Association between Adherence to the EAT-Lancet Diet and Cardiometabolic Risk Factors in Brazilian Adults and Elderly

Table of Contents

Overall Summary

Overview

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.

Key Findings

Strengths

Areas for Improvement

Significant Elements

Table 1

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.

Figure 2

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.

Conclusion

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.

Section Analysis

Abstract

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Materials and methods

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

figure 1

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.

First Mention

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.

Critique
Visual Aspects
  • The infographic is clear and well-organized, making it easy to understand the scoring criteria for each component.
  • The use of icons helps to quickly identify each food group.
  • The color-coding (green for the central circle) is visually appealing.
Analytical Aspects
  • The infographic effectively conveys the relative importance of different food groups in the CHDI.
  • The scoring system is transparent and easy to follow.
  • The infographic could benefit from a brief explanation of why these specific food groups are included in the index and how they relate to cardiovascular health.
Numeric Data
  • Fruits: 340 g/day
  • Vegetables: 180 g/day
  • Fish and Seafood: 28.6 g/day
  • Whole Grains: 90 g/day
  • Legumes: 80 g/day
  • Nuts: 12.9 g/day
  • Dairy: 250 g/day
  • Red Meat: 28.6 g/day
  • Sugar-Sweetened Beverages (SSB): 142.9 g/day
  • Processed Meat: 12.9 g/day
  • Ultra-Processed Food (UPF): 4 points

Results

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

table 1

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.

First Mention

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.

Critique
Visual Aspects
  • The table is well-organized and easy to read, with clear labels for each variable and PHDI tercile.
  • The use of both 'n' and 'npond' (weighted n) is helpful for understanding the sample distribution, but a brief explanation of the weighting procedure in the table caption would be beneficial.
  • The table could be improved by using clearer formatting to distinguish between the three terciles, such as shading or borders.
Analytical Aspects
  • The inclusion of p-values helps to identify statistically significant differences between the terciles.
  • The table provides a comprehensive overview of the study population, covering a wide range of characteristics.
  • The table would be even more informative if it included effect sizes (e.g., odds ratios or mean differences) along with the p-values to quantify the magnitude of the differences between terciles.
table 1 (continued)

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.

First Mention

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.

Critique
Visual Aspects
  • The table maintains the clear and organized format of the first part of Table 1.
  • The consistent use of 'n', 'npond', and percentages facilitates comparison across variables and terciles.
  • Similar to the first part, clearer visual separation between terciles would improve readability.
Analytical Aspects
  • The inclusion of p-values allows for the identification of statistically significant differences between PHDI terciles for the additional variables.
  • Presenting CHDI scores categorized by PHDI terciles helps to explore the relationship between these two dietary indices.
  • Including effect sizes alongside p-values would enhance the interpretation of the results by quantifying the magnitude of the differences between terciles.
figure 2

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.

First Mention

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.

Critique
Visual Aspects
  • The radar chart format effectively displays the multi-dimensional data for both the PHDI and CHDI.
  • The axes are clearly labeled, making it easy to identify each food group or component.
  • The inclusion of standard deviations provides a sense of the variability in scores.
Analytical Aspects
  • The charts clearly show which components contribute most to the overall PHDI and CHDI scores.
  • Comparing the two charts allows for insights into the overlap and differences between the two dietary indices.
  • The charts could be improved by adding a reference line or area representing the recommended intake levels for each component, making it easier to assess the participants' adherence to the guidelines.
table 2

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).

First Mention

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.

Critique
Visual Aspects
  • The table is well-organized and easy to read.
  • The use of clear labels and abbreviations makes it easy to understand the variables and statistical measures.
  • The table could benefit from highlighting the statistically significant associations in bold or with asterisks.
Analytical Aspects
  • The table effectively presents the results of the regression analysis, showing the strength and direction of the associations.
  • The inclusion of confidence intervals and p-values provides information about the statistical significance and precision of the estimates.
  • The table could be improved by providing a brief explanation of the meaning and interpretation of the beta coefficients and R-squared value in the context of the study.
Numeric Data
  • CHDI (Total): 0.12
  • Fruits: 0.6
  • Vegetables: 1.22
  • Fish and Seafood: 0.061
  • Red Meat: -0.35
  • SSB: 0.11
  • Whole Cereals: 0.49
  • Legumes: 0.64
  • Nuts: 0.96
  • Processed Meat: 0.05
  • Dairy: -0.09
  • UPF: -1.28
  • R-squared: 0.482
table 3

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.

First Mention

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.

Critique
Visual Aspects
  • The table is clear and easy to read.
  • The use of confidence intervals provides a measure of uncertainty around the estimates.
  • The table could be improved by including a visual representation of the trend, such as a line graph showing the relationship between NOVA score and UPF caloric contribution.
Analytical Aspects
  • The table effectively presents the data and supports the study's conclusions.
  • The use of the NOVA score provides a standardized way to categorize UPF consumption.
  • The table could benefit from a more detailed explanation of the NOVA score and its implications for health.
Numeric Data
  • NOVA Score 0: 9.78 %
  • NOVA Score 1: 13.57 %
  • NOVA Score 2: 19.01 %
  • NOVA Score 3: 23.55 %
  • NOVA Score 4: 31.93 %
  • NOVA Score 5: 38.49 %
  • NOVA Score 6: 39.01 %
figure 3

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.

First Mention

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.

Critique
Visual Aspects
  • The horizontal bar chart is an effective way to display the frequency of consumption for each UPF category.
  • The labels are clear and easy to read.
  • The chart could be improved by ordering the bars from highest to lowest frequency for easier comparison.
Analytical Aspects
  • The chart clearly shows the relative consumption frequencies of different UPF categories.
  • The chart could benefit from including the actual number of participants who consumed each food item, in addition to the percentages.
  • The chart could also be enhanced by linking the UPF categories to their corresponding NOVA scores, providing a more complete picture of UPF consumption patterns.
Numeric Data
  • Packaged snacks, shoestring potatoes, and crackers: 16.94 %
  • Margarine: 14.14 %
  • Processed meats: 13.25 %
table 4

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.

First Mention

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.

Critique
Visual Aspects
  • The table is well-organized and easy to read, with clear labels for variables, coefficients, confidence intervals, and p-values.
  • The separation into two models for continuous and categorical variables improves clarity.
  • The table could benefit from highlighting the statistically significant results in bold or another visual cue.
Analytical Aspects
  • The table clearly presents the results of the linear regression analysis, allowing readers to understand the direction and strength of the associations.
  • The inclusion of confidence intervals provides information about the precision of the estimates.
  • The table could be improved by providing a brief explanation of the meaning and interpretation of the beta coefficients in the context of the study. For example, explaining that a negative beta for SBP means that a higher PHDI score is associated with lower SBP.
Numeric Data
  • Model 1 R-squared: 0.427
  • DBP Beta: 0.46
  • DBP p-value: 0.044
  • SBP Beta: -0.14
  • SBP p-value: 0.016
  • Fasting Blood Glucose Beta: 0.12
  • Fasting Blood Glucose p-value: 0.514
  • Total Cholesterol Beta: -0.8
  • Total Cholesterol p-value: 0.021
  • LDL-c Beta: -0.1
  • LDL-c p-value: 0.026
  • HDL-c Beta: -0.02
  • HDL-c p-value: 0.928
  • Triglycerides Beta: -0.21
  • Triglycerides p-value: 0.227
  • Model 2 R-squared: 0.474
  • Diabetes Mellitus Beta: -5.58
  • Diabetes Mellitus p-value: 0.013
  • Dyslipidemia Beta: -4.19
  • Dyslipidemia p-value: 0.041
  • Arterial Hypertension Beta: -1.63
  • Arterial Hypertension p-value: 0.45

Discussion

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Conclusions

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Declarations

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

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