APOE Genotype and Insulin Modulate the Impact of Dietary Macronutrients on Cognitive Performance in Older Adults

Table of Contents

Overall Summary

Overview

This study investigates how an individual's genetic makeup (specifically, their APOE genotype) and insulin levels affect the relationship between what they eat and how well they think. Researchers used data from a large clinical trial of older adults at risk of dementia, focusing on those without diabetes. They looked at how different combinations of carbohydrates, fats, and proteins were linked to overall cognitive performance over three years, considering both long-term dietary patterns and short-term dietary changes. The study found that APOE genotype significantly influences this relationship, suggesting potential for personalized dietary advice to protect brain health.

Key Findings

Strengths

Areas for Improvement

Significant Elements

Figure 2

Description: Figure 2 visually represents the core findings of how APOE genotype influences the relationship between different dietary components (carbohydrates/fat ratio, protein, etc.) and global cognition. It uses forest plots to show the effect sizes and confidence intervals for each genotype, allowing for direct comparison and highlighting statistically significant interactions. This figure is crucial for understanding the main effects of APOE on diet-cognition relationships.

Relevance: This figure visually summarizes the main findings of the study, making it easier to understand the complex interactions between diet, APOE genotype, and cognition.

Figure 5

Description: Figure 5 illustrates the complex three-way interaction between diet, APOE genotype, and insulin levels on global cognition. It uses forest plots to show the effects of different dietary factors on cognition across various insulin and APOE strata. The overlapping insulin strata help visualize the dose-dependent relationships. This figure highlights the importance of considering all three factors together when making dietary recommendations.

Relevance: Figure 5 adds another layer of complexity by showing how insulin levels further modify the relationship between diet, APOE genotype, and cognition.

Conclusion

This study provides compelling evidence that our genetic makeup, specifically our APOE genotype, significantly influences how diet affects cognitive health in older adults. While a lower carbohydrate/fat ratio and higher protein intake appear generally beneficial, the optimal dietary approach likely varies based on individual APOE genotype and insulin status. Further research with larger, more diverse populations, including randomized controlled trials testing specific dietary interventions, is needed to confirm these findings and develop effective personalized nutrition strategies to promote healthy aging and prevent cognitive decline. Understanding the complex interplay between genetics, metabolism, and nutrition is crucial for developing targeted interventions to preserve brain health.

Section Analysis

Abstract

Overview

This study investigated how APOE genotype and fasting insulin modify the relationship between dietary macronutrients and cognitive performance in older adults at risk of dementia. Researchers used data from the FINGER trial, focusing on non-diabetic participants. They found that APOE genotype significantly influenced the relationship between carbohydrate/fat ratio and protein intake with cognitive performance. Specifically, individuals with higher values on an APOE gradient (reflecting a shift from ε-23 to ε-44) showed a more favorable association between lower carbohydrate/fat ratio and higher protein intake with better cognitive outcomes. Insulin levels also interacted with diet and APOE, with both high and low insulin levels associated with stronger effects of diet on cognition, primarily in APOE ε-34/44 carriers. The study suggests that APOE-based precision nutrition may be promising, but further research is needed.

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Overview

This introduction sets the stage for a study investigating the impact of diet on cognitive health in older adults. It highlights the existing evidence supporting the Mediterranean diet (MeDi) but points out the ambiguity regarding its carbohydrate and fat composition. The introduction emphasizes the need for further research on how macronutrient ratios, particularly the carbohydrate/fat ratio (CFr), influence cognitive health. It also introduces APOE genotype and insulin status as potential factors that could modify the relationship between diet and cognition, justifying the study's focus on these factors.

Key Aspects

Strengths

Suggestions for Improvement

Methods

Overview

This section details the methods used to investigate how APOE genotype and baseline insulin levels influence the relationship between diet and cognition in older adults at risk of dementia. The study uses a panel analysis design with data from the FINGER trial, including non-diabetic participants aged 60-77. Dietary intake was assessed using 3-day food records, and global cognition was measured using a modified Neuropsychological Test Battery. The analysis employed mixed regression models to examine the interactions between diet, APOE genotype, and insulin, considering both between- and within-subject effects. The study also explores the validity of using a continuous APOE gradient and adjusts for potential confounders like age, sex, education, BMI, and medication use.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

figure 1

Figure 1 provides a visual representation of the study design and the statistical analyses employed. Panel A illustrates the structure of the panel data, showing measurements of independent variable 'X' (diet) and dependent variable 'Y' (cognition) at three time points (0, 1, and 2) for each participant. This panel sets the stage for understanding the repeated measures nature of the study. Panel B uses hypothetical data points for two subjects, 'A' and 'B', over the three years to explain the concepts of 'within-effect' and 'between-effect'. The within-effect (red, solid line) represents the change in 'Y' over time for each individual relative to their own average. The between-effect (green, striped line) represents the difference in average 'Y' between individuals. Panel C presents intra-class correlation coefficients (ICCs) for various variables in the study. ICCs measure the proportion of total variation in a variable that is due to differences between individuals. Higher ICCs indicate greater between-subject variability. Diet variables are highlighted in bold, indicating that they have relatively higher within-subject variability compared to other variables. Each dotted line in Panel C represents data from a single time point, and the color-coding corresponds to the information used in different statistical models.

First Mention

Text: "Because this approach appears uncommon in nutritional epidemiology (probably because many studies either use a single measure of diet and/or an irreversible disease outcome), we clarify our analytical approach extensively in relation to descriptions of the statistical methods below. Hypothesized effect modifiers (APOE-genotype and baseline insulin) of the impact of dietary parameters on global cognition were investigated."

Context: The authors are discussing the study design and statistical analyses, emphasizing the importance of clarifying their approach due to its uncommon nature in nutritional epidemiology.

Relevance: This figure is crucial for understanding the study's methodology. It clarifies the panel data structure, explains the within- and between-subject effects that are central to the analysis, and provides insight into the variability of different variables using ICCs. This information is essential for interpreting the study's results.

Critique
Visual Aspects
  • In Panel B, the use of hypothetical data, while illustrative, could be made more realistic by adding some random variation around the trend lines. This would better reflect real-world data and prevent the misconception that within-subject changes are perfectly linear.
  • The description of the dotted lines in Panel C is unclear. It would be helpful to explicitly label or connect these lines to the corresponding time points in Panel A. This would improve the clarity and connection between the panels.
  • The color-coding in Panel C, while helpful, could be improved by using a colorblind-friendly palette to ensure accessibility for all readers.
Analytical Aspects
  • While the figure explains within- and between-subject effects, it doesn't visually represent the 'mixed model' which is mentioned in the text as the primary analysis. Adding a visual representation of the mixed model would enhance the figure's comprehensiveness.
  • The figure could benefit from a brief explanation of how ICCs are interpreted. For example, stating that higher ICC values indicate greater between-subject variability would make the figure more self-explanatory.
  • The figure could be improved by adding a brief explanation of why diet variables having higher within-subject variability is relevant to the study's aims. This would connect the figure more directly to the research question.
Numeric Data
  • Sample size for ICC calculation: 1251

Results

Overview

This section presents the findings regarding the influence of APOE genotype and insulin on the relationship between diet and global cognition. The results show that a continuous APOE gradient (from ε-23 to ε-44) modifies the association between diet and cognition, with a lower carbohydrate/fat ratio and higher protein intake being more beneficial for those with higher APOE gradient values. A significant interaction was also found between the composite diet score (higher carbohydrates and fiber, lower fat and protein) and the APOE gradient. Furthermore, insulin levels were found to modulate the association between diet and cognition, particularly in individuals with APOE ε-34/44. The results suggest that APOE genotype and insulin levels play a crucial role in how diet affects cognitive performance.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

table 1

Table 1 presents the baseline characteristics of the participants included in this specific study (a subset of the FINGER trial) and compares them to the full FINGER sample. This comparison aims to demonstrate that the subsample used in this study is representative of the larger FINGER cohort. The table includes various demographic, health, and lifestyle characteristics, such as age, sex, education level, global cognition scores, intervention/control group assignment, dropout rates, diabetes prevalence, cholesterol-lowering drug use, APOE genotype frequencies, BMI, blood pressure, insulin levels and related measures (HOMA-IR, HOMA-beta), HbA1c, fasting glucose, lipid profile (total cholesterol, HDL, LDL, triglycerides, apolipoprotein B), and dietary intake (total energy, carbohydrates, fat, protein, fiber, and saturated fat ratio). The data are presented as means with standard deviations, medians with interquartile ranges (or a broader range of percentiles), or percentages with counts, depending on the type of variable. A footnote clarifies the presentation of data and explains that insulin analysis was not performed for individuals who had already dropped out of the study at the time of the insulin measurements.

First Mention

Text: "Baseline characteristics of the participants are reported in Table 1, including a comparison with the full FINGER sample, indicating negligible differences."

Context: This sentence introduces Table 1 and its purpose within the Results section.

Relevance: This table is essential for understanding the characteristics of the study population and assessing the generalizability of the findings. It allows readers to compare the subsample used in this analysis to the full FINGER sample, ensuring that the subsample is representative and that any observed associations are not due to differences in baseline characteristics.

Critique
Visual Aspects
  • The table is dense and could be improved by using visual cues like shading or bolding to highlight key comparisons or differences between the subsample and the full sample. This would make it easier for readers to quickly grasp the main points.
  • Consider presenting the APOE genotype frequencies as percentages in addition to the raw counts. This would facilitate easier comparison between the two samples.
  • The table could be made more visually appealing by using more white space and adjusting the font size and spacing.
Analytical Aspects
  • While the table provides descriptive statistics, it doesn't include any statistical tests to formally compare the subsample and full sample. Adding p-values for these comparisons would strengthen the claim of negligible differences.
  • The table could benefit from a brief explanation of why certain variables were log-transformed. This would enhance the transparency of the data analysis.
  • Consider adding a column indicating the clinical significance of any observed differences between the subsample and the full sample. This would help readers understand the practical implications of these differences.
Numeric Data
  • Subsample size: 676
  • Full sample size: 1251
figure 2

Figure 2 explores the relationship between various dietary parameters and global cognition, stratified by APOE genotype. The figure is organized into five rows, each representing a different dietary parameter: carbohydrate/fat ratio (CFr), protein, fiber, saturated/total fat ratio (SAFr), and a composite score (a combination of CFr, fiber, and protein). Each row has three columns. Column A presents forest plots showing the effect of each dietary parameter on global cognition for different APOE genotypes (ε-23, ε-24, ε-33, ε-34, and ε-44). The forest plots display point estimates (represented by squares) and 95% confidence intervals (represented by horizontal lines) for each genotype. Column B shows p-values for interactions between the APOE strata, indicating whether the effect of diet on cognition differs significantly between genotypes. P-values less than 0.10 are displayed. Column C investigates the validity of treating APOE as a continuous gradient (rather than distinct categories) in its interaction with diet. It presents forest plots showing the interaction between the dietary parameter and a continuous APOE gradient, with sensitivity analyses performed by excluding each APOE stratum one at a time. This helps determine if the effect of APOE on the diet-cognition relationship is linear or if specific genotypes have unique effects. The figure caption provides details about the coding of the APOE gradient, the statistical model used (mixed model), and the covariates included in the model.

First Mention

Text: "For each diet variable, analyses are reported in 3 columns in Figure 2."

Context: This sentence introduces Figure 2 and its structure within the Results section.

Relevance: This figure is central to the study's main findings, demonstrating how APOE genotype modifies the relationship between diet and cognition. It visually presents the effects of different dietary parameters on cognition across various APOE genotypes, allowing for direct comparison and identification of potential interactions. The figure also explores the validity of using a continuous APOE gradient, which is a key methodological aspect of the study.

Critique
Visual Aspects
  • The figure is quite complex and dense, making it challenging to interpret at a glance. Consider splitting the figure into separate panels for each dietary parameter or using a different visual representation (e.g., line graphs) to improve clarity.
  • The use of different symbols or colors for males and females in the ε-44 stratum would improve readability and highlight the sex-specific effects.
  • The forest plots in Column C could be simplified by only showing the overall interaction effect with the continuous APOE gradient and omitting the sensitivity analyses. The sensitivity analyses could be presented in a supplementary figure.
Analytical Aspects
  • While the figure shows p-values for interactions, it doesn't provide the actual interaction effect sizes (beta coefficients). Including these values would provide more information about the magnitude and direction of the interactions.
  • The figure caption could be more explicit about the interpretation of the sensitivity analyses in Column C. Explain what it means for the APOE gradient to be 'valid' and how this is determined from the sensitivity analyses.
  • The figure could benefit from a brief explanation of why a mixed model was chosen for the analysis and the implications of using this model for interpreting the results.
Numeric Data
  • Sample size: 676
figure 4

This figure is a forest plot designed as a graphical sensitivity analysis to assess the coherence between the within- and between-subject effects on global cognition. It displays the estimated effects of different dietary components (CFr, Protein, Fiber, SAFr, and a composite score 'Comp') on various cognitive outcomes (global cognition, memory, executive function, and processing speed) across an APOE gradient. Each data point in the plot represents the interaction between a specific diet component and the APOE gradient, with the horizontal lines indicating the 95% confidence intervals. Different symbols (circle, triangle, square, diamond) and line styles (solid, dashed, dotted, dash-dotted) distinguish the different cognitive outcomes. The plot aims to visually demonstrate whether the slopes of the mixed model (which combines within- and between-subject effects) are more influenced by the within-subject estimates.

First Mention

Text: "Estimated within-subject effects of diet on global cognition by APOE The estimated effect on global cognition for some suppositious diet changes (clarified below) is shown in Table 2. The aim was to give an intuitive interpretation of estimated effect sizes, regardless of whether confidence intervals overlap 0. Estimates from the fixed-effects model [illustrated in Figure 4 for the composite score (Comp) and in Supplementary Figure 4 for all diet variables] were multiplied with a scaling factor achieved by the SD (for protein) or from crude regression on macronutrient concentrations from the variables CFr, and Comp respectively."

Context: This figure is introduced in the context of explaining the estimated within-subject effects of diet on global cognition, stratified by APOE genotype. It's mentioned alongside Table 2, which presents the numerical estimates, and Supplementary Figure 4, which shows similar plots for all diet variables.

Relevance: This figure is relevant because it visually represents the sensitivity analysis comparing the within- and between-subject effects of diet on cognition across the APOE gradient. This helps to understand the relative contribution of within-subject variability to the overall effects observed in the mixed model, providing a more nuanced understanding of the relationship between diet, APOE, and cognition.

Critique
Visual Aspects
  • The figure could be improved by adding a clear title that concisely describes the purpose of the sensitivity analysis. For example, 'Sensitivity Analysis: Comparison of Within- and Between-Subject Effects of Diet on Cognition across APOE Gradient'.
  • The x-axis label could be made more informative by specifying the units of the interaction term (e.g., 'Interaction between Diet Component and APOE Gradient (z-score units)').
  • Adding a brief explanation within the figure or caption about the interpretation of the different line types (solid, dashed, dotted, dash-dotted) would improve clarity for readers unfamiliar with forest plots.
Analytical Aspects
  • The figure caption mentions that it's intended as a sensitivity analysis, but it doesn't explicitly state what aspect of the analysis it's sensitive to. Clarifying this would strengthen the interpretation of the results.
  • The caption states that the slopes of the mixed model appear to be more influenced by the within-estimates, but it doesn't provide any quantitative measure of this influence. Adding a measure like the proportion of variance explained by within-subject effects would be helpful.
  • The figure focuses on the interaction between diet and APOE, but it doesn't show the main effects of diet or APOE. Including these main effects in the plot or a supplementary figure would provide a more complete picture of the relationships being studied.
Numeric Data
figure 3

Figure 3 is a forest plot illustrating the interaction between diet and APOE genotype on cognitive subdomains (memory, executive function, and processing speed). The x-axis represents the continuous APOE gradient, ranging from -1 (ε-23) to 2 (ε-44). The y-axis lists five diet variables: carbohydrate/fat ratio (CFr), protein, fiber, saturated/total fat ratio (SAFr), and a composite score (Comp). Each diet variable has separate data points for each cognitive subdomain, represented by different symbols and line styles. The horizontal lines extending from each data point represent the 95% confidence intervals. Striped or hollow symbols indicate that the results for that specific data point are primarily driven by the ε-44 genotype, suggesting a non-linear relationship or a strong influence of this genotype. The figure aims to show how the effect of diet on cognitive outcomes varies across the APOE gradient and within different cognitive domains.

First Mention

Text: "A sensitivity analysis on cognitive subdomains (Figure 3) indicated that memory, more than executive function and processing speed, was driving the observed diet x APOE interactions for global cognition."

Context: The figure is first mentioned in the context of a sensitivity analysis examining the role of cognitive subdomains in the observed interactions between diet and APOE genotype on global cognition. The text highlights that memory appears to be the primary driver of these interactions.

Relevance: Figure 3 is relevant because it provides a more detailed understanding of the interaction between diet, APOE genotype, and cognition by examining specific cognitive subdomains. This helps to pinpoint which cognitive domains are most susceptible to the influence of diet and APOE genotype, providing insights into the potential mechanisms underlying these interactions.

Critique
Visual Aspects
  • The figure could benefit from a more descriptive title, such as 'Interaction between Diet and APOE Genotype on Cognitive Subdomains'.
  • The y-axis labels could be made more concise by using abbreviations for the diet variables (e.g., CFr instead of carbohydrate/fat ratio) and providing a legend explaining these abbreviations.
  • The use of different symbols and line styles for different cognitive outcomes is helpful, but the visual distinction between them could be enhanced by using more distinct shapes or colors, especially for colorblind readers.
Analytical Aspects
  • The figure caption could be improved by providing more detail about the calculation of the composite score (Comp).
  • The caption mentions that striped/hollow symbols indicate results driven by ε-44, but it doesn't explain the criteria for this designation. Quantifying the contribution of ε-44 to the effect would be helpful.
  • The figure presents the interaction effects, but it doesn't show the main effects of diet or APOE on cognitive subdomains. Including these main effects or providing them in a supplementary figure would provide a more complete picture of the relationships.
Numeric Data
table 2

Table 2 presents estimated within-subject effects on global cognition resulting from hypothetical dietary changes, stratified by APOE genotype. It examines three dietary shifts: a 10 E% exchange between carbohydrates and fat, a 5 E% decrease in protein, and a shift from a hypothetical 'Diet A' to 'Diet B'. Diet A is characterized by lower carbohydrates (35 E%), higher fat (41 E%), higher protein (21 E%), and lower fiber (1.4 E%). Diet B, conversely, has higher carbohydrates (58 E%), lower fat (25 E%), lower protein (13 E%), and higher fiber (3.1 E%). The table provides point estimates and 95% confidence intervals (CIs) for the effect of each dietary change on global cognition (measured as a z-score) for each APOE genotype (ε-23, ε-24, ε-33, ε-34, and ε-44). Separate estimates are provided for ε-44 males and females due to a significant interaction effect for the carbohydrate/fat exchange. The table demonstrates how the impact of these dietary changes on cognition varies depending on an individual's APOE genotype.

First Mention

Text: "The estimated effect on global cognition for some suppositious diet changes (clarified below) is shown in Table 2."

Context: The authors are introducing a table that illustrates the estimated effects of hypothetical diet changes on global cognition.

Relevance: Table 2 is highly relevant as it provides a quantifiable estimate of how different dietary changes might impact cognitive function depending on an individual's APOE genotype. This information is central to the study's goal of exploring personalized nutrition strategies for cognitive health.

Critique
Visual Aspects
  • The table could be visually enhanced by using color-coding or shading to highlight significant effects or patterns across APOE genotypes. This would improve readability and make it easier to identify key findings.
  • The presentation of the suppositious diets A and B could be improved by adding a visual representation, such as a pie chart or bar graph, showing the macronutrient breakdown. This would make the dietary differences more intuitive.
  • The table could benefit from clearer labeling of the confidence intervals. Indicating that the values in parentheses represent 95% CIs would enhance clarity.
Analytical Aspects
  • The table focuses on hypothetical dietary changes, which may not fully reflect real-world dietary patterns. While useful for illustrating the potential impact of macronutrient shifts, the results should be interpreted with caution.
  • The table doesn't provide information on the statistical significance of the differences between APOE genotypes for each dietary shift. Adding p-values for these comparisons would enhance the table's analytical value.
  • The table could be strengthened by discussing the limitations of using within-subject estimates to predict the effects of long-term dietary changes. Acknowledging the potential for different effects over longer periods would improve the interpretation of the results.
Numeric Data
  • Carbohydrate intake in Diet A: 35 E%
  • Fat intake in Diet A: 41 E%
  • Protein intake in Diet A: 21 E%
  • Fiber intake in Diet A: 1.4 E%
  • Carbohydrate intake in Diet B: 58 E%
  • Fat intake in Diet B: 25 E%
  • Protein intake in Diet B: 13 E%
  • Fiber intake in Diet B: 3.1 E%
figure 4

Figure 4 is a forest plot visualizing the estimated within-subject effect of a composite diet score on cognition, stratified by APOE genotype. The composite diet score is a z-score derived from the carbohydrate/fat ratio, fiber, and inversely, protein. The plot displays point estimates and 95% confidence intervals (CIs) for both global cognition and memory, represented by circles and triangles, respectively. Each APOE genotype (ε-23, ε-24, ε-33, ε-34, ε-44) has its own set of data points, allowing for direct comparison of the diet-cognition relationship across genotypes. The x-axis represents the effect size (standardized beta coefficient), and the y-axis lists the APOE genotypes. The plot allows for visual assessment of the direction and magnitude of the association between the composite diet score and cognitive outcomes for each APOE genotype. A negative effect size indicates that a higher composite diet score (more carbohydrates and fiber, less fat and protein) is associated with lower cognitive performance, while a positive effect size indicates the opposite.

First Mention

Text: "Estimated within-subject effects of diet on global cognition by APOE The estimated effect on global cognition for some suppositious diet changes (clarified below) is shown in Table 2. The aim was to give an intuitive interpretation of estimated effect sizes, regardless of whether confidence intervals overlap 0. Estimates from the fixed-effects model [illustrated in Figure 4 for the composite score (Comp) and in Supplementary Figure 4 for all diet variables] were multiplied with a scaling factor achieved by the SD (for protein) or from crude regression on macronutrient concentrations from the variables CFr, and Comp respectively."

Context: The authors are discussing the within-subject effects of diet on global cognition, referring to Figure 4 as an illustration of these effects using a composite diet score.

Relevance: Figure 4 is highly relevant because it visually represents the core findings of the study regarding the interaction between diet, as represented by the composite score, and APOE genotype on cognitive outcomes. It provides a clear and concise way to compare the direction and magnitude of the diet-cognition relationship across different APOE genotypes.

Critique
Visual Aspects
  • The figure could benefit from clearer labeling of the x-axis. While 'composite diet score' is mentioned, specifying the units (z-score) and the direction of the score (higher score = more carbohydrates/fiber, less fat/protein) would improve interpretability.
  • Adding a vertical line at zero on the x-axis would visually emphasize the point of no effect and make it easier to identify statistically significant associations.
  • The use of different symbols for global cognition and memory is helpful, but the symbols could be made larger and more visually distinct to improve readability.
Analytical Aspects
  • The figure focuses on a composite diet score, which may obscure the individual contributions of different dietary components. While the composite score captures a general dietary pattern, exploring the individual effects of carbohydrates, fat, protein, and fiber would provide a more nuanced understanding.
  • The figure only presents within-subject effects. While this helps to control for confounding, it doesn't capture the potential long-term effects of diet on cognition. Adding a separate figure or panel showing between-subject effects would provide a more complete picture.
  • The figure could be strengthened by including a brief explanation of the clinical significance of the observed effect sizes. For example, relating the z-score changes to meaningful differences in cognitive performance would enhance the interpretation of the findings.
Numeric Data
  • Sample size: 676
  • Sample size for ε-23: 61
  • Sample size for ε-24: 17
  • Sample size for ε-33: 391
  • Sample size for ε-34: 183
  • Sample size for ε-44: 24
FIGURE 5

Figure 5 visually explores the three-way interaction between diet, APOE genotype (dichotomized as APOE+ (ε-34/44) and APOE- (ε-23/24/33)), and insulin levels on global cognition. It uses forest plots to display the estimated effects of different dietary components (Carbohydrate/Fat ratio, Protein, Fiber, and a Composite Score) on cognition across varying insulin strata. Each forest plot presents point estimates (represented by squares) and 95% confidence intervals (horizontal lines extending from the squares) for the effect of the diet component on global cognition. The plots are stratified by insulin levels, with each stratum divided into three categories: 1) APOE+ individuals within that insulin stratum, 2) APOE- individuals within that insulin stratum, and 3) all other individuals (regardless of APOE) from different insulin strata. This design allows for comparison of the diet-cognition relationship between APOE+ and APOE- individuals within the same insulin stratum, while also showing how this relationship changes across different insulin levels. The overlapping insulin strata are used to illustrate dose-dependent effects, meaning how the effects of diet on cognition change with increasing or decreasing insulin levels. The figure aims to visually assess whether the interaction between diet and APOE on cognition is influenced by insulin levels and whether this influence varies across the range of insulin concentrations.

First Mention

Text: "When we graphically explored interactions diet x APOE by various insulin quantiles for CFr, protein, fiber, and Comp (Figure 5), those analyses indicated that insulin modulated the slope between diet and cognition in a dose-dependent manner, but almost exclusively in the APOE+ stratum."

Context: The authors are discussing the interaction between diet, APOE genotype, and insulin levels on global cognition, and they refer to Figure 5 as a graphical exploration of these interactions.

Relevance: Figure 5 is highly relevant as it directly addresses a core research question: whether insulin modifies the relationship between diet and cognition differently in APOE+ and APOE- individuals. The visual representation of the three-way interaction allows for a more intuitive understanding of the complex interplay between these factors.

Critique
Visual Aspects
  • The use of multiple overlapping insulin strata, while intended to show dose-dependence, can make the plots visually cluttered and difficult to interpret. Simplifying the presentation, perhaps by focusing on fewer, non-overlapping strata or using a different visualization method, could improve clarity.
  • The three categories within each insulin stratum (APOE+, APOE-, and all others) are not visually distinct enough. Using different colors or symbols for these categories would make it easier to compare the effects within and between categories.
  • The figure caption could be more concise and focused on the key message. It currently contains a lot of methodological detail that could be moved to the methods section.
Analytical Aspects
  • The figure primarily focuses on visual exploration and doesn't provide any statistical tests for the three-way interaction. Including p-values for the interaction terms would strengthen the analysis.
  • The inclusion of 'all other strata' as a third category in each plot can be confusing and may not add much value to the analysis. Focusing on the comparison between APOE+ and APOE- within each stratum would simplify the interpretation.
  • The figure doesn't clearly show the direction of the interaction. Adding annotations or highlighting specific trends in the plots would make it easier to understand how insulin modifies the diet-APOE interaction.
Numeric Data
TABLE 3

Table 3 presents the associations between various dietary parameters and global cognition, stratified by APOE genotype (APOE+ (ε-34/44) vs. APOE- (ε-23/24/33)). It investigates the effects of four diet parameters: Carbohydrate/Fat ratio (CFr), Protein, Fiber, and a Composite diet score (calculated as the mean of z-scores of CFr, Fiber, and inversely Protein). The table shows the results for two different statistical models (Model A and Model B). Model A adjusts for a comprehensive set of confounders, including age, sex, education, time, time * randomization group, APOE, time * APOE, total energy intake, BMI, and cholesterol-lowering drugs. Model B excludes total energy intake, BMI, and cholesterol-lowering drug use as covariates. For each diet parameter and APOE group, the table presents the estimated effect size (beta coefficient) and the associated p-value. It also shows the interaction effect (beta coefficient and p-value) between each diet parameter and APOE genotype. The purpose of the table is to compare the associations between diet and cognition in APOE+ and APOE- individuals and to assess whether these associations differ significantly between the two groups. The inclusion of two models allows for evaluating the sensitivity of the results to the inclusion or exclusion of certain covariates.

First Mention

Text: "Associations between diet and global cognition stratified on APOE+/-, regardless of insulin, are reported in Table 3 as an exploratory complement to Figure 2."

Context: The authors are discussing the relationship between diet and cognition based on APOE genotype, and they refer to Table 3 as an exploratory analysis that complements the findings presented in Figure 2.

Relevance: Table 3 is relevant because it provides a direct comparison of the associations between diet and cognition in APOE+ and APOE- individuals. This comparison is central to understanding the role of APOE genotype in modifying the effects of diet on cognitive performance. The sensitivity analysis with two different models adds to the robustness of the findings.

Critique
Visual Aspects
  • The table could be visually enhanced by using bold font or highlighting to emphasize the key findings, such as significant interaction effects.
  • The table could be made more concise by presenting only the results from the primary model (Model A) in the main table and including the results from the sensitivity analysis (Model B) in a supplementary table.
  • Adding a brief explanation of the meaning of the beta coefficients and p-values in the table caption would make the table more accessible to a wider audience.
Analytical Aspects
  • While the table shows the interaction effects, it doesn't provide any information on the direction or magnitude of these interactions. Including a brief interpretation of the interaction terms would enhance the understanding of the findings.
  • The rationale for excluding total energy intake, BMI, and cholesterol-lowering drugs in Model B could be explained more clearly. This would strengthen the justification for the sensitivity analysis.
  • The table could be improved by including effect sizes (e.g., standardized mean differences) to quantify the magnitude of the differences in the diet-cognition associations between APOE+ and APOE- individuals.
Numeric Data

Discussion

Overview

This discussion section summarizes the study's key findings regarding the influence of APOE genotype and insulin on the relationship between diet and cognitive performance in older adults at risk of dementia. The study found that APOE genotype significantly modulated the association between various dietary parameters and cognition. Individuals with higher APOE gradient values (indicating a shift from ε-23 to ε-44) generally benefited more from a lower carbohydrate/fat ratio and higher protein intake. The APOE ε-33 genotype showed minimal association between diet and cognition, suggesting metabolic flexibility. Insulin levels also played a role, primarily influencing the diet-cognition relationship in APOE ε-34/44 carriers. The discussion also addresses limitations of the study, such as the exploratory nature of the data and potential biases, and highlights the strengths, including the use of within-subject effects and dose-dependent demonstrations of effect modification. The authors suggest further research is needed to validate these findings and explore their implications for precision nutrition.

Key Aspects

Strengths

Suggestions for Improvement

Conclusions

Overview

This section summarizes the study's main findings, emphasizing that APOE genotype influences the relationship between dietary macronutrients and cognitive performance. It highlights the potential of APOE-based precision nutrition but acknowledges the need for replication in larger, more diverse samples and clinical trials. The conclusion also suggests that both high and low insulin levels can modify the effects of diet on cognition.

Key Aspects

Strengths

Suggestions for Improvement

Appendix A. Supplementary data

Overview

This appendix likely contains supplementary figures and data that provide additional details and support for the findings presented in the main paper. These supplementary materials may include more detailed information on the statistical analyses, additional figures illustrating the results, or further breakdowns of the data by subgroups. This information is intended to enhance the reader's understanding of the study and allow for a more in-depth analysis of the findings.

Key Aspects

Strengths

Suggestions for Improvement

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