it may interact with other lifestyle factors, such as diet and body weight, to influence cognitive performance," Khan noted.
• Blood Pressure's Crucial Role: The prominence of both systolic and diastolic blood pressure highlights cardiovascular health as a critical pillar of brain health, potentially influencing blood flow and vessel integrity in the brain.
• BMI's Cognitive Cost: The strong link between higher BMI and poorer flanker performance underscores the systemic impact of body weight on neurological function.

The study acknowledges that established brain-healthy diets like DASH, Mediterranean, and MIND have shown protective effects against cognitive decline. However, this ML approach suggests that in the complex interplay of factors influencing real-time cognitive performance (like the flanker task), direct physiological markers like blood pressure and body weight might have a more immediately measurable impact than overall dietary patterns alone.

"Clearly, cognitive health is driven by a host of factors, but which ones are most important?" asked Verma. "We wanted to evaluate the relative strength of each of these factors in combination with all the others."

The findings point towards a future where machine learning helps tailor interventions. "This study reveals how machine learning can bring precision and nuance to the field of nutritional neuroscience," Khan stated. "By moving beyond traditional approaches, machine learning could help tailor strategies for aging populations, individuals with metabolic risks or those seeking to enhance cognitive function through lifestyle changes."

While eating well and staying active remain vital components of a brain-healthy lifestyle, this cutting-edge machine learning study emphasizes that managing blood pressure and maintaining a healthy weight may be even more critical for preserving sharp focus and quick cognitive processing as we age. It underscores the interconnectedness of cardiovascular health, metabolic health, and brain function, providing a clearer roadmap for interventions aimed at maintaining cognitive vitality.