Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake

Abstract

Key Aspects

• Study Objective: The primary objective of this study is to address the pervasive issue of inaccurate dietary intake data in nutritional epidemiology. The researchers aim to develop a more accurate method for identifying unreliable self-reported dietary data by comparing reported energy intake with total energy expenditure (TEE).
• Methodology: The study utilizes the International Atomic Energy Agency Doubly Labeled Water Database, which contains 6,497 TEE measurements from individuals aged 4 to 96 years. This data is used to derive a predictive equation for TEE based on easily acquired variables such as body weight, age, and sex.
• Predictive Equation Development: A regression equation is developed to predict expected TEE. This equation has 95% predictive limits, which are then used to screen for misreporting by participants in dietary studies. The equation is designed to be more accurate than previous methods, such as the Goldberg cut-off, which have limitations due to errors in predicting basal metabolic rate and arbitrary multipliers.
• Application and Findings: The predictive equation is applied to two large datasets: the National Diet and Nutrition Survey and the National Health and Nutrition Examination Survey. The analysis reveals that the level of misreporting in these studies is greater than 50%.
• Bias Identification: The study finds that the macronutrient composition reported in these dietary studies is systematically biased. Specifically, the level of misreporting is correlated with the reported proportions of different macronutrients, leading to potentially spurious associations between diet components and body mass index.
• Implications: The findings highlight the significant issue of misreporting in dietary studies and its impact on the accuracy of nutritional epidemiology research. The developed predictive equation offers a more robust tool for identifying and potentially correcting for this misreporting, thereby improving the reliability of research in this field.

Strengths

• Comprehensive Data Utilization

  The study leverages a large and diverse dataset from the International Atomic Energy Agency Doubly Labeled Water Database, enhancing the robustness and generalizability of the predictive equation.

  "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." (Page 58)
• Clear Methodology

  The abstract clearly outlines the methodological approach, including the development of a regression equation and its application to large datasets, providing a concise overview of the study's design.

  "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." (Page 58)
• Significant Findings

  The study reveals a high level of misreporting in dietary studies and identifies systematic biases in reported macronutrient composition, highlighting critical issues in nutritional epidemiology.

  "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%." (Page 58)

Suggestions for Improvement

• Enhance Clarity on Equation Variables

  This medium-impact improvement would enhance the reader's understanding of the predictive equation's inputs and their relevance. The Abstract section particularly needs this detail as it sets the stage for the study's methodology and findings. Elaborating on the specific variables used in the regression equation would strengthen the paper by providing a clearer picture of how TEE is predicted and how these variables were chosen. This would also help readers appreciate the novelty and robustness of the approach compared to previous methods. Ultimately, clarifying the variables used in the predictive equation would improve the study's scientific contribution by ensuring the methodology is transparent and easily understood.

  "The resultant regression equation predicts expected TEE from easily acquired variables, such as body weight, age and sex, with 95% predictive limits" (Page 58)

  Implementation: Specifically mention the key variables used in the regression equation, such as body weight, age, sex, and any other significant predictors. For example, "The resultant regression equation predicts expected TEE from easily acquired variables, such as body weight, age, sex, height, and ethnicity, with 95% predictive limits..."

• Provide Context on Misreporting Implications

  This high-impact improvement would significantly enhance the reader's understanding of the study's broader implications. The Abstract section needs this context to effectively communicate the significance of the findings to the field of nutritional epidemiology. Briefly elaborating on the consequences of misreporting for research and public health would strengthen the paper by highlighting the importance of accurate dietary data and the potential impact of the new predictive equation. This would also underscore the study's contribution to improving the reliability of nutritional research. Ultimately, providing context on the implications of misreporting would significantly improve the study's impact by emphasizing the practical value of the findings.

  "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." (Page 58)

  Implementation: Include a sentence or phrase that briefly explains the implications of misreporting for nutritional epidemiology. For example, "This misreporting can lead to inaccurate conclusions about diet-disease relationships and hinder the development of effective public health interventions."

• Clarify the Novelty of the Approach

  This medium-impact improvement would enhance the reader's understanding of the study's unique contribution to the field. The Abstract section needs this clarification to effectively position the research within the existing literature. Briefly explaining how the new predictive equation differs from and improves upon previous methods would strengthen the paper by highlighting its novelty and potential to advance the field. This would also help readers appreciate the significance of the study's findings in the context of existing research limitations. Ultimately, clarifying the novelty of the approach would improve the study's scientific contribution by clearly demonstrating its advancement over previous methods.

  "Nutritional epidemiology aims to link dietary exposures to chronic disease, but the instruments for evaluating dietary intake are inaccurate." (Page 58)

  Implementation: Add a sentence or phrase that explicitly states how the new predictive equation differs from previous approaches, such as the Goldberg cut-off. For example, "Unlike previous methods that rely on basal metabolic rate estimations and arbitrary multipliers, this equation uses a data-driven approach to predict TEE and identify misreporting."

Introduction

Key Aspects

• Problem of Misreporting in Dietary Studies: The Introduction section highlights the pervasive issue of misreporting in dietary studies, which complicates the accurate quantification of food intake and hinders the ability to link nutritional exposures to disease outcomes. Misreporting encompasses inaccuracies in estimating food amounts, memory lapses, deliberate falsification of reports, and changes in eating behavior during recording periods. This issue is not limited to self-reporting but also includes errors introduced by investigators during data conversion, such as assuming uniform portion sizes.
• Consequences of Misreporting: The section emphasizes that misreporting has led to significant misunderstandings in nutritional epidemiology, such as the erroneous belief that individuals with obesity have very low energy intakes. This has resulted in a misattribution of obesity to defects in energy expenditure rather than inaccuracies in dietary reporting. The severity of misreporting has prompted calls to cease publishing studies relying on self-reported dietary intake, yet these studies continue to proliferate, supported by endorsements from various government bodies.
• Early Attempts to Address Misreporting: Early efforts to address misreporting involved defining cut-off limits for screening intake records based on predicted basal energy expenditure (BEE). The 'Goldberg cut-off' was developed by multiplying estimated BEE by 1.35, assuming that a daily total energy expenditure (TEE) lower than this value would be incompatible with survival. However, this method is prone to errors in predicting resting metabolic rate and relies on an arbitrary multiplier, thus only detecting very low reported intakes and missing many other inaccuracies.
• Introduction of Doubly Labeled Water Technique: The doubly labeled water (DLW) technique, which measures energy expenditure directly from the elimination of isotopes of oxygen and hydrogen, is introduced as a more accurate method. An analytical error of about 7% is associated with this technique, depending on the equation used. McCrory et al. proposed using DLW measurements to predict TEE and screen dietary recalls, offering an improvement over the Goldberg cut-off. However, this approach was limited by its reliance on equations derived from a small sample and the use of arbitrary cut-off limits.
• Current Study's Approach: The current study aims to address the limitations of previous methods by assembling a large database of DLW measurements from over 7,500 individuals. This database is used to derive prediction equations for TEE based on easily measured parameters such as body weight, height, age, and sex. The study's goal is to provide a more robust tool for identifying individuals who may be under- or over-reporting their dietary intake in surveys, thereby improving the accuracy of nutritional epidemiology research.
• Application to Large Datasets: The derived prediction equations are applied to two large publicly available dietary surveys, the National Diet and Nutrition Survey (NDNS) and the National Health and Nutrition Examination Survey (NHANES). The study demonstrates the use of these equations in identifying misreporting and shows that the level of under-reporting is underestimated by previous tools, leading to biases in evaluating dietary composition.

Strengths

• Comprehensive Overview of the Problem

  The Introduction effectively outlines the pervasive issue of misreporting in dietary studies, providing a clear rationale for the study's focus on developing a more accurate method for identifying such errors.

  "All these methods are prone to 'misreporting' because people cannot accurately estimate the amount of food they are eating" (Page 58)
• Clear Explanation of Previous Methods and Limitations

  The section clearly explains previous methods, such as the Goldberg cut-off and its modifications, and highlights their limitations, setting the stage for the need for a new approach.

  "However, this approach is susceptible to two major problems: error in the predicted resting metabolic rate and the arbitrary nature of the 1.35 multiplier." (Page 59)
• Strong Justification for the Current Study

  The Introduction provides a strong justification for the current study by highlighting the limitations of existing methods and the need for a more robust approach based on a large dataset of doubly labeled water measurements.

  "In this context, we have assembled a database of DLW measurements of healthy individuals" (Page 59)

Suggestions for Improvement

• Expand on the Novelty of the DLW Database

  This medium-impact improvement would enhance the reader's understanding of the study's unique contribution to the field. The Introduction section needs this clarification to effectively position the research within the existing literature and highlight the innovative use of the extensive DLW database. Elaborating on the specific advantages and novel aspects of this database, such as its size, diversity, and the inclusion of various age groups and ethnicities, would strengthen the paper by emphasizing its potential to overcome limitations of previous studies. This would also help readers appreciate the significance of the study's findings in the context of existing research limitations and underscore the advancement this database represents in the field of nutritional epidemiology. Ultimately, expanding on the novelty of the DLW database would improve the study's scientific contribution by clearly demonstrating its advancement over previous methods and its potential to provide more accurate and generalizable insights into dietary misreporting.

  "In this context, we have assembled a database of DLW measurements of healthy individuals." (Page 59)

  Implementation: Include a paragraph that details the unique features of the DLW database, such as its size, diversity, and the range of ages and ethnicities included. For example, "This study leverages an unprecedentedly large and diverse database of doubly labeled water measurements, encompassing over 7,500 individuals aged 8 days to 96 years from various ethnic backgrounds. This extensive dataset allows for the development of more robust and generalizable prediction equations for TEE, addressing limitations of previous studies that relied on smaller, less diverse samples."

• Clarify the Implications of Misreporting for Public Health

  This high-impact improvement would significantly enhance the reader's understanding of the study's broader implications for public health. The Introduction section needs this context to effectively communicate the significance of accurate dietary assessment beyond the research setting. Briefly elaborating on how misreporting can lead to flawed public health policies, inaccurate dietary guidelines, and ineffective interventions would strengthen the paper by highlighting the real-world consequences of the problem. This would also underscore the study's contribution to improving the evidence base for public health nutrition and emphasize the practical value of the new predictive equation in addressing these issues. Ultimately, clarifying the implications of misreporting for public health would significantly improve the study's impact by emphasizing the importance of accurate dietary data for promoting population health and preventing chronic diseases.

  "Misreporting has real negative consequences." (Page 58)

  Implementation: Add a few sentences that explain the potential consequences of misreporting for public health policy and interventions. For example, "Accurate dietary assessment is crucial not only for research but also for informing public health policies, developing dietary guidelines, and designing effective interventions to prevent chronic diseases. Misreporting can lead to erroneous conclusions about diet-disease relationships, resulting in misguided policies and interventions that fail to address the true nutritional needs of the population."

• Provide More Context on the Study Population

  This medium-impact improvement would enhance the reader's understanding of the study's scope and generalizability. The Introduction section needs this context to effectively frame the research within the broader population and highlight any potential limitations. Briefly describing the characteristics of the study population, such as age range, sex distribution, and ethnicity, would strengthen the paper by providing a clearer picture of the individuals included in the database and the potential applicability of the findings. This would also help readers assess the representativeness of the sample and identify any potential biases or limitations in generalizing the results to other populations. Ultimately, providing more context on the study population would improve the study's scientific contribution by ensuring transparency and allowing for a more nuanced interpretation of the findings.

  "The database includes measurements of over 7,500 individuals of diverse ethnicity aged 8 days to 96 years." (Page 59)

  Implementation: Include a brief description of the study population, mentioning the age range, sex distribution, and ethnicity of the individuals included in the DLW database. For example, "The database includes measurements from over 7,500 individuals aged 8 days to 96 years, with a diverse representation of ethnicities, including White, African, Asian, and Hispanic populations. The sample includes both males and females, providing a comprehensive dataset for developing prediction equations across different demographic groups."

Results

Key Aspects

• Predictive Model Development: The researchers developed predictive models for Total Energy Expenditure (TEE) using two primary approaches: classical general linear regression and machine learning models. The general linear regression included variables such as body weight, height, age, age squared, self-reported ethnicity, sex, and elevation. Machine learning models, including Random Forest, XGBoost, and Support Vector Regression, were also tested but did not improve upon the classical regression, likely because the predictors were linearly related to the output variable. The natural logarithm of body weight (ln(BW)) was the most significant predictor, with other variables like height, age, age squared, elevation, and sex also being highly significant. Notably, females had lower TEE than males, and there were significant differences in TEE based on self-reported ethnicity, particularly among White and African participants living outside Africa.
• Predictive Equation Derivation: A predictive equation for TEE was derived with coefficients reduced to four significant figures, resulting in minimal discrepancy (0.03%) compared to an equation using full precision. The equation is expressed as: In(TEE) = -0.2127 + 0.4167 × In(BW) + 0.006565 × Height - 0.02054 × Age + 0.0003308 × Age² - 0.000001852 × Age³ + 0.09126 × In(Elevation) - 0.04092 × Sex + 0.01940 × A - 0.03899 × AA + 0.006238 × AS + 0.02626 × W - 0.0155 × H + 0.003589 × NA - 0.0006759 × Height × In(Elevation) + 0.002018 × Age × In(Elevation) - 0.00002262 × Age² × In(Elevation) - 0.006947 × Sex × In(Elevation). This equation allows for the prediction of TEE based on easily measured parameters and includes adjustments for various demographic factors.
• Validation and Predictive Intervals: The derived equation was validated using a separate dataset of 598 individuals. The validation confirmed that 94.6% of independent TEE measurements fell within the 95% predictive limits. Ninety-five percent predictive intervals (95% PI) were calculated to provide a range of values likely to contain the true value for a new observation. The lower and upper 95% PI were defined as: Lower 95% PI = (pTEE × 0.7466) – 1.5405 and Upper 95% PI = (pTEE × 1.3395) + 2.7668, where pTEE is the predicted mean TEE. These intervals offer an objective evaluation of confidence for each prediction, surpassing previous methods that relied on arbitrary cut-off points.
• Application to Survey Data: The predictive equation was applied to two large dietary surveys: the National Diet and Nutrition Survey (NDNS) and the National Health and Nutrition Examination Survey (NHANES). The analysis revealed that a significant proportion of dietary reports fell outside the predicted TEE limits. In NHANES, approximately 43.7% of adult dietary reports were within the predictive interval, while in NDNS, the figures were 36.6% for males and 38.1% for females. Children generally had a higher percentage of reports within the predicted range compared to adults. These findings suggest a high level of misreporting or undereating in both surveys.
• Effects of Age and BMI on Under-reporting: The study examined the relationship between the discrepancy in reported energy intake and predicted TEE as a function of age and Body Mass Index (BMI). In adults, the extent of under-reporting was largely independent of age, although a slight improvement was observed with age in the NDNS dataset. The discrepancy between reported intake and predicted expenditure was strongly negatively correlated with individual BMI. Individuals with higher BMI exhibited larger discrepancies, indicating greater under-reporting. This effect was more pronounced in children than in adults.
• Under-reporting and Macronutrient Intake: The researchers explored the relationship between the discrepancy in energy intake and the proportional macronutrient composition of the reported diet. They found a strong negative relationship between the reported percentage of energy from protein and the absolute size of the energy discrepancy. As the reported protein intake increased, the discrepancy became more negative, indicating greater under-reporting. Conversely, as the percentage of fat energy increased, the discrepancy became more positive. These findings suggest that individuals who under-reported their total energy intake also reported a greater percentage of protein and a reduced percentage of fat in their diets.

Strengths

• Clear Presentation of Predictive Equation

  The Results section clearly presents the derived predictive equation for TEE, including all significant terms and their coefficients, which enhances the transparency and reproducibility of the study.

  "In this analysis, we were able to derive a predictive equation with each coefficient reduced to four significant figures." (Page 59)
• Comprehensive Validation

  The researchers conducted a thorough validation of the predictive equation using an independent dataset, demonstrating its robustness and confirming that a high percentage of measurements fell within the predicted limits.

  "The validation dataset confirmed that 94.6% of independent TEE measurements were within these 95% predictive limits" (Page 60)
• Detailed Analysis of Survey Data

  The application of the predictive equation to two large dietary surveys (NDNS and NHANES) provides a detailed and insightful analysis of misreporting, including stratification by age, sex, and BMI.

  "For adults in NHANES, approximately 43.7% of dietary reports were within the predictive interval" (Page 60)

Suggestions for Improvement

• Expand on Machine Learning Model Comparisons

  This medium-impact improvement would provide a more comprehensive understanding of the methodological choices made in the study. The Results section needs this detail to fully justify the selection of the classical general linear regression model over the machine learning alternatives. Elaborating on the specific reasons why the machine learning models (Random Forest, XGBoost, and Support Vector Regression) did not outperform the classical regression would strengthen the paper by providing a clearer rationale for the chosen approach. This would also help readers appreciate the nuances of model selection in the context of predicting TEE and understand the limitations of each method. Ultimately, expanding on the machine learning model comparisons would improve the study's methodological rigor by ensuring transparency in the model selection process and providing a more complete picture of the analytical approach.

  "These machine learning models did not improve on the classical general linear regression modelling" (Page 59)

  Implementation: Include a paragraph that details the performance metrics of each machine learning model compared to the classical regression, such as R-squared values, mean absolute error, and any other relevant statistics. For example, "While the machine learning models showed comparable performance to the classical regression, with R-squared values of X for Random Forest, Y for XGBoost, and Z for Support Vector Regression, they did not offer significant improvements in predictive accuracy. This is likely due to the linear relationships between the predictors and TEE, which are adequately captured by the classical regression model."

• Clarify the Rationale for Using 95% Predictive Intervals

  This medium-impact improvement would enhance the reader's understanding of the statistical methods employed in the study. The Results section needs this clarification to justify the choice of using 95% predictive intervals (PI) over other potential metrics for assessing misreporting. Providing a more detailed explanation of why 95% PI were selected and how they offer advantages over previous methods, such as the Goldberg cut-off, would strengthen the paper by providing a stronger statistical foundation for the analysis. This would also help readers appreciate the novelty and robustness of the approach in identifying misreporting. Ultimately, clarifying the rationale for using 95% PI would improve the study's methodological rigor by ensuring transparency in the statistical methods and providing a clearer justification for their use.

  "Ninety-five per cent predictive intervals (95% PI) are the range of values that are 95% likely to contain the true value" (Page 60)

  Implementation: Add a few sentences that explain the advantages of using 95% PI, such as their ability to account for individual variability and provide a more accurate assessment of misreporting compared to fixed cut-off values. For example, "The 95% PI were chosen over traditional cut-off methods because they provide a statistically sound range that accounts for individual variability in TEE. Unlike fixed cut-offs, which can lead to misclassification, the 95% PI offer a more nuanced and accurate approach to identifying potential misreporting."

• Provide More Context on the Implications of the Findings for Specific Populations

  This high-impact improvement would significantly enhance the reader's understanding of the study's broader implications for different demographic groups. The Results section needs this context to effectively communicate the significance of the findings beyond the overall levels of misreporting. Briefly elaborating on how the observed patterns of misreporting vary across specific populations, such as children, adults, and individuals with different BMIs, and the potential consequences for nutritional research and public health in these groups would strengthen the paper by highlighting the practical relevance of the findings. This would also underscore the study's contribution to improving the accuracy of dietary assessment in diverse populations. Ultimately, providing more context on the implications of the findings for specific populations would significantly improve the study's impact by emphasizing the importance of tailored approaches to addressing misreporting and promoting accurate dietary data collection in different demographic groups.

  "The deficit between reported intake and predicted expenditure was strongly negatively correlated with individual BMI" (Page 61)

  Implementation: Include a few sentences or a paragraph that discusses the implications of the findings for specific populations, such as the higher prevalence of under-reporting among individuals with higher BMI and the potential impact on obesity research. For example, "The finding that under-reporting is more prevalent among individuals with higher BMI has important implications for studies investigating the relationship between diet and obesity. This highlights the need for targeted strategies to address misreporting in this population and improve the accuracy of dietary data in obesity research."

Non-Text Elements

Table 1 | Significant terms in the general linear model analysis (10 decimal...

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Table 1 | Significant terms in the general linear model analysis (10 decimal places) predicting TEE

Figure/Table Image (Page 3)

First Reference in Text

The derived significant predictors and their regression coeffi-cients are reported in Table 1.

Description

• Purpose of the table: This table shows the results of a statistical analysis called 'general linear model analysis'. You can think of this like drawing a line through a cloud of points on a graph to see if there's a relationship between different things. Here, they're looking at what factors might be related to something called 'Total Energy Expenditure' (TEE), which is the amount of energy (like calories) a person uses in a day.
• Content of the table: The table lists different factors that the analysis found to be important in predicting TEE. For each factor, it gives a 'coefficient,' which is like the slope of the line in our graph analogy – it tells us how much TEE is expected to change if that factor changes. It also shows the 'standard error' of the coefficient, which is a measure of how precise that estimate is. Additionally, it includes a 'T-value,' which helps determine if the factor is statistically significant, and a 'P-value,' which is the probability of seeing the observed relationship (or a stronger one) if there was actually no real relationship between the factor and TEE. A low P-value (typically less than 0.05) suggests that the relationship is statistically significant, meaning it's unlikely to have occurred by chance.
• Specific factors listed: The factors listed in the table are things like body weight, height, age, sex, elevation, and ethnicity. 'In[BW (kg)]' refers to the natural logarithm of body weight in kilograms. The natural logarithm is a mathematical function that helps to transform the data in a way that makes it more suitable for this type of analysis. Similarly, 'In[Elevation (m)]' is the natural logarithm of the elevation where the measurement was taken, in meters. 'Sex' is a categorical variable, likely coded as male or female. 'Ethnicity' is also a categorical variable, with categories like 'A' for African, 'AA' for African living outside Africa, 'AS' for Asian, 'W' for White, 'H' for Hispanic, and 'NA' for not available.
• Precision of the coefficients: The coefficients in the table are reported to 10 decimal places. This level of precision is unusual in many scientific fields, but the authors state later in the paper that they did this for reproducibility, so that others can get the exact same results if they use their model.

Scientific Validity

• Model Specification: The authors have included a comprehensive set of predictors in their model, including demographic, anthropometric, and environmental variables. The inclusion of interaction terms (e.g., Height x In[Elevation (m)]) is appropriate and suggests a thorough exploration of potential relationships. However, the rationale for including specific interaction terms could be more explicitly stated.
• Statistical Significance: The reported T-values and P-values allow for a clear assessment of the statistical significance of each predictor. The use of a general linear model is appropriate given the nature of the dependent variable (TEE) and the mix of continuous and categorical predictors.
• Precision of Coefficients: While reporting coefficients to 10 decimal places is unconventional, the authors justify this decision based on the need for precise replication of their predictive model. This level of precision, although unusual, does not inherently invalidate the scientific rigor of the analysis. It is crucial, however, that the authors provide a sensitivity analysis demonstrating the impact of this precision on the model's predictions.

Communication

• Clarity of Column Headers: The column headers are generally clear and provide sufficient information to understand the table's contents. However, 'SE coefficient' could be more explicitly labeled as 'Standard Error of Coefficient' for better clarity.
• Footnotes: The footnote defining the ethnicity abbreviations is helpful. However, providing a brief explanation of the coding for 'Sex' within the table or in a footnote would further enhance clarity.
• Caption Clarity: The caption is concise but could be slightly more descriptive. For example, it could be revised to: 'Table 1 | Significant terms and their coefficients from the general linear model analysis predicting Total Energy Expenditure (TEE), presented with 10 decimal places for reproducibility.'

Table 2 | Summary of observations inside and outside the tolerance limits in...

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Table 2 | Summary of observations inside and outside the tolerance limits in the NDNS and NHANES datasets

Figure/Table Image (Page 4)

First Reference in Text

Using the predictive equations developed above, the number and percentage of individuals that fell outside the predicted limits (both over and under) and within the predicted limits are shown in Table 2, stratified by data source, age (adults versus children) and sex.

Description

• Purpose of the table: This table summarizes how well the predictions from their equation match up with real-world data from two large dietary surveys, the NDNS (National Diet and Nutrition Survey) and NHANES (National Health and Nutrition Examination Survey). It's like checking if a weatherman's forecast (the equation's prediction) accurately reflects the actual weather (the survey data).
• Tolerance Limits: The 'tolerance limits' refer to the range of values within which the researchers expect the real-world data to fall, based on their equation. It's like setting a margin of error around the prediction. If the weatherman predicts a temperature of 20 degrees Celsius, he might say the actual temperature will likely be between 18 and 22 degrees. That range is the tolerance limit.
• Organization of the table: The table is organized by dividing the survey participants into groups based on which survey they were part of (NDNS or NHANES), whether they were adults or children, and their sex (male or female). For each group, the table shows how many people had reported dietary intakes that fell within the predicted range (inside the tolerance limits), below the predicted range (underestimated), and above the predicted range (overestimated). These counts are also shown as percentages of the total number of people in each group.
• Interpretation of the data: If the equation is a good predictor, most people's reported intakes should fall within the tolerance limits. If a large percentage of people fall outside the limits, it suggests that the equation may not be accurately reflecting real-world dietary intake, or that there's a lot of misreporting in the surveys. For example a high percentage of 'underestimated' would suggest that many people are reporting eating less than what the equation predicts they should need based on factors like their weight, height, and age.

Scientific Validity

• Appropriateness of Tolerance Limits: The scientific validity of this table hinges on the appropriateness of the tolerance limits used. The authors have previously described how these limits were derived (95% prediction intervals), which is a statistically sound approach. However, the validity of applying these limits to assess misreporting relies on the assumption that deviations from the predicted TEE primarily reflect misreporting rather than individual variability or other factors not captured by the model.
• Stratification by Data Source, Age, and Sex: Stratifying the results by data source, age, and sex is crucial for identifying potential biases or differences in the performance of the predictive equation across different populations. This allows for a more nuanced interpretation of the results and helps to pinpoint specific groups where misreporting may be more prevalent. The choice of these stratification variables is justified given their known associations with dietary intake and reporting behaviors.
• Use of Number and Percentage: Presenting both the number and percentage of individuals within each category is helpful for interpretation. Percentages provide a standardized way to compare across groups of different sizes, while raw numbers give a sense of the actual sample sizes involved.

Communication

• Clarity of Column Headers: The column headers are relatively clear but could be improved. 'Number underestimated' and 'Number overestimated' could be more explicitly defined as 'Number below the lower tolerance limit' and 'Number above the upper tolerance limit,' respectively. Similarly, 'Number within range' could be clarified as 'Number within tolerance limits.'
• Caption Clarity: The caption is generally clear and informative. It could be slightly improved by explicitly stating that the tolerance limits are based on the 95% prediction intervals of the predictive equation.
• Footnote: The footnote is helpful in explaining what the table shows. However, it could be made more informative by briefly mentioning the years the datasets cover, which is relevant context for interpreting the results.
• Overall Readability: The table is well-organized and relatively easy to read. The use of bold font for the main categories (e.g., Male children, Female children) enhances readability.

Fig. 1 | Misreporting in relation to age, BMI and sex. a, Comparison of the...

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Fig. 1 | Misreporting in relation to age, BMI and sex. a, Comparison of the difference between predicted TEE and self-reported energy intake (EI) in the NDNS (n = 12,694) and NHANES (n = 5,873) datasets in relation to age for children (≤16 yr) and adults (>16 yr). b, Comparison of the difference between predicted TEE and self-reported energy intake in the same datasets in relation to BMI for children (≤16 yr) and adults (>16 yr). Negative values show observations lower than prediction and positive values show prediction higher than observation.

Figure/Table Image (Page 5)

First Reference in Text

We plotted the difference between the survey estimate of daily energy intake and the predicted TEE as a function of age and body mass index (BMI) for both the NDNS and NHANES datasets (Fig. 1).

Description

• Overall Purpose: This figure is trying to show how well people's self-reported food intake matches up with what a scientific equation predicts they should be eating. The equation predicts Total Energy Expenditure (TEE), which is the number of calories a person burns in a day. The researchers are comparing this prediction to self-reported energy intake (EI), which is what people say they eat in dietary surveys. The difference between these two (predicted TEE - self-reported EI) is an indication of potential 'misreporting' - either under-reporting (eating less than they say) or over-reporting (eating more than they say).
• Structure of the Figure: The figure is divided into two parts, labeled 'a' and 'b'. Each part contains four graphs. Part 'a' looks at the relationship between misreporting and age, while part 'b' looks at the relationship between misreporting and Body Mass Index (BMI), which is a measure of body fat based on height and weight. Each graph is a scatter plot, with each dot representing a person in the study. The graphs are further split by the dataset used (NDNS or NHANES) and whether the participants were children or adults.
• X and Y Axes: In part 'a', the x-axis (horizontal) represents age in years, while in part 'b', it represents BMI. In both parts, the y-axis (vertical) represents the difference between predicted TEE and self-reported EI. A value of 0 on the y-axis means that the predicted TEE and self-reported EI are the same. Negative values mean that people are reporting eating less than the equation predicts (under-reporting), while positive values mean they are reporting eating more (over-reporting).
• Interpretation of the Data Points: Each dot on the scatter plots shows an individual's data. For example, in part 'a', a dot on the NDNS-Adults graph with an x-axis value of 40 and a y-axis value of -5 would represent a 40-year-old adult in the NDNS study who reported eating 5 megajoules (a unit of energy) less per day than the equation predicted. The red line on each graph is a trend line, which is like drawing a line through the middle of the dots to see the general pattern. If the trend line slopes downwards, it means that as age or BMI increases, the difference between predicted TEE and self-reported EI tends to become more negative (more under-reporting).

Scientific Validity

• Appropriateness of Visualization: Using scatter plots with trend lines is an appropriate way to visualize the relationship between continuous variables like age, BMI, and the difference between predicted and reported energy intake. This allows for a visual assessment of the magnitude and direction of misreporting across different age and BMI groups.
• Statistical Analysis: The reference text indicates that the authors plotted the difference between predicted and reported energy intake, but it doesn't specify the method used to generate the trend lines. It's crucial to know whether these are simple linear regressions or if a more sophisticated smoothing technique was employed. The choice of method can influence the interpretation of the trends.
• Sample Size: The large sample sizes from the NDNS and NHANES datasets provide robust data for this analysis. However, it's important to consider potential biases or limitations inherent in these datasets, such as the reliance on self-reported dietary intake.

Communication

• Clarity of Axes Labels: The axes labels are generally clear and informative. The y-axis label could be slightly improved by specifying the units (MJ d-1) for the difference between predicted TEE and self-reported EI.
• Legend: The figure lacks a legend to differentiate between the NDNS and NHANES datasets and between children and adults. Adding a legend would significantly improve the clarity and interpretability of the graphs.
• Caption: The caption is relatively clear but could be more concise. It could also benefit from explicitly stating that the red lines represent trend lines.
• Visual Clutter: The use of different colors and symbols for each dataset and age group, combined with the trend lines, creates some visual clutter. Using a more minimalist color scheme or separating the graphs for children and adults could improve readability.
• Trend Line Description: The caption should mention that the red lines are trend lines, and the method used to generate them should be specified in the methods section.

Table 3 | Relationships between the discrepancy of intake to expenditure and...

Full Caption

Table 3 | Relationships between the discrepancy of intake to expenditure and self-reported dietary macronutrient composition

Figure/Table Image (Page 5)

First Reference in Text

Next, we explored the relationship between the discrepancy in energy intake and the proportional macronutrient composition (percentage energy) of the reported diet (Table 3).

Description

• Purpose of the Table: This table explores whether the tendency for people to over- or under-report their food intake is related to the types of food they eat. Specifically, it looks at whether the difference between what people report eating and what an equation predicts they need (the 'discrepancy') is linked to the proportion of their diet that comes from carbohydrates, protein, and fat. These three are called 'macronutrients' and are the main components of food that provide energy.
• Structure of the Table: The table is divided into four sections, each representing a different set of data: the full NDNS dataset, the screened NDNS dataset, the full NHANES dataset, and the screened NHANES dataset. 'Screened' here likely refers to removing data points that were considered unreliable based on some criteria, like falling outside the tolerance limits mentioned in previous tables. Each section shows the results of a statistical analysis called 'multiple regression analysis'. This is a method used to examine the relationship between a dependent variable (in this case, the discrepancy between reported and predicted energy intake) and several independent variables (the percentage of energy from carbohydrates, protein, and fat).
• Key Terms Explained: 'Coefficient' in this context refers to the estimated change in the discrepancy (in kilojoules per day) for a one-unit change in the percentage of energy from each macronutrient. For example, a coefficient of -207.3 for 'Percentage protein' in the full NDNS dataset means that for every 1% increase in the proportion of protein in the diet, the discrepancy between reported and predicted intake is estimated to decrease by 207.3 kJ/day (meaning more under-reporting). 'SE coefficient' stands for the standard error of the coefficient, which is a measure of the precision of the estimate. 'P-value' is the probability of observing the relationship (or a stronger one) if there was actually no real relationship between the macronutrient and the discrepancy. A low P-value (typically less than 0.05) suggests that the relationship is statistically significant. 'R²' is a measure of how well the model fits the data, with higher values indicating a better fit.

Scientific Validity

• Appropriateness of Statistical Method: Multiple regression analysis is an appropriate method for examining the relationship between the discrepancy in energy intake and the proportional macronutrient composition of the diet. The use of both full and screened datasets allows for an assessment of the robustness of the findings.
• Interpretation of Coefficients: The interpretation of the coefficients is crucial. The authors should provide a more detailed discussion of the implications of the positive and negative coefficients for different macronutrients. For instance, they should discuss potential reasons why a higher reported protein intake is associated with a greater discrepancy (more under-reporting).
• Consideration of Confounding Factors: While the table presents the results of multiple regression, which adjusts for the other included macronutrients, there may be other confounding factors that influence both macronutrient composition and the discrepancy in energy intake. These potential confounders should be acknowledged and discussed.
• Comparison of Full and Screened Datasets: Comparing the results from the full and screened datasets is valuable for assessing the impact of removing potentially unreliable data points. The authors should provide a more detailed comparison of these results and discuss any notable differences.

Communication

• Clarity of Column Headers: The column headers are generally clear and informative. However, 'Term' could be more explicitly labeled as 'Predictor Variable' or 'Macronutrient.'
• Caption Clarity: The caption is concise but could be more descriptive. It could be revised to: 'Table 3 | Results of multiple regression analyses examining the relationships between the discrepancy of reported energy intake to predicted expenditure and the self-reported dietary macronutrient composition (percentage of total energy) in the NDNS and NHANES datasets, using both full and screened data.'
• Table Organization: The organization of the table into four sections is logical and facilitates comparison across datasets and data treatments (full vs. screened). However, the table is quite dense, and the use of bold font or shading could help to visually separate the different sections.
• Explanation of Screening: The table would benefit from a brief explanation of the screening criteria used to define the 'screened' datasets. This information could be included in a footnote or in the methods section.
• Units: It would be helpful to include the units for the coefficients (kJ/day per 1% change in macronutrient) in the column header or a footnote.