Longitudinal associations between fruit and vegetable intakes and depressive symptoms in middle-aged and older adults from four international twin cohorts

Table of Contents

Overall Summary

Overview

This longitudinal study of 3483 twins (aged 45-90) from four HICs found a modest association between higher fruit and vegetable intake and lower depressive symptoms over 5-11 years. Using linear mixed-effects models and controlling for confounders, high fruit intake was associated with a 0.29-point reduction, and high vegetable intake with a 0.26-point reduction in depressive symptoms (based on a median score of 18) compared to low intakes. The ICE FALCON method suggested no familial confounding for vegetables, but results for fruit were inconclusive. Sensitivity analyses revealed that including potatoes attenuated the association for vegetables, and excluding one cohort altered the significance. These findings suggest a potential, though small, benefit of fruit and vegetable consumption for depressive symptoms, but causal claims are limited by the observational design and self-reported dietary data.

Key Points

Strength: Twin Study Design (written-content)
The study leverages a longitudinal design with twin cohorts, allowing for control of genetic and shared environmental factors, a significant strength in addressing potential confounding.
Section: Methods
Limitation: Self-reported Dietary Data (written-content)
The reliance on self-reported FFQs for dietary assessment introduces potential recall bias and measurement error, limiting the accuracy of intake estimations.
Section: Methods
Strength: Statistical Rigor (written-content)
The statistical analyses, including linear mixed-effects models and ICE FALCON, are appropriate for longitudinal twin data and address familial confounding, enhancing the study's rigor.
Section: Results
Limitation: Modest Effect Sizes (written-content)
The modest effect sizes observed raise questions about the clinical significance of the findings. The small reductions in depressive symptoms may not translate into meaningful real-world improvements for individuals.
Section: Discussion
Limitation: HIC-centric Sample (written-content)
The study's focus on HIC populations limits the generalizability of the findings to other socioeconomic and cultural contexts.
Section: Conclusion
Limitation: Potential Residual Confounding (written-content)
While the study controls for several confounders, residual confounding due to unmeasured factors remains a possibility, limiting causal interpretations.
Section: Discussion
Contextual Placement (written-content)
The study contributes to the existing literature on diet and mental health, but the findings should be interpreted cautiously due to methodological limitations and the correlational nature of the data.
Section: Discussion
Real-world Implications (written-content)
The study's findings, while not definitive, can inform public health recommendations and encourage further research on the role of diet in mental well-being.
Section: Conclusion

Conclusion

This longitudinal twin study suggests a modest association between higher fruit and vegetable intake and lower depressive symptoms over time in middle-aged and older adults from high-income countries (HICs). While the twin design and statistical adjustments for confounders strengthen the study, the reliance on self-reported dietary data and the modest effect sizes limit causal interpretations and practical utility. The observed associations may be influenced by residual confounding or other unmeasured factors. While the findings contribute to the growing body of evidence suggesting a link between diet and mental health, they do not definitively establish a causal relationship. Future research with more robust dietary assessments, clinical depression measures, and diverse populations, including those from LMICs, is needed to strengthen causal inference and explore the clinical significance of these findings. The practical implications for practitioners remain limited, though the study supports the general recommendation of promoting fruit and vegetable consumption as part of a healthy lifestyle.

Section Analysis

Abstract

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Key Aspects

Strengths

Suggestions for Improvement

Methods

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Table 1. Details of contributing cohorts. *n=number of individuals with dietary...
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Table 1. Details of contributing cohorts. *n=number of individuals with dietary and depressive symptom data at baseline and at least one depressive symptom follow-up assessment; **n=number of individuals with dietary and depressive symptom data at baseline and at least one depressive symptom follow-up assessment and having a co-twin who also provided this data.

First Reference in Text
A summary of cohorts and follow-up periods is contained in Table 1.
Description
  • Overview: The table summarizes key information about the four international twin cohorts contributing data to the study. These cohorts are the Middle Age Danish Twins Study (MADT), the Minnesota Twin Study of Adult Development and Aging (MTSADA), the Older Australian Twins Study (OATS), and the Swedish Adoption/Twin Study of Aging (SATSA). For each cohort, it provides the location, the baseline year of the study, the length of the follow-up period for assessing depression, the number of follow-up assessment waves, and two sample sizes (n and **n). The value of 'n' represents the number of individuals with both dietary and depressive symptom data at the start of the study (baseline) and at least one follow-up assessment of depressive symptoms. The value of '**n' further restricts this count to individuals whose co-twin also has this data available.
  • Organization: The table organizes information by cohort, presenting it in a row-wise fashion. This allows easy comparison across cohorts. The columns provide specific details about each study's design and data collection, including the location, baseline year, the total follow-up period for assessing depression, and the number of follow-up assessments.
Scientific Validity
  • Justification of Sample Sizes: Providing two sample sizes ('n' and '**n') is essential for transparency and facilitates accurate interpretation of the study findings. 'n' represents all participants with relevant baseline and follow-up data, while '**n' represents the subset used in specific analyses that require data from both twins in a pair. This clear distinction strengthens the methodological rigor of the study, particularly in analyses involving twin comparisons or methods requiring information from both siblings.
  • Cohort Information: The table clearly identifies the cohorts used, their respective locations, baseline years, and follow-up periods. This information is crucial for understanding the context of the data and for assessing the generalizability of the findings. The variety in geographical location strengthens the study's external validity.
Communication
  • Clarity and Structure: The table is clearly structured and easy to read. The use of abbreviations is helpful, especially for long study names, but defining them explicitly beneath the table enhances clarity and accessibility for readers unfamiliar with these studies. Explaining both *n and **n is crucial for interpreting the sample sizes used in different analyses.
  • Informativeness: The caption clearly articulates the key information presented in the table: the contributing cohorts, their locations, baseline years, follow-up periods, and, importantly, the two different sample sizes used in the analyses. This allows readers to quickly grasp the context and scope of the data presented.
Supplementary Tables S1-2. Details of assessment methods.
First Reference in Text
Details of assessment methods are contained in Supplementary Tables S1-2.
Description
  • Overview of assessment details: These supplementary tables provide detailed information about how fruit and vegetable intake and depressive symptoms were measured in each of the four studies. Imagine each study using its own tools to measure these things. The tables are like instruction manuals for those tools, explaining how the measurements were taken in each study. For dietary intake, this might include details about the type of questionnaire used (e.g., food frequency questionnaire), the specific foods included, and the timeframe considered (e.g., usual intake over the past year). For depressive symptoms, the tables describe the assessment tool, like the CES-D scale where participants rate how often they felt certain emotions, the scoring system, and any cut-off points used to define depression.
  • Organization of information: The tables are organized by study and by measurement type (fruit/vegetable intake and depressive symptoms). This allows readers to easily compare the methods used across the different studies and to understand the specific details of how these key variables were assessed.
Scientific Validity
  • Transparency and reproducibility: Providing detailed information about the assessment methods is crucial for evaluating the validity and reliability of the study's findings. This transparency allows other researchers to scrutinize the methods used, assess potential biases, and replicate the study if needed.
  • Assessment quality and validation: The validity of the study depends on the quality of the assessment methods used. The supplementary tables should include details that allow readers to assess this quality. For dietary assessments, information about the validity and reliability of the questionnaires used is essential. For depressive symptoms, details about the validation and psychometric properties of the chosen instruments should be provided. Including this information would strengthen the scientific rigor of the study.
  • Consistency of methods over time: Given the study's focus on longitudinal associations, it is essential that the supplementary tables clearly describe whether the same assessment methods were used at all follow-up time points or if any changes were made. Consistency in measurement over time is critical for ensuring the accuracy of the longitudinal analyses. Any changes in assessment methods over time could introduce bias and should be carefully considered and explained.
Communication
  • Organization and accessibility: Providing the assessment details in supplementary tables is a good practice, allowing the main text to focus on the key findings while still providing comprehensive information for interested readers. Clear labeling of the tables (S1 and S2) and a concise caption facilitate easy navigation.
  • Informativeness of caption: While the caption is concise, it could be more informative by briefly indicating the type of information contained in each table. For example, mentioning that Table S1 details dietary assessment and Table S2 details depressive symptom assessment would improve readers' ability to quickly locate the relevant information.
Supplementary Table S3. Cut-offs for fruit and vegetable intake categories.
First Reference in Text
Cut-offs for each cohort were selected to achieve as close as possible to equal numbers of participants in each category, taking into consideration the distribution of intakes (for example in the MADT cohort, 1210 of the 2298 participants reported consuming one serve of fruit/day therefore the 3 categories used were < 1 serve/day; 1 serve/day and > 1 serve/day) (Supplementary Table S3).
Description
  • Overview of cut-off values: This table shows the specific values used to divide participants into "low," "medium," and "high" intake groups for both fruits and vegetables. Imagine having a group of people and sorting them based on how much fruit they eat daily. This table would list the dividing lines. For instance, anyone eating less than one serving of fruit per day might be in the "low" group, those eating one serving per day in the "medium" group, and those eating more than one serving per day in the "high" group. These dividing lines, or "cut-offs," are chosen to have roughly equal numbers of people in each group while still reflecting the typical spread of fruit and vegetable consumption in each of the four studies.
  • Table organization and content: The table is organized by study (MADT, MTSADA, OATS, and SATSA) and presents separate cut-offs for fruit and vegetable intakes. For each study and intake type, there are cut-off values defining the boundaries between the three intake categories. The cut-offs reflect the specific distribution of intakes in each study.
Scientific Validity
  • Justification for cut-off selection: The authors' approach of creating roughly equal-sized groups by using specific cut-points is acceptable. This helps ensure adequate statistical power within each intake category, especially with non-normal distributions where standard deviations might not be the most informative measure of spread. However, the arbitrary nature of these cut-offs introduces a degree of subjectivity. The authors attempt to mitigate this by considering the distribution of intakes within each cohort.
  • Consideration of data distribution and alternative methods: While aiming for equal group sizes is a common practice, it is essential to consider the underlying distribution of the data. If the data are highly skewed, aiming for perfectly equal groups might lead to cut-offs that misrepresent clinically meaningful differences in intake. The authors mention taking the distribution of intakes into account, but providing more detail in the supplementary table about the actual distributions would strengthen the justification for the chosen cut-offs. Visualizations such as histograms or box plots would provide further insight into this categorization process. Additionally, exploring alternative categorization strategies, such as using quantiles (e.g., tertiles, quartiles), would strengthen the analysis.
Communication
  • Transparency and reproducibility: Clearly presenting the cut-off values for each cohort in a supplementary table enhances transparency and allows for detailed scrutiny of the categorization process. This contributes to the reproducibility of the study and facilitates comparison with other research.
  • Clarity and completeness of caption: While the caption adequately describes the table's content, it could benefit from greater detail. Including information about the units used for the cut-offs (e.g., servings/day) would enhance clarity. Additionally, explicitly stating whether the cut-offs apply to daily or weekly intake would prevent ambiguity.

Results

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Table 2. Baseline characteristics of participants by study. SD standard...
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Table 2. Baseline characteristics of participants by study. SD standard deviation; BMI body mass index, IQR inter quartile range; MADT Middle Age Danish Twins Study; MTSADA Minnesota Twin Study of Adult Development and Aging; OATS Older Australian Twins Study; SATSA Swedish Adoption/Twin Study of Aging.

First Reference in Text
Baseline characteristics of participants were presented in Table 2.
Description
  • Overview: Table 2 presents the baseline characteristics of participants from four different twin studies. Imagine each study as a separate group of people we are observing. For each group, the table tells us about things like their age, how many are women, their education level, how many live alone, their Body Mass Index (BMI), their physical and cognitive health, how much fruit and vegetables they eat daily, and their depressive symptom scores at the beginning of the study. It also includes the distribution of twin types within each study (monozygotic, same-sex dizygotic, and opposite-sex dizygotic). Monozygotic twins are identical, while dizygotic twins are like regular siblings. These characteristics are shown as averages (mean) with a measure of spread (standard deviation) for normally distributed data, and medians with interquartile ranges for data not following a normal distribution. The depressive symptom score is described with the median and IQR (interquartile range) which provides the range within which the central 50% of scores lie.
  • Data presentation and organization: The table is organized by study, presenting characteristics in separate columns for each of the four cohorts (MADT, MTSADA, OATS, and SATSA). It also includes a "Total" column for aggregated values across all studies. Rows represent different characteristics, allowing easy comparison across the studies for each specific characteristic. The table uses standard statistical measures like mean, standard deviation (SD), median, and interquartile range (IQR) to summarize the data. IQR represents the difference between the 75th and 25th percentiles of the data, showing the spread of the middle 50% of the values.
Scientific Validity
  • Importance of baseline data: Presenting baseline characteristics is crucial for demonstrating the comparability of the cohorts and for identifying any potential confounding factors that need to be addressed in subsequent analyses. Providing data on demographics, health status, dietary habits, and initial depressive symptoms establishes the foundation for interpreting the longitudinal relationships examined in the study. Zygosity information is vital as twin studies often rely on comparisons within twin pairs to control for genetic and shared environmental factors.
  • Completeness of information: The authors clearly present descriptive statistics for each cohort and overall. However, the lack of details regarding the scales used for "physical health" and "cognitive health" makes it difficult to interpret these measures fully. Providing this information, such as the scoring range, or clarifying the specific cognitive tests used, would improve the transparency and scientific rigor of the study.
Communication
  • Clarity and structure: The table is generally well-organized and presents a comprehensive overview of the participants' baseline characteristics. However, using abbreviations like SD and IQR without explanation within the table itself could hinder quick interpretation. While they are defined in the caption, repeating them under the table would improve readability.
  • Use of scales: While the table effectively communicates baseline characteristics, it lacks clarity regarding the scoring of "physical health" and "cognitive health." Specifying the scales used (e.g., range, possible maximum score) would enhance understanding and allow for better comparison across studies. Additionally, clarifying the meaning of the z-score for cognitive health would be beneficial.
Table 3. Baseline characteristics of participants by category of fruit and...
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Table 3. Baseline characteristics of participants by category of fruit and vegetable intake. SD standard deviation; BMI body mass index, IQR Inter quartile range.

First Reference in Text
Characteristics by category of fruit and vegetable intake are included in Table 3.
Description
  • Overview: This table shows the baseline characteristics of the study participants, but unlike Table 2 which presented them by study, this one organizes them by how much fruit and vegetables people ate at the start. Imagine sorting everyone into three groups based on their fruit consumption: low, medium, and high. Then, for each group, we look at things like their age, gender, education, living situation, BMI, physical and cognitive health, their actual average fruit and vegetable intake, and their depressive symptoms score. We repeat this process but now grouping people based on vegetable consumption. This helps us see if there are any initial differences between people who eat different amounts of fruits and vegetables before we even look at changes over time. The data is summarized using familiar statistical measures: averages (mean), standard deviations (SD), medians, and IQR (interquartile range).
  • Data presentation: The table is organized with separate sections for fruit intake and vegetable intake. Within each section, there are columns for each intake category (low, medium, and high). This side-by-side presentation makes it straightforward to compare characteristics across different levels of fruit and vegetable consumption. Statistical summaries, such as mean and SD for normally distributed continuous variables and median with IQR for those not normally distributed, are presented for each characteristic within each intake group. IQR shows the data spread by presenting the difference between the 75th and 25th percentile values.
Scientific Validity
  • Importance of stratification: Stratifying baseline characteristics by intake categories is a crucial step for evaluating potential confounding and effect modification. It helps determine if the groups differ systematically on other factors besides diet that could influence their risk of depression. This is important for the study's internal validity and ensures that any observed associations between diet and depression are not spurious due to pre-existing differences between the intake groups.
  • Missing scale information: While Table 3 provides valuable descriptive data, the omission of specific scales used for physical and cognitive health measurements limits its scientific utility. Without clear definitions or scoring ranges for these measures, meaningful comparisons across intake categories are hampered. Including this information would enhance the table's contribution to the overall study.
Communication
  • Clarity and structure: The layout is clear, but providing explicit units (e.g., servings/day) for fruit and vegetable intake would significantly enhance readability. While the table caption defines SD and IQR, it would improve clarity to reiterate these definitions below the table for quick reference.
  • Lack of context for health measures: Similar to Table 2, a clear explanation of the scales or scoring systems for "physical health" and "cognitive health" is missing. Without this context, it's challenging to compare these values across different intake categories or with other studies. Providing this detail is essential for effective communication.
Table 4. Associations between fruit and vegetable intake and depressive...
Full Caption

Table 4. Associations between fruit and vegetable intake and depressive symptoms over time (5-11 years), n=3483. Mean intakes serves/day combined analysis: Fruit-Low 0.3 0.2, Moderate 1.0 0.2, High 2.1 0.8; Vegetables-Low 0.5 0.3, Moderate 1.1 0.4, High 2.0 0.7. Analysis based on log10 transformed depressive symptoms, transforming back for the fully adjusted model results in the following ratios-Fruit: Moderate 0.986, High 0.984, Vegetables: Moderate 0.989, High 0.985. Based on a median depressive symptom score of 18, a change from low intake is associated with the following reduction in depressive symptoms: Fruit- Moderate 0.25, High 0.29; Vegetables-Moderate 0.20, High 0.26. Partially adjusted model adjusted for age, sex, education and living alone status; fully adjusted model additionally adjusted for BMI, physical health and cognitive ability. MADT Middle Age Danish Twins Study; MTSADA Minnesota Twin Study of Adult Development and Aging; OATS Older Australian Twins Study; SATSA Swedish Adoption/Twin Study of Aging. Significant results indicated in bold.

First Reference in Text
A similar result was found in the fully adjusted model (moderate: =-0.005 [95%CI 0.009, -0.001], I2=0%; high: =-0.006 [95% 0.011, 0.002], I =0%) (Table 4).
Description
  • Overview: This table shows how fruit and vegetable intake relate to changes in depressive symptoms over time. It does this by looking at four different groups of twins (MADT, MTSADA, OATS, and SATSA) and by considering three levels of intake: low, medium, and high. Imagine each level of intake as a team, so we have a "low fruit" team, a "medium fruit" team, and a "high fruit" team, and the same for vegetables. The table compares the changes in depression scores for the medium and high intake teams relative to the low intake team. This comparison is done using a statistical method called linear mixed-effects regression, which is like drawing a line of best fit through data points that are clustered (like twin data) and measured over time. The "β" value represents the slope of this line. Because the original depressive symptom data were skewed, they were transformed using a logarithmic function (log10) before analysis, hence the back-transformation of β is provided. The 95% confidence interval (CI) indicates the range within which the true value of β is likely to fall. I² is a measure of inconsistency across studies - how much the study results differ from each other. The table also shows the average intake (in servings/day) for each intake level and what the values mean in terms of changes in depressive symptoms scores.
  • Data presentation: The table is organized into two main sections: one for fruit and one for vegetables. Within each section, results are presented separately for each cohort and then combined using a meta-analysis, a method of statistically combining the results of multiple studies. Two models are presented: "partially adjusted" and "fully adjusted." These models control for other factors that might influence the relationship between diet and depression. The partially adjusted model considers age, sex, education, and living alone status, while the fully adjusted model adds BMI, physical health, and cognitive ability. The reference intake group in these comparisons is the "low" intake level. The table also presents the mean intake for each level and provides interpretation of results both in terms of values and converted scores.
Scientific Validity
  • Justification of multiple models: Presenting results from both partially and fully adjusted models is crucial for understanding the impact of different sets of confounders. This approach strengthens the analysis by allowing readers to assess how the associations change when factors like BMI, physical health, and cognitive ability are considered.
  • Appropriateness of statistical methods: Using linear mixed-effects models is appropriate for analyzing longitudinal data with clustered observations, like the twin data in this study. This method accounts for correlations within twin pairs and across time, leading to more accurate estimates of the associations between diet and depressive symptoms.
  • Assessment of heterogeneity: Providing the I² statistic alongside the combined results from the meta-analysis offers valuable insight into the heterogeneity across the four cohorts. A low I² indicates that the results are relatively consistent across studies, strengthening the overall conclusions.
  • Interpretation of transformed data: While the transformation of the depressive symptom scores is understandable given the skewness, providing both transformed results and their back-transformed interpretations is essential for conveying practical significance. The added context of the effect size on a typical depressive symptom score of 18 makes the results more relatable and meaningful.
Communication
  • Clarity of results presentation: Presenting both the regression coefficients ( ) and the transformed ratios and changes in depressive symptom scores helps provide both statistical significance and practical relevance, making the results more interpretable for a broader audience. Highlighting significant p-values in bold improves readability and quickly directs the reader to the key findings.
  • Specification of reference category: While the caption provides essential details, it could be improved by clarifying the reference category for the intake comparisons. Explicitly stating that the moderate and high intakes are compared to the 'low' intake category would enhance understanding.
  • Organization of information: The caption becomes quite dense with information. Separating the mean intake values for each group into a separate table row within the table itself could improve the overall clarity of the caption and make it easier to locate this specific information.
Supplementary Figure S1. Directed Acyclic Graph (DAG).
First Reference in Text
A Directed Acyclic Graph (DAG)37 was used to assist in determining which potential confounders should be adjusted based on the direction of their likely relationships with the exposure and outcome (Supplementary Figure S1).
Description
  • Overview of a DAG: The DAG is a visual representation of the assumed causal relationships between fruit/vegetable intake, depressive symptoms, and other relevant variables. Think of it like a map showing how different factors might influence each other. Arrows connect variables, indicating the direction of the presumed causal effect. For example, an arrow from "Age" to "Depressive Symptoms" suggests that age might influence the likelihood of experiencing depressive symptoms. The absence of an arrow between two variables indicates an assumption of no direct causal effect. Variables included in the DAG are those measured in the study, like age, sex, education, BMI, physical and cognitive health, and socioeconomic status.
  • Purpose of the DAG: The DAG is used to identify potential confounders, variables that influence both the "exposure" (fruit/vegetable intake) and the "outcome" (depressive symptoms). Confounders can create spurious associations if not accounted for in the statistical analysis. The DAG allows researchers to visualize these potential confounders and decide which ones to include in their adjusted statistical models. By controlling for these confounders, the researchers can isolate the specific effect of fruit/vegetable intake on depressive symptoms, independent of the influence of these other factors.
Scientific Validity
  • Methodological rigor: Using a DAG to guide confounder selection is a rigorous approach to causal inference in observational studies. By visually representing the hypothesized causal relationships, the DAG helps minimize bias due to confounding and strengthens the internal validity of the study's conclusions.
  • Justification of causal assumptions: The scientific validity of the DAG depends on the accuracy of the hypothesized causal relationships depicted in the figure. These relationships should be based on prior scientific knowledge and theoretical understanding of the subject matter. Justifying the included relationships based on existing literature would further strengthen the validity of the DAG. Similarly, explaining any excluded pathways and their rationale would strengthen the transparency and rigor of the study.
Communication
  • Visual clarity and organization: The DAG's visual clarity could be enhanced by using a consistent shape or color scheme to differentiate between different variable types (e.g., exposures, outcomes, confounders). This visual distinction would aid in quicker comprehension of the causal relationships being depicted. Labeling the arrows with the hypothesized direction of the relationship would further improve clarity.
  • Contextualization and explanation: While the figure provides a valuable representation of the hypothesized causal relationships, providing a brief narrative description of the DAG within the supplementary materials would greatly enhance its communicative power. This description should explain the key pathways and justify the inclusion or exclusion of specific variables. This would make the figure more accessible to readers less familiar with DAGs.
Supplemental Table S4. Comparison of included versus excluded participants.
First Reference in Text
A comparison of included versus excluded participants revealed that included participants were younger, healthier, more educated, and less likely to be female or living alone (Supplemental Table S4).
Description
  • Overview of participant comparison: This supplementary table compares the characteristics of the participants included in the main analyses with those who were excluded. Imagine having a large group of potential participants. Some meet the study's criteria and are included, while others are excluded for various reasons (e.g., missing data, not meeting age criteria). This table shows the average characteristics of these two groups. For example, it might show the average age, gender distribution, education level, health status, and dietary habits of both the included and excluded groups. This helps us understand how the included participants might differ from the broader population the study intended to represent and assess potential biases introduced by the exclusion criteria.
  • Table organization and statistical presentation: The table likely organizes information into two main columns: "Included" and "Excluded." Rows represent various participant characteristics, such as age, sex, education level, health status indicators, dietary intake measures, and baseline depressive symptoms. The specific statistics presented in each cell will likely vary depending on the type of variable being compared. For continuous variables, means and standard deviations or medians and interquartile ranges might be reported. For categorical variables, counts and percentages would be appropriate. P-values from statistical tests, like t-tests or chi-square tests, are likely also presented to indicate if the observed differences between the groups are statistically significant.
Scientific Validity
  • Assessment of selection bias: Comparing included and excluded participants is crucial for understanding the generalizability of the study's findings. This helps determine if there are systematic differences between those who participated and those who did not, which could affect how the results apply to the broader population of interest. By identifying these differences, researchers can acknowledge potential limitations of the study related to sample selection bias.
  • Comprehensiveness of comparison: The strength of the comparison depends on the completeness of the data presented. The table should include all relevant variables that could potentially differ between the included and excluded groups. The reference text mentions age, health, education, sex, and living alone status, which are all important factors. However, including additional relevant baseline characteristics, such as income level, access to healthcare, or pre-existing health conditions, would provide a more comprehensive assessment of potential biases. Additionally, the table should clearly state the criteria used for including and excluding participants.
  • Discussion of implications: While identifying differences between the included and excluded groups is important, the authors should further discuss the potential implications of these differences on the study's results and conclusions. Simply noting that the groups differ is not enough; it is essential to analyze how these differences might bias the observed associations between fruit and vegetable intake and depressive symptoms. This would enhance the rigor and impact of the study.
Communication
  • Conciseness and clarity: Presenting the comparison in a supplementary table maintains the focus of the main text on the primary analyses while providing valuable information about the study's sample and potential biases. The concise caption clearly indicates the table's purpose.
  • Informativeness of caption: While the reference text mentions key differences, the caption itself could be more informative. Including a brief summary of the statistical tests used (e.g., t-tests, chi-square) in the caption would enhance clarity and allow readers to better interpret the significance of the reported differences.
Supplementary Table S5. Sensitivity analysis including potatoes in vegetable...
Full Caption

Supplementary Table S5. Sensitivity analysis including potatoes in vegetable intake.

First Reference in Text
In the first sensitivity analysis, when potatoes were included in vegetable intake, there was no longer a relationship for either moderate or high vegetable intakes, compared with low intakes (moderate: = -0.005 [95%CI -0.012, 0.001], I2=55%; high: =-0.004 [95%CI – 0.008, 0.001], I = 10%) (Supplementary Table S5).
Description
  • Overview of sensitivity analysis: This table shows the results of a "sensitivity analysis" where potatoes are counted as part of the vegetable intake. Think of a sensitivity analysis like a "what if" scenario. In the main analysis, potatoes were treated separately from other vegetables. This table explores what happens to the results if we change that assumption and include potatoes in the total vegetable intake. It then compares the effect of this combined fruit and vegetable intake (including potatoes) on depressive symptoms over time, similar to the main analysis in Table 4.
  • Table organization and data presentation: The table likely presents similar information as Table 4, but with the key difference being the inclusion of potatoes in the vegetable intake calculation. It probably shows the associations (β coefficients, 95% confidence intervals, p-values, and I² statistics) between different intake levels and changes in depressive symptoms over time for each cohort and the combined analysis. The specific format and organization are likely similar to Table 4, enabling direct comparison of the results with and without potatoes included in vegetable intake.
Scientific Validity
  • Importance of sensitivity analysis: Conducting this sensitivity analysis is crucial for assessing the robustness of the study's findings. By including potatoes in the vegetable intake, the authors explore a different definition of the exposure variable and determine if the initial results hold under this modified assumption. This helps address potential concerns about the arbitrary exclusion of potatoes and enhances the credibility of the overall conclusions.
  • Handling of potato data and interpretation: The validity of this analysis depends on the appropriate handling of the potato intake data. The table should clearly define how potato consumption was quantified and combined with other vegetable intakes. Providing details about different potato types (e.g., boiled, fried, processed) and their respective contributions to the total intake would strengthen the analysis. The reference text only mentions that the relationship becomes non-significant when potatoes are included but explaining the possible reasons behind this change within the table would enhance interpretation and scientific rigor.
Communication
  • Clarity and conciseness: The caption clearly and concisely describes the purpose of the sensitivity analysis. Presenting this analysis in a supplementary table allows for a focused exploration of this specific issue without disrupting the flow of the main results.
  • Informativeness: While the caption adequately labels the table, it could be more informative. Including details in the caption about the specific outcomes and statistical measures presented in the table would improve clarity and help readers quickly locate relevant information.
Supplementary Table S6. ICE FALCON analysis.
First Reference in Text
In ICE FALCON analysis using paired twin data, for fruit intake, no association was found in either Model 1 or Model 2 (no association between either a twin's intake and their depressive symptoms or a twin's intake and their co-twin's depressive symptoms).
Description
  • Overview of ICE FALCON analysis: This supplementary table presents the results of the ICE FALCON analysis, a statistical method used in twin studies to assess whether observed associations might be due to shared genetic or environmental factors rather than a true causal link. Imagine comparing the fruit intake of one twin to their own depressive symptoms and also to their co-twin's depressive symptoms. If a twin's fruit intake is related to both their own and their co-twin's depressive symptoms, it suggests that some shared factor, rather than fruit itself, might be the true driver. ICE FALCON helps disentangle these relationships. It involves three models: Model 1 examines the association between a twin's fruit intake and their own depressive symptoms; Model 2 examines the association between a twin's fruit intake and their co-twin's depressive symptoms; and Model 3 further assesses changes in these relationships after specific adjustments.
  • Table organization and data presentation: The table likely organizes the data by fruit and vegetable intake separately, and then by model (Model 1, Model 2, and Model 3). Within each model, results for each cohort (MADT, MTSADA, OATS, SATSA) and a combined analysis are presented. The content likely includes regression coefficients, corresponding confidence intervals, p-values, and possibly other relevant statistics. This presentation facilitates comparison across models and cohorts, enabling a detailed examination of the role of familial confounding.
Scientific Validity
  • Methodological rigor: Employing ICE FALCON is a statistically sound method for addressing familial confounding in twin studies, strengthening the internal validity of the study's conclusions about causality. This approach leverages the unique structure of twin data to distinguish between associations driven by shared genetics or environment and those potentially representing causal relationships.
  • Assumptions and limitations: The validity of ICE FALCON relies on certain assumptions, particularly the absence of other confounders not accounted for in the analysis. The authors should clearly state these assumptions in the supplementary table or accompanying text. It's crucial to consider whether these assumptions are met in the context of the current study and address any limitations that could arise from violations of these assumptions.
  • Completeness of results: While the reference text mentions the lack of association for fruit intake in Models 1 and 2, the table should provide the full results, including the effect estimates, confidence intervals, and p-values, even for non-significant findings. This transparency is essential for scientific rigor and enables readers to draw their own conclusions. The table should also provide sufficient detail about the model specifications, including the specific variables adjusted for in each model.
Communication
  • Clarity and conciseness: The concise caption clearly indicates the analysis method used. Placing this analysis in a supplementary table is appropriate, allowing for a detailed examination without cluttering the main results section.
  • Informativeness of caption: While the caption mentions ICE FALCON, it lacks context. Briefly explaining the purpose of ICE FALCON (assessing familial confounding) in the caption would make the table more accessible to readers unfamiliar with this method. Additionally, specifying the type of data presented in the table (e.g., regression coefficients, p-values) would improve clarity.

Discussion

Key Aspects

Strengths

Suggestions for Improvement

Conclusion

Key Aspects

Strengths

Suggestions for Improvement

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