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Research published in Nutritional Journal Machine learning techniques were used to determine which health and lifestyle factors best predict cognitive performance across adult life expectancy. This study assessed how variables such as age, blood pressure, body mass index (BMI), diet, and physical activity were related to performance of tasks testing attentional control and response speed.
This task, known as the flanker test, requires that individuals identify the orientation of the central arrow while ignoring adjacent arrows that distract them. This is a commonly used measure of attention and inhibitory control.
Age and cardiovascular measurements appeared as dominant predictors
Data were collected from 374 adults aged 19 to 82 years and evaluated using a wide range of measures including BMI, dietary habits, blood pressure, and physical activity levels. Participants also completed flanker tests that measured both accuracy and response time.
Among the machine learning models evaluated, the most accurate predictor of performance was emphasized as the strongest single predictor. Diastolic BP, BMI, and systolic BP were affected. Diet quality measured by adherence to a healthy diet index was less predictive than age or cardiovascular measurements, but was associated with improved task performance.
Lifestyle patterns can mitigate certain risk factors
This study noted that high BMI and increased blood pressure are generally associated with lower outcomes, but increased physical activity and better dietary adherence may partially offset these effects. In particular, physical activity emerged as a moderate predictor, and interactions with other lifestyle factors suggested a complex relationship with cognitive performance.
Machine learning provides depth of analysis
Traditional statistical methods often examine a single variable alone. In contrast, machine learning allows for the simultaneous evaluation of multiple potentially interacting variables. This approach revealed a nuanced connection between lifestyle factors that may otherwise remain obscure.
Researchers tested several algorithms to identify the best algorithms that could predict cognitive performance based on collected data. The models were validated using standard machine learning practices to assess robustness.
This study was supported by the Personalized Nutrition Initiative and the National Center for Supercomputing Applications at the University of Illinois Urbana-Champaign.
reference: Predict cognitive outcomes via nutrition and health markers using supervised machine learning. J Nutr. 2025. doi:10.1016/j.tjnut.2025.05.003
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