Nutritional epidemiology plays a crucial role in understanding the impact of dietary choices on chronic diseases. However, traditional methods of evaluating dietary intake, such as food frequency questionnaires and food diaries, are often prone to inaccuracies due to human error or intentional misreporting. This can lead to misleading data and incorrect conclusions when developing nutritional strategies and policies.
To address this issue, an international team led by Prof. John Speakman from the Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences developed a novel predictive model for total energy expenditure. Published in Nature Food, this study combines classical statistics and machine learning to provide a more objective way of assessing the validity of food intake records.
The researchers utilized the doubly-labeled water technique, an isotope-based method that directly measures an individual’s energy needs. By analyzing over 6,000 measurements and applying advanced statistical and machine learning approaches, they derived a predictive model that was validated in an additional 600 subjects. This model is currently the most accurate method for estimating energy requirements without the need for physical measurements.
Applying this model to large surveys of food intake data in the US and UK, researchers found that a significant percentage of records had unrealistically low energy intake levels. This highlights the prevalence of misreporting in dietary surveys and the need for more accurate assessment methods.
Prof. John Speakman emphasized the importance of acknowledging and correcting flawed data in nutrition science. By using this new predictive model, researchers can identify and eliminate erroneous data, leading to more reliable conclusions and potentially revising long-held beliefs in the field.
For more information, the study titled “Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake” can be found in Nature Food. This groundbreaking research provides a valuable tool for improving the accuracy of dietary surveys and advancing our understanding of the link between diet and chronic diseases.
