Unlocking Brain Health: How Machine Learning Reveals Essential Lifestyle Factors for Optimal Cognitive Function

A recent study sheds light on health and lifestyle factors tied to better brain function. Researchers used machine learning to find out what influences how quickly and accurately people can focus on tasks without getting distracted.

Published in The Journal of Nutrition, the study highlighted that age, blood pressure, and body mass index (BMI) were key indicators of success on a task called the flanker test. This test measures how well someone can concentrate on one object while ignoring surrounding distractions.

Interestingly, diet and physical activity also played a role, even if it was smaller. These factors can help counteract the negative effects of high BMI and other health issues.

Naiman Khan, a professor of health and kinesiology at the University of Illinois Urbana-Champaign, led the research. He explained, “Standard methods can’t analyze complex data like this study does.” By using machine learning, they could look at many factors at once, which provided deeper insights into cognitive performance.

The study involved 374 adults aged 19 to 82. Researchers collected various data, including age, BMI, blood pressure, activity levels, and dietary habits. They then assessed how these factors influenced performance on the flanker test.

Khan noted that previous research has linked better cognitive function in older adults to healthy eating habits. Diets rich in antioxidants and omega-3 fatty acids have been associated with maintaining brain health. For example, the Dietary Approaches to Stop Hypertension (DASH) diet and the Mediterranean diet have shown potential in protecting against cognitive decline.

“Diets like the Mediterranean and MIND have been shown to help prevent cognitive decline and dementia,” Khan remarked.

Verma, a Ph.D. student working with Khan, asked an important question: “What factors are most significant for cognitive health?” The study aimed to rank the importance of various lifestyle factors together.

The researchers tested multiple machine learning models to see which factors best predicted test performance. They found age was the most critical factor, followed by diastolic blood pressure and BMI. While the healthy eating index correlated with better scores, it was less influential than blood pressure or BMI.

“Physical activity was a moderate predictor of how quickly participants responded, suggesting it works in tandem with diet and weight to impact cognitive function,” Khan added.

This research offers new avenues in nutritional neuroscience. By using machine learning, scientists can refine strategies for improving cognitive health, especially in aging populations or individuals at metabolic risk.

This study was supported by the Personalized Nutrition Initiative and the National Center for Supercomputing Applications at the University of Illinois. Khan, who is also a dietitian, is involved in multiple programs at the university related to nutrition and neuroscience.

For a deeper dive, check out the original study: Predicting Cognitive Outcome Through Nutrition and Health Markers Using Supervised Machine Learning.

In summary, focusing on our diet, staying active, and monitoring health metrics like BMI and blood pressure can positively influence brain function. As research progresses, we may find even more targeted ways to enhance cognitive health.

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Aging, Blood, Blood Pressure, Body Mass Index, Brain, Cognitive Function, Diet, Exercise, Kinesiology, Machine Learning, Neuroscience, Nutrition, Physical Activity, Research