article · Nature Food
Nutritional epidemiology links diet to chronic disease, but tools measuring dietary intake frequently produce inaccurate data. To detect unreliable reporting, estimated intakes can be evaluated against total energy expenditure. Using 6,497 doubly labelled water measurements from individuals aged 4 to 96 years held in the International Atomic Energy Agency database, a predictive regression equation was developed. The model calculates expected energy expenditure using accessible variables, namely body weight, age, and sex, providing 95% predictive limits to screen for misreporting in dietary research. When tested on the National Diet and Nutrition Survey and the National Health and Nutrition Examination Survey, the equation revealed misreporting levels exceeding 50%. Increasing misreporting introduced systematic bias into reported macronutrient compositions, creating potentially false associations between specific dietary components and body mass index.
Public health guidance relies on nutritional studies to understand the connections between diet, obesity, and chronic diseases. Widespread errors in self-reported food intake undermine this evidence base. By establishing a straightforward method to identify misreported data using basic physical metrics, research programmes can clean existing datasets and avoid drawing erroneous conclusions about the health impacts of specific foods.
The predictive equation is an applied and tested data-screening tool suited for epidemiological researchers, public health bodies, and clinical trial managers. Although primarily an analytical method rather than an off-the-shelf commercial product, the equation could be integrated into nutrition tracking software and health data platforms to automatically flag implausible dietary intake records.
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Nutritional epidemiology aims to link dietary exposures to chronic disease, but the instruments for evaluating dietary intake are inaccurate. One way to identify unreliable data and the sources of errors is to compare estimated intakes with the total energy expenditure (TEE). In this study, we used the International Atomic Energy Agency Doubly Labeled Water Database to derive a predictive equation for TEE using 6,497 measures of TEE in individuals aged 4 to 96 years. The resultant regression equation predicts expected TEE from easily acquired variables, such as body weight, age and sex, with 95% predictive limits that can be used to screen for misreporting by participants in dietary studies. We applied the equation to two large datasets (National Diet and Nutrition Survey and National Health and Nutrition Examination Survey) and found that the level of misreporting was >50%. The macronutrient composition from dietary reports in these studies was systematically biased as the level of misreporting increased, leading to potentially spurious associations between diet components and body mass index.
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DOI: 10.1038/s43016-024-01089-5
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