This cross-sectional study investigated the relationship between adherence to the EAT-Lancet diet, a diet designed for both human and planetary health, and cardiometabolic risk factors in a sample of 398 adults and elderly residents of Natal, Brazil, between 2019 and 2020. Researchers used the Planetary Health Diet Index (PHDI) to measure how closely participants' diets aligned with the EAT-Lancet recommendations and the Cardiovascular Health Diet Index (CHDI) to assess cardiovascular health. The study collected sociodemographic information, lifestyle data, dietary intake via 24-hour recall, anthropometric measurements, and biochemical markers. Multiple linear regression models were used to analyze the associations between the PHDI, CHDI, and cardiometabolic risk factors, while adjusting for potential confounders like age, body mass index (BMI), and income. The study aimed to understand how dietary patterns impact both individual and environmental well-being.
Description: This table presents participant characteristics categorized by PHDI adherence terciles (low, medium, and high). It provides demographic, lifestyle, health condition, and biochemical data, enabling comparisons across different levels of PHDI adherence.
Relevance: Table 1 helps characterize the study sample and explore potential associations between PHDI adherence and various factors. This allows readers to better understand the study population and how their characteristics relate to their dietary habits.
Description: This figure consists of two radar charts visually representing participants' adherence to the PHDI (Chart A) and CHDI (Chart B). Each axis on the chart corresponds to a food group or component of the index, allowing for a quick comparison of adherence across different dietary components.
Relevance: Figure 2 provides a clear and concise overview of the study population's dietary patterns. It allows for a visual comparison of adherence to both the sustainability-focused PHDI and the heart-health-focused CHDI, highlighting areas of alignment and divergence.
This study provides evidence for a positive association between adherence to the EAT-Lancet diet and improved cardiovascular health indicators in a Brazilian population. The findings suggest that promoting sustainable diets may offer co-benefits for both planetary and human health. Further research, particularly using longitudinal designs and incorporating detailed policy recommendations, is needed to establish causality and translate these findings into effective public health interventions. Future research should also address the challenges of promoting sustainable diets in diverse sociocultural contexts, particularly in developing countries facing issues related to food access and affordability. Exploring culturally tailored interventions to promote healthy and sustainable eating habits could improve population health and contribute to global sustainability efforts.
This study investigates the link between adherence to the EAT-Lancet diet and cardiometabolic risk factors in adults and elderly residents of Natal, a Brazilian city. The EAT-Lancet diet is designed to improve both human and planetary health. Researchers used the Planetary Health Diet Index (PHDI) and the Cardiovascular Health Diet Index (CHDI) to measure diet quality and cardiovascular health, respectively. The study found a positive association between following the EAT-Lancet diet and improved cardiovascular health indicators.
The abstract immediately states the study's aim, which is to investigate the association between the EAT-Lancet diet and cardiometabolic risk factors. This clarity helps readers quickly understand the research focus.
The abstract succinctly describes the study's cross-sectional design, the population sampled, and the indices used for assessment. This brevity is appropriate for an abstract and provides essential information.
The abstract concludes by directly stating the main finding: a positive association between the EAT-Lancet diet and improved cardiovascular health indicators. This reinforces the study's key takeaway message.
While the abstract mentions a positive association, it would be stronger if it quantified the improvement in cardiovascular health indicators. For example, mentioning the magnitude of change in specific parameters (e.g., "X% reduction in blood pressure") would add more weight to the findings.
Rationale: Quantifying the improvement would make the abstract more impactful and provide readers with a more concrete understanding of the study's findings.
Implementation: Include specific numerical results, such as the percentage change or difference in means for relevant cardiometabolic parameters.
Acknowledging limitations, even briefly, strengthens the abstract's scientific rigor. For instance, mentioning the cross-sectional design and its implications for causality would enhance the abstract's transparency.
Rationale: Addressing limitations increases the reader's confidence in the study's findings and acknowledges the inherent constraints of the research design.
Implementation: Add a concise sentence acknowledging any major limitations, such as "Due to the cross-sectional design, causality cannot be established."
This introduction sets the stage for a study investigating the relationship between the EAT-Lancet diet and cardiovascular health. It begins by highlighting the global prevalence and preventability of cardiovascular diseases (CVDs), emphasizing the role of diet and lifestyle. The introduction then discusses the interconnectedness of human health and environmental sustainability, particularly in the context of dietary choices. It introduces the concept of sustainable diets and their potential benefits for cardiovascular health, primarily through mechanisms like replacing saturated fats with unsaturated fats and increasing fiber intake. The introduction also acknowledges the challenges of adopting sustainable diets, such as accessibility and cost, especially in contexts of inequality. Finally, it introduces the EAT-Lancet Commission's report and its proposed "planetary health diet" (PHD), which aims to promote both human and planetary well-being. The introduction concludes by emphasizing the need to study dietary patterns to understand the synergy between cardiovascular health and planetary health and introduces the Planetary Health Diet Index (PHDI) and Cardiovascular Health Diet Index (CHDI) as tools for this purpose.
The introduction effectively establishes the context of the study by highlighting the global burden of CVDs and their preventability. This emphasizes the relevance of the research question.
The introduction clearly links dietary choices to both human health and environmental sustainability, setting the stage for investigating the synergy between these two aspects.
The introduction effectively introduces key concepts like sustainable diets, the EAT-Lancet Commission's report, and the PHD, providing a foundation for understanding the study's focus.
The introduction could streamline the discussion of the complex relationship between nutritionally adequate diets and environmental impact. While the point about potential conflicts is valid, it could be made more concisely.
Rationale: A more concise explanation would improve the flow and readability of the introduction.
Implementation: Condense the information into a single, clear sentence highlighting the potential for conflict between nutritional adequacy and environmental impact.
The introduction could more explicitly explain why studying dietary patterns is crucial for understanding the relationship between diet, health, and sustainability. It mentions that dietary patterns provide a more comprehensive analysis, but it could elaborate on this point.
Rationale: A stronger rationale would better justify the study's focus on dietary patterns.
Implementation: Expand on the advantages of studying dietary patterns, such as their ability to capture the combined effects of multiple food groups and dietary components.
The introduction briefly mentions a priori and a posteriori approaches to dietary pattern analysis but doesn't clearly explain the difference. This could confuse readers unfamiliar with these terms.
Rationale: A clear distinction between these approaches would enhance the reader's understanding of the methodological landscape.
Implementation: Provide a more detailed explanation of each approach, including examples and their respective strengths and weaknesses.
This section details the study's design and procedures used to investigate the association between the EAT-Lancet diet and cardiometabolic risk factors. It describes the cross-sectional study conducted in Natal, Brazil, between 2019 and 2020, involving 398 adults and elderly individuals. The study used a complex sampling plan, accounting for the COVID-19 pandemic's impact on data collection. Data collection included sociodemographic and lifestyle information, dietary intake (using a 24-hour recall and analyzed with the PHDI and CHDI), anthropometric measurements, and biochemical markers related to cardiometabolic risk. The data analysis involved calculating frequencies, means, standard deviations, and using multiple linear regression models to assess associations between dietary indices and cardiometabolic risk factors, adjusting for potential confounders.
The section provides a thorough explanation of the sampling plan, including the rationale for the chosen method, the stages involved, and the adjustments made due to the COVID-19 pandemic. This level of detail enhances the study's transparency and reproducibility.
The study collected a wide range of data, including sociodemographic factors, lifestyle information, dietary intake, anthropometric measurements, and biochemical markers. This comprehensive approach allows for a more thorough investigation of the research question.
The section clearly explains how dietary intake was assessed, including the use of a 24-hour dietary recall and the calculation of the PHDI and CHDI. This clarity is crucial for understanding how the key exposures were measured.
While the section mentions the target sample size and the impact of the pandemic, it doesn't explicitly justify the final sample size of 398 or discuss its adequacy for the study's objectives. A power analysis or a justification based on the expected effect size and desired precision would strengthen this aspect.
Rationale: Justifying the sample size would enhance the study's rigor and provide readers with greater confidence in the results.
Implementation: Include a power analysis or a clear justification for the final sample size, explaining how it was determined and why it is considered sufficient for the study's aims.
The section mentions the use of multiple linear regression models but doesn't specify the exact variables included in each model or the specific hypotheses being tested. Providing more detail on the model specifications would improve the transparency and reproducibility of the analysis.
Rationale: A more detailed description of the regression models would allow readers to better understand the statistical analysis and interpret the results.
Implementation: Specify the dependent and independent variables included in each model, along with any interaction terms or transformations applied. Also, state the specific hypotheses being tested by each model.
The section doesn't mention how missing data were handled in the analysis. It's important to address this issue, as missing data can introduce bias and affect the validity of the results.
Rationale: Addressing the issue of missing data would enhance the study's transparency and allow readers to assess the potential impact of missingness on the findings.
Implementation: Describe the extent of missing data for each variable and explain the methods used to handle missingness, such as imputation or complete case analysis.
Figure 1 is an infographic detailing the eleven components of the CHDI (Cardiovascular Health Diet Index) and how they are scored. Each component represents a food group, and the scoring is based on daily consumption amounts. Higher scores indicate better adherence to a cardioprotective diet. For example, consuming more than 340g of fruits per day earns the maximum 10 points for that component, while no fruit consumption earns 0 points. Similarly, the scoring for vegetables, fish and seafood, whole grains, legumes, nuts, and dairy is based on exceeding specific daily intake thresholds. Conversely, the scoring for red meat, sugar-sweetened beverages (SSB), processed meat, and ultra-processed foods (UPF) is inversely related to consumption; lower intake earns higher scores. The infographic uses icons to represent each food group and clearly presents the scoring criteria for each component.
Text: "The parameters for each metric can be seen in Fig. 1."
Context: The CHDI is structured on eleven dietary intake metrics to assess cardiovascular health [28]. Each item the CHDI meets is scored proportionally to the consumption, which can reach up to 10 points, meaning it ranges from 0 to 110 points. The higher this score, the better the individual cardiovascular health. The parameters for each metric can be seen in Fig. 1.
Relevance: Figure 1 is crucial for understanding how the CHDI is calculated and what dietary components it emphasizes. It provides a visual representation of the scoring criteria, making it easier to grasp the index's components and their relative importance in promoting cardiovascular health.
This section presents the findings of the study, which examined the association between adherence to the EAT-Lancet diet and cardiometabolic risk factors. The average Planetary Health Diet Index (PHDI) score was 29.4 out of 150, and the average Cardiovascular Health Diet Index (CHDI) score was 32.63 out of 110. The PHDI score was positively associated with the CHDI score and the consumption of fruits, vegetables, and legumes. Conversely, it was negatively associated with ultra-processed food (UPF) consumption. The most frequently consumed UPFs were packaged snacks, potatoes, and crackers, followed by margarine. A higher PHDI score was also linked to lower systolic blood pressure, total cholesterol, and LDL cholesterol. Additionally, individuals with type 2 diabetes and dyslipidemia had lower PHDI scores.
The results section clearly presents the main findings of the study, including the average PHDI and CHDI scores and their associations with various factors. The use of tables and figures enhances the clarity of the presentation.
The study uses appropriate statistical methods, such as multiple linear regression, to analyze the data and control for potential confounders. This strengthens the validity of the findings.
The results section provides specific examples of the most commonly consumed UPFs in the study population, which helps to contextualize the findings and make them more relatable.
While the average PHDI and CHDI scores are presented, the section could provide more context for interpreting these scores. For example, explaining what constitutes a "high" or "low" score on each index would be helpful.
Rationale: Providing more context for the scores would help readers understand the magnitude of adherence to each diet in the study population.
Implementation: Explain what constitutes a high, moderate, and low score on both the PHDI and CHDI. Consider providing reference values from other studies or populations if available.
The results section focuses on statistical significance but could be strengthened by discussing the clinical significance of the findings. For example, explaining the practical implications of the observed associations between PHDI and cardiometabolic markers would be valuable.
Rationale: Discussing the clinical significance would make the findings more relevant to healthcare professionals and readers interested in the practical implications of the study.
Implementation: Interpret the magnitude of the observed associations in terms of their potential impact on cardiovascular health outcomes. Consider discussing the clinical relevance of the changes in SBP, total cholesterol, and LDL cholesterol.
While the section mentions some key associations, it doesn't present the full results of the regression models. Including a table with all the regression coefficients, confidence intervals, and p-values would provide a more complete picture of the analysis.
Rationale: Presenting the full results of the regression models would enhance the transparency and reproducibility of the analysis.
Implementation: Include a table that summarizes the results of all the regression models, including the coefficients, confidence intervals, and p-values for all the independent variables.
Table 1 presents the characteristics of the participants in the Brazuca-Natal study, categorized by their adherence to the Planetary Health Diet Index (PHDI). The PHDI is divided into three terciles (low, medium, and high adherence). The table shows the distribution of participants across these terciles for various characteristics, including demographics (age group, sex, education level, income), lifestyle factors (BMI, physical activity, alcohol and tobacco use), health conditions (hypertension, diabetes, dyslipidemia), and biochemical markers (blood pressure, glucose, cholesterol, etc.). For each characteristic, the table provides the number of participants (n) and the weighted number (npond), along with their respective percentages within each PHDI tercile. The p-value indicates whether there are statistically significant differences in the distribution of each characteristic across the PHDI terciles.
Text: "Data from 398 individuals of both sexes were collected. Table 1 presents the characteristics of the confirmed population and their scores related to PHDI adherence terms."
Context: Results Data from 398 individuals of both sexes were collected. Table 1 presents the characteristics of the confirmed population and their scores related to PHDI adherence terms.
Relevance: Table 1 is essential for understanding the characteristics of the study population and how these characteristics relate to their adherence to the PHDI. It provides a detailed breakdown of the sample across different levels of PHDI adherence, allowing for comparisons and identification of potential associations between PHDI and various factors.
This table is a continuation of Table 1, providing further details on the characteristics of the study population and their adherence to the Planetary Health Diet Index (PHDI). It includes additional variables such as age group, triglycerides, and the Cardiovascular Health Diet Index (CHDI), categorized by PHDI terciles. The table structure is consistent with the first part of Table 1, showing the number of participants (n), the weighted number (npond), percentages, and p-values for each variable. Footnotes explain abbreviations and the statistical methods used.
Text: "Table 1 (continued)"
Context: Page 9 of 15 Table 1 (continued)
Relevance: This continuation of Table 1 provides additional information about the study population and their dietary habits, specifically regarding triglycerides and CHDI scores, which are important for understanding the relationship between the PHDI and cardiovascular health.
Figure 2 presents two radar charts. Chart (A) illustrates the mean scores and standard deviations for each component of the Planetary Health Diet Index (PHDI). The components, represented by axes on the radar chart, include whole grains, tubers, fruits, vegetables (with subcategories for dark green, red/orange, and total), legumes, nuts, dairy, fish, red meat, eggs, oils, added sugars, and ultra-processed foods. The chart shows how well the study participants adhered to each component of the PHDI. Chart (B) presents similar information for the Cardiovascular Health Diet Index (CHDI), showing mean scores and standard deviations for fruits, vegetables, fish and seafood, whole grains, legumes, nuts, dairy, red meat, sugar-sweetened beverages, processed meat, and ultra-processed foods. This chart helps visualize the participants' adherence to a heart-healthy diet.
Text: "The mean PHDI was 29.4 points (95% CI 28.04:30.81), on a total score ranging from 0 to 150, with higher scores for fruits, vegetables, legumes, oils, and dairy, respectively (Fig. 2A)."
Context: The mean PHDI was 29.4 points (95% CI 28.04:30.81), on a total score ranging from 0 to 150, with higher scores for fruits, vegetables, legumes, oils, and dairy, respectively (Fig. 2A). The mean CHDI was 32.63 points (95% CI 31.50:33.78), on a total score ranging from 0 to 110, with higher scores for legumes, fruits, vegetables, and dairy, respectively (Fig. 2B).
Relevance: Figure 2 is essential for understanding the dietary patterns of the study participants. It visually represents their adherence to both the PHDI (focused on sustainability) and the CHDI (focused on cardiovascular health), allowing for a comparison of their performance on these two indices and highlighting areas where their diets align with or deviate from the recommendations.
Table 2 displays the statistical association between the Planetary Health Diet Index (PHDI) and various components of the Cardiovascular Health Diet Index (CHDI). It shows the beta coefficients (β), representing the strength and direction of the association, along with their 95% confidence intervals (CI) and p-values. A positive beta indicates a positive association, while a negative beta indicates a negative association. The table also provides the R-squared value, indicating the proportion of variance in PHDI explained by the CHDI components. For example, the table shows a positive association between PHDI and CHDI total score, fruits, vegetables, and legumes, and a negative association with ultra-processed foods (UPF).
Text: "No significant associations were found for fish and seafood, red meats, sugar-sweetened beverages, whole cereals, nuts, processed meats, and dairy products (Table 2)."
Context: The final score of the CHDI was positively associated with the PHDI (β = 0.12, 95% CI 0.31–0.54), as well as with fruits (β = 0.60, 95% CI 0.16–1.04), vegetables (β = 1.22, 95% CI 0.65–1.75), and legumes (β = 0.64, 95% CI 0.08–1.21). Conversely, the PHDI was negatively associated with the consumption of UPF (β = -1.28, 95% CI -2.24:-0.33). No significant associations were found for fish and seafood, red meats, sugar-sweetened beverages, whole cereals, nuts, processed meats, and dairy products (Table 2).
Relevance: Table 2 is crucial for understanding the relationship between adherence to a sustainable diet (PHDI) and adherence to a heart-healthy diet (CHDI). It quantifies the associations between the overall PHDI score and the individual components of the CHDI, providing insights into which aspects of a heart-healthy diet are most strongly related to a sustainable dietary pattern.
Table 3 presents the contribution of ultra-processed foods (UPF) to the total caloric intake of participants in the Brazuca-Natal Study. The table is organized by the NOVA score, a system for classifying foods based on their level of processing. Each row represents a different NOVA score, ranging from 0 to 7, indicating the number of UPF 'points' present in a participant's diet. The table shows the number (N) and percentage of participants with each score and the corresponding percentage of their total caloric intake derived from UPF, presented with a 95% confidence interval. For example, participants with a NOVA score of 0 (no UPF points) derived approximately 9.78% of their calories from UPF, while those with a score of 7 derived close to 39.01% of their calories from UPF. The table demonstrates a clear trend: as the NOVA score increases (more UPF points), the percentage of calories from UPF also increases.
Text: "Table 3 quantitatively provides, through the NOVA score, the UPF consumed and their percentage of caloric contribution by participants of the Brazuca-Natal Study, Brazil (2019–2020)."
Context: The final score of the CHDI was positively associated with the PHDI (β = 0.12, 95% CI 0.31–0.54), as well as with fruits (β = 0.60, 95% CI 0.16–1.04), vegetables (β = 1.22, 95% CI 0.65–1.75), and legumes (β = 0.64, 95% CI 0.08–1.21). Conversely, the PHDI was negatively associated with the consumption of UPF (β = -1.28, 95% CI -2.24:-0.33). No significant associations were found for fish and seafood, red meats, sugar-sweetened beverages, whole cereals, nuts, processed meats, and dairy products (Table 2). Table 3 quantitatively provides, through the NOVA score, the UPF consumed and their percentage of caloric contribution by participants of the Brazuca-Natal Study, Brazil (2019–2020). It can be observed that the higher the quantitative participation of UPF in the diet, the more significant their caloric contribution.
Relevance: This table is relevant because it quantifies the relationship between UPF consumption and total caloric intake, providing evidence for the negative impact of UPF on diet quality. It supports the study's objective of assessing the association between diet and cardiometabolic risk factors.
Figure 3 is a horizontal bar chart illustrating the frequency of consumption of various ultra-processed foods (UPF) among participants in the Brazuca-Natal Study. The chart lists different UPF categories on the vertical axis, such as 'Packaged snacks, shoestring potatoes, and crackers,' 'Margarine,' 'Sausage, hamburger, or nuggets,' etc. The horizontal axis represents the percentage of participants who consumed each food item. The longest bar, representing 'Packaged snacks,' indicates the highest consumption frequency, followed by 'Margarine' and 'Processed meats.' The chart visually demonstrates the prevalence of different UPF categories in the study population's diet.
Text: "In Fig. 3, the frequency of ultra-processed food consumption according to the NOVA score shows a higher frequency of consumption of ultra-processed foods such as packaged snacks, shoestring potatoes, and crackers (16.94%), followed by margarine (14.14%) and processed meats (13.25%) among adults and elderly individuals in the Brazuca-Natal study."
Context: Table 3 quantitatively provides, through the NOVA score, the UPF consumed and their percentage of caloric contribution by participants of the Brazuca-Natal Study, Brazil (2019–2020). It can be observed that the higher the quantitative participation of UPF in the diet, the more significant their caloric contribution. In Fig. 3, the frequency of ultra-processed food consumption according to the NOVA score shows a higher frequency of consumption of ultra-processed foods such as packaged snacks, shoestring potatoes, and crackers (16.94%), followed by margarine (14.14%) and processed meats (13.25%) among adults and elderly individuals in the Brazuca-Natal study.
Relevance: Figure 3 provides a clear visual representation of the most frequently consumed UPF categories in the study population, highlighting the prevalence of these foods in their diets. This information is crucial for understanding the context of UPF consumption and its potential impact on health outcomes.
Table 4 presents the results of a linear regression analysis exploring the association between the Planetary Health Diet Index (PHDI) and various cardiometabolic risk factors. The analysis is adjusted for age, income, and BMI. The table is divided into two models. Model 1 examines continuous variables including diastolic and systolic blood pressure (DBP and SBP), fasting blood glucose, total cholesterol, LDL-c (low-density lipoprotein cholesterol), HDL-c (high-density lipoprotein cholesterol), and triglycerides. Model 2 examines categorical variables including diabetes mellitus, dyslipidemia, and arterial hypertension. For each variable, the table presents the beta coefficient (β), the 95% confidence interval (CI), and the p-value. The R-squared value is provided for each model.
Text: "From the results obtained in Table 4, it can be inferred that when using the linear regression model adjusted for age, income, and BMI, individuals had a negative association with SBP (mmHg) (β -0.14, 95% CI -0.25 to -0.01), total cholesterol (mg/dL) (β -0.80, 95% CI -1.31 to -0.46), and LDL-c (mg/dL) (β -0.10, 95% CI -0.19 to -0.08) with p < 0.05."
Context: From the results obtained in Table 4, it can be inferred that when using the linear regression model adjusted for age, income, and BMI, individuals had a negative association with SBP (mmHg) (β -0.14, 95% CI -0.25 to -0.01), total cholesterol (mg/dL) (β -0.80, 95% CI -1.31 to -0.46), and LDL-c (mg/dL) (β -0.10, 95% CI -0.19 to -0.08) with p < 0.05. No association was found for DBP, fasting blood glucose (mg/dL), HDL-c (mg/dL), and triglycerides (mg/dL). We also observed in Table 4 that the sustainability index of the diet is 5.58 points lower for individuals with type 2 diabetes (95% CI -10.20:-0.95), and similarly, people with dyslipidemia have 4.19 points less (95% CI -8.48:-0.09) (p < 0.05). No association was found between PHDI and self-reported hypertension (R²=0.47).
Relevance: Table 4 is highly relevant as it presents the main findings of the study regarding the association between PHDI and cardiometabolic risk factors. It allows readers to assess the statistical significance and magnitude of these associations, which are central to the research question.
This discussion section analyzes the study results within the context of planetary health, which emphasizes the interconnectedness of human actions, ecosystems, and human well-being. The study found a significant association between adherence to the EAT-Lancet diet (measured by the PHDI) and improved cardiovascular health indicators (lower SBP, total cholesterol, and LDL-c). Individuals with type 2 diabetes and dyslipidemia had lower PHDI scores. The discussion explores the potential mechanisms behind this synergy, including increased fiber intake, weight management, and improved gut microbiota. It also examines the role of specific food groups, highlighting the positive contributions of fruits, vegetables, and legumes, and the negative impact of ultra-processed foods (UPF). The discussion acknowledges the challenges of adopting sustainable diets, particularly in developing countries like Brazil, where socioeconomic inequalities and cultural influences play a significant role. It concludes by emphasizing the need for integrated approaches to promote both healthy and sustainable dietary habits, including policy interventions.
The discussion effectively places the study's findings within the broader framework of planetary health, highlighting the interconnectedness of human health and environmental sustainability. This adds depth and relevance to the research.
The discussion goes beyond simply reporting the associations and delves into the potential mechanisms by which sustainable diets can improve cardiovascular health. This strengthens the interpretation of the findings.
The discussion acknowledges the challenges of adopting sustainable diets, particularly in developing countries, and discusses the limitations of the study's cross-sectional design. This enhances the discussion's transparency and scientific rigor.
While the discussion mentions the negative impact of UPF, it could be strengthened by providing more specific examples of how UPF consumption contributes to poor cardiovascular health and environmental problems. Connecting UPF consumption to specific mechanisms, such as inflammation or gut dysbiosis, would be beneficial.
Rationale: A more detailed discussion of UPF would provide a more comprehensive understanding of its negative impacts and strengthen the argument for promoting sustainable diets.
Implementation: Provide specific examples of how UPF contributes to poor health and environmental problems. Discuss the mechanisms by which UPF exerts these negative effects, such as its impact on inflammation, gut microbiota, or resource use.
The discussion briefly mentions the importance of policy interventions but could be expanded to provide more specific recommendations for promoting sustainable diets. Discussing specific policy options, such as food labeling regulations, taxes on unhealthy foods, or subsidies for healthy foods, would be valuable.
Rationale: Elaborating on policy implications would make the discussion more impactful and provide actionable recommendations for promoting sustainable diets.
Implementation: Discuss specific policy options that could be implemented to encourage sustainable dietary choices. Consider examples from other countries or regions that have successfully implemented such policies.
While the discussion acknowledges the study's limitations, it could be strengthened by explicitly connecting these limitations to the interpretation of the findings. For example, discussing how the cross-sectional design limits the ability to draw causal inferences would be helpful.
Rationale: Connecting the limitations to the interpretation of the findings would enhance the discussion's scientific rigor and provide a more nuanced perspective on the results.
Implementation: Explicitly discuss how each limitation might affect the interpretation of the findings and suggest ways to address these limitations in future research.
The study concludes that adopting a sustainable diet, like the EAT-Lancet diet, is associated with better cardiovascular health indicators. This reinforces the idea that diets that are good for the planet are also good for people. The study also found a link between higher ultra-processed food (UPF) consumption and lower adherence to sustainable diets. The authors suggest that access to diverse food production, processing, and marketing is essential for achieving both human and planetary health. They emphasize the importance of integrating healthy and sustainable dietary guidelines and highlight the role of government food policies in promoting sustainable eating.
The conclusion effectively summarizes the main findings of the study in a clear and concise manner, highlighting the link between sustainable diets and improved health indicators.
The conclusion reinforces the important message that diets beneficial for the environment are also beneficial for human health, aligning with the principles of planetary health.
The conclusion emphasizes the role of government food policies in promoting sustainable eating, which is a crucial aspect of translating research findings into real-world action.
While the conclusion mentions the importance of policy interventions, it would be stronger if it provided more specific policy recommendations. For example, suggesting specific policies related to food labeling, taxation, or subsidies could be helpful.
Rationale: Providing specific policy recommendations would make the conclusion more actionable and potentially influence policy decisions.
Implementation: Include examples of specific policies that could be implemented to promote sustainable diets, such as taxes on unhealthy foods, subsidies for healthy and sustainable foods, or regulations on food marketing and advertising.
The conclusion could be broadened to discuss the wider implications of the findings beyond the specific study population. For example, discussing how the findings could inform global efforts to promote sustainable diets and address climate change would be valuable.
Rationale: Discussing the broader implications would enhance the significance of the study and its potential impact on a larger scale.
Implementation: Connect the findings to global challenges related to food systems, environmental sustainability, and public health. Discuss how the study's results could contribute to addressing these challenges.
The conclusion would benefit from briefly acknowledging the study's limitations, such as its cross-sectional design and the challenges related to data collection during the pandemic. Suggesting future research directions, such as longitudinal studies or interventions to promote sustainable diets, would also strengthen the conclusion.
Rationale: Addressing limitations and suggesting future research directions would enhance the scientific rigor of the conclusion and provide a roadmap for future studies.
Implementation: Briefly mention the study's limitations and their potential impact on the interpretation of the findings. Suggest specific research questions or study designs that could address these limitations and further explore the relationship between sustainable diets and health outcomes.
This section outlines the ethical considerations and potential conflicts of interest related to the research. It states that the study received ethical approval, participants provided informed consent, and the authors declare no competing interests.
The section clearly states that the study received ethical approval, including the specific approval number. This transparency is essential for ensuring ethical conduct in research.
The section explicitly mentions that participants provided informed consent, which is a cornerstone of ethical research. This demonstrates respect for participants' autonomy and their right to make informed decisions about their involvement in the study.
The section includes a clear declaration of competing interests, stating that the authors have no competing interests to declare. This transparency is crucial for maintaining the integrity and credibility of the research.
While the section mentions informed consent, it would be beneficial to specify the type of consent obtained (e.g., written, verbal, or implied). Providing more detail about the consent process would further enhance transparency.
Rationale: Specifying the type of consent provides a more complete picture of the ethical considerations and strengthens the study's ethical rigor.
Implementation: Clarify the type of informed consent obtained from the participants, such as written informed consent, and briefly describe the process by which consent was obtained.
Including a statement about data availability would enhance the transparency and reproducibility of the research. This could involve specifying where the data is stored and how it can be accessed by other researchers.
Rationale: Providing information about data availability promotes open science practices and allows other researchers to verify the study's findings or conduct secondary analyses.
Implementation: Add a statement indicating whether the data is available and, if so, where it can be accessed. If the data is not publicly available, explain the reasons for its restricted access.