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JAMA Network Open
JAMA Netw Open. 2024 Feb; 7(2): e2354473.
Published online 2024 Feb 7. doi:10.1001/jamanetworkopen.2023.54473
PMCID: PMC10851095
PMID: 38324314
A Secondary Analysis of the GALIAT Cluster Randomized Clinical Trial
Cristina Cambeses-Franco, MSc,1 Francisco Gude, PhD,2 Alfonso J. Benítez-Estévez, PhD,3 Sara González-García, PhD,1 Rosaura Leis, PhD,4 Juan Sánchez-Castro, MD,5,6 María Teresa Moreira, PhD,1 Gumersindo Feijoo, PhD,1 and Mar Calvo-Malvar, PhD
3
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Associated Data
- Supplementary Materials
Key Points
Question
Could traditional dietary patterns effectively address the complex relationship between environmental sustainability and the incidence of diet-related diseases like metabolic syndrome?
Findings
In this secondary analysis of a cluster randomized clinical trial including 574 participants, a traditional Atlantic dietary intervention significantly reduced the incidence of metabolic syndrome. There was no statistically significant difference in the reduction in dietary carbon footprint emissions in the intervention group compared with the control group.
Meaning
These findings suggest that traditional diets could serve as valuable tools to promote the convergence of human and planetary health, making them noteworthy models of sustainable and healthy dietary patterns.
This secondary analysis of a cluster randomized clinical trial assesses the effects of the Atlantic diet on metabolic syndrome incidence and the environmental carbon footprint.
Abstract
Importance
The universal call to action for healthier and more sustainable dietary choices is the framework of the United Nations’s Sustainable Development Goals. The Atlantic diet, originating from the northwest of the Iberian Peninsula, represents an example of a traditional diet that aligns with these principles.
Objective
To explore a 6-month intervention based on the Atlantic diet’s effects on metabolic and environmental health, assessing metabolic syndrome (MetS) incidence and the carbon footprint.
Design, Setting, and Participants
The Galician Atlantic Diet study was a 6-month randomized clinical trial designed to assess the effects of this regional traditional diet on families’ eating habits. The study was conducted from March 3, 2014, to May 29, 2015, at a local primary health care center in the rural town of A Estrada in northwestern Spain and involved a multisectoral collaboration. Families were randomly selected from National Health System records and randomized 1:1 to an intervention or control group. This secondary analysis of the trial findings was performed between March 24, 2021, and November 7, 2023.
Interventions
Over 6 months, families in the intervention group received educational sessions, cooking classes, written supporting material, and foods characteristic of the Atlantic diet, whereas those randomized to the control group continued with their habitual lifestyle.
Main Outcomes and Measures
The main outcomes were MetS incidence, defined per National Cholesterol Education Program Adult Treatment Panel III guidelines, and carbon footprint emissions as an environmental metric using life cycle assessment with daily dietary intake as the functional unit.
Results
Initially, 250 families were randomized (574 participants; mean [SD] age, 46.8 [15.7] years; 231 males [40.2%] and 343 females [59.8%]). The intervention group included 126 families (287 participants) and the control group, 124 families (287 participants). Ultimately, 231 families completed the trial. The intervention significantly reduced the risk of incident cases of MetS (rate ratio, 0.32; 95% CI, 0.13-0.79) and had fewer MetS components (proportional odds ratio, 0.58; 95% CI, 0.42-0.82) compared with the control condition. The intervention group did not have a significantly reduced environmental impact in terms of carbon footprint emissions compared with the control group (−0.17 [95% CI, −0.46 to 0.12] kg CO2 equivalents/person/d).
Conclusions and Relevance
These findings provide important evidence that a family-focused dietary intervention based on a traditional diet can reduce the risk of incident MetS. Further research is needed to understand the underlying mechanisms and determine the generalizability to other populations, taking into account regional cultural and dietary variations.
Trial Registration
ClinicalTrials.gov Identifier: NCT02391701
Introduction
Diets worldwide are changing, often with a negative impact on human and planetary health. To achieve the United Nations’s Sustainable Development Goals (SDGs) on noncommunicable disease reduction (SDG 3) and climate change mitigation (SDG 13), a shift to sustainable, healthy dietary patterns is necessary.1 These diets should support health and be environmentally friendly, accessible, affordable, safe, equitable, and culturally acceptable.1 Some traditional regional diets, such as the Atlantic diet, align with this concept. The Atlantic diet shares similarities with the Mediterranean diet and has been linked to lower metabolic risk factors2,3,4,5,6 and environmental benefits.7
We hypothesize that a shift to a traditional Atlantic diet would not only improve population health but also contribute to the sustainability of the environment, while taking into account the identity and diversity of the food system and culture of northwest Spain and northern Portugal. To date, no clinical trial has been conducted that assesses the effects of a traditional diet (ie, the Atlantic diet) from a dual-sustainability perspective on human and environmental health, thus addressing 2 of the United Nations’s SDGs. Therefore, in this report, we address 2 questions: (1) whether an intervention based on a traditional Atlantic diet can reduce the incidence of new cases of metabolic syndrome (MetS) or contribute to a higher resolution of existing cases compared with a general population and (2) whether this intervention would result in a lower environmental impact.
Methods
Study Design
This study is a post hoc secondary analysis of the Galicia Atlantic Diet (GALIAT) study, a 6-month, community-focused randomized clinical trial designed to assess the effects of the traditional Atlantic diet on metabolic health and dietary habits in families. The GALIAT trial was performed from March 3, 2014, to May 29, 2015, at the local primary health care center in the rural town of A Estrada in northwestern Spain. This trial was grounded in the traditional diet of the study area and entailed a collaborative, multisectoral approach involving citizens, local businesses, researchers, and public institutions.8 The trial was deliberately shaped to be pragmatic, prioritizing effectiveness and practicality in primary care settings.9
Study procedures are detailed in previous publications,6,8 and the study protocol is provided in Supplement 1. The Galacian Autonomic Committee for Research Ethics approved the trial (code 2013/531) and the current study. During the preassessment session, participants were briefed on the project’s objectives, informed about subject selection methods, given detailed information on participation requirements, and acquainted with their rights as participants. Additionally, comprehensive project documentation, along with informed consent forms for all family members (including adults, children, and guardians) were provided. Participants who chose to participate further were then scheduled for an initial evaluation at the health center where, accompanied by their families, they submitted signed informed consent forms and underwent evaluations to confirm compliance with the inclusion/exclusion criteria. The trial was performed in accordance with the Declaration of Helsinki and the principles of Good Clinical Practice. The study was retrospectively registered with ClinicalTrials.gov (NCT02391701) and followed the Consolidated Standards of Reporting Trials (CONSORT) 2010 guidelines10 and the Template for Intervention Description and Replication (TIDieR) checklist.11 The current data analysis was performed between March 24, 2021, and November 7, 2023.
Participants
Recruitment within the community was performed through a random representative sample of 3500 individuals, aged 18 to 85 years and stratified by decades of age, drawn from the Spanish National Health System Register of a single rural population of approximately 20 000 inhabitants. These index individuals and their household family members were invited to participate if they lived in a family unit of at least 2 members aged 3 to 85 years; were not currently taking lipid-lowering medication; were not pregnant; and did not have alcoholism,12 major cardiovascular disease, dementia, or a life expectancy of less than 1 year, which specifically refers to individuals with a terminal disease (Supplement 1). The study comprised participants of Spanish ethnicity and Caucasian descent. Fieldwork personnel gathered data on the specific birthplaces and family histories of the participants.
Randomization and Procedures
Families were randomly assigned in a 1:1 ratio to the intervention or control group using a computer-generated random number table. The dietary intervention was based on the Atlantic diet, the traditional diet in northwestern Spain and Portugal, which is composed of local, fresh, and minimally processed seasonal foods like vegetables, fruits, whole grains, beans, and olive oil. The Atlantic diet also features high fish and seafood consumption, along with starch-based products, dried fruits (particularly chestnuts), milk, cheese, and moderate meat and wine intake.8,13
The dietary counseling aimed to modify food habits in accordance with the characteristics of the Atlantic diet but not necessarily to restrict energy intake. Dietary recommendations were adapted to conform to the preferences and nutritional needs of each participant. In the intervention group, families attended 3 nutrition education sessions at the health care center and received additional support, including a cooking class, written materials, and regular food baskets with traditional Atlantic diet items (eFigure 1 in Supplement 2). Control group participants were advised to maintain their usual lifestyle. Previous articles assessed dietary patterns, with baseline values showing similarity (eMethods in Supplement 2).9
At the baseline and after 6 months, information was collected on dietary intake, physical activity,14 medication use, and other variables. Masking procedures were implemented at the randomization and data entry stages to minimize potential biases. However, due to the nature and design of our study, masking at the intervention level was not feasible.
Outcomes
Definition of MetS
Metabolic syndrome was defined according to criteria from the National Cholesterol Education Program Adult Treatment Panel III (ATP III) guidelines15 and based on meeting at least 3 of the following 5 criteria: (1) waist circumference greater than 102 cm for men or greater than 88 cm for women, (2) triglyceride levels of 150 mg/dL or higher (≥1.7 mmol/L), (3) high-density lipoprotein cholesterol less than 40 mg/dL (<1.03 mmol/L) for men or less than 50 mg/dL (<1.3 mmol/L) for women, (4) blood pressure of 130/85 mm Hg or higher, and (5) fasting glucose level of 110 mg/dL or higher (≥6.2 mmol/L). Participants who were being treated with antidiabetic, antihypertensive, or triglyceride-lowering medications were classified as positive for the respective criterion. We investigated (1) whether the intervention can reduce the incidence of new cases (among those participants without MetS at baseline) and (2) whether there were differences in the prevalence of MetS and its components (among all-comers) at the end of follow-up between the intervention and control groups.
Metabolic Variables
Metabolic variables were measured at the primary health care center. All anthropometric measurements were made in triplicate (eMethods in Supplement 2).
Dietary Intake
A 3-day food diary was used to collect dietary intake data (eMethods in Supplement 2). Nutritionists verified all records completed by participants in their presence. Dietary intake was analyzed using DIAL, version 3.3.5.0 professional nutrition analysis software.16
Carbon Footprint
Life Cycle Assessment
Life cycle assessment (LCA) is a method of quantifying the environmental impacts of the life cycle of products, processes, or services.17 The most common environmental impact category assessed in LCA is the carbon footprint, the best-known indicator of climate change.
Functional Unit
The LCA approach was used to calculate each participant’s dietary carbon footprint, relative to a functional unit, as a baseline for all calculations. The functional unit chosen was individual daily dietary intake based on the results of the dietary questionnaires.
Methodology
The carbon footprint framework assessed CO2 emissions associated with each participant’s diet. This well-established indicator was used while considering variations in LCA study boundaries. Our approach focused on the life cycle from food production to household consumption, with adjustments for supply chain food losses and waste (eMethods and eFigure 2 in Supplement 2).18,19,20,21,22,23
Statistical Analysis
Sample size was determined for the primary outcome (Supplement 1). Analyses were conducted for the intention-to-treat population, with missing data imputed using the multivariable imputation by chained equations method.24 The main analysis used multiple imputation using Markov chain Monte Carlo method with 30 imputations for each missing measurement. A per-protocol sensitivity analysis was also performed. Baseline comparability was assessed using χ2 tests (categorical variables) and Student t tests (continuous variables). Modified Poisson regressions with robust error variance were used to estimate rate ratios (RRs) and their 95% CIs for new MetS cases (in participants without MetS) in the intervention group compared with the control group25 after adjusting for age and sex. For exploratory purposes, point prevalences (with 95% CIs) for MetS and its components at baseline and after 6 months were estimated.
Cumulative logit regression models for ordered categories were constructed to examine the effect of the intervention on the number of MetS risk factors as an ordinal variable ranked from 0 to 5 compared with control condition. Mixed-effects linear models were used to evaluate changes in carbon footprint emissions from baseline to the 6-month follow-up in all randomized participants, as well as in those who completed the study. These models were adjusted for age, sex, and baseline values, with intervention as a fixed effect and family as the random effect. The intraclass correlation coefficient (ICC) was calculated to examine how much variance in the carbon footprint outcome was explained by the family cluster effect. All analyses were conducted using Stata, version 16.0 statistical software (StataCorp LLC). The χ2 test was used for differences between categorical variables, and the Student t test for continuous variables. All statistical tests were 2-sided, and P < .05 was deemed statistically significant.
Results
Baseline Characteristics
Of 250 families (720 participants) who were recruited and randomized, a total of 121 families (270 adults [aged ≥18 years]) in the intervention group and 110 (248 adults) in the control group completed the trial (Figure 1). Briefly, all participants were White, the mean (SD) number of persons per family unit was 2.3 (0.8), the mean (SD) participant age was 46.8 (15.7) years, and 231 participants (40.2%) were male and 343 (59.8%) female. As previously reported,6 the randomization produced 2 groups with similar characteristics of baseline sociodemographic and potential confounding factors, including education, smoking, alcohol intake, and physical activity, with the exception of age, which was significantly higher in the intervention group (mean [SD], 48.2 [15.8] vs 45.3 [15.4] years for the intervention and control groups, respectively) (Table 1). All individuals in the intervention group participated in the nutrition education program. A total of 241 of 287 (84.0%) participants in the intervention arm and 238 of 287 (82.9%) in the control group filled out the 3-day food record completely at baseline and 6 months.
Figure 1.
Overview of the Study Population
Table 1.
Baseline Characteristics of the Participants in the Study
| Characteristic | No. (%)a | |
|---|---|---|
| Control group | Intervention group | |
| No. of participants | ||
| Families | 124 | 126 |
| Individuals | 287 | 287 |
| Participants per family, mean (SD) | 2.3 (0.8) | 2.3 (0.7) |
| Sex | ||
| Male | 114 (39.7) | 117 (40.8) |
| Female | 173 (60.3) | 170 (59.2) |
| Age, mean (SD), y | 45.3 (15.4) | 48.2 (15.8) |
| Marital status | ||
| Married or with partner | 195 (67.8) | 211 (73.4) |
| Divorced, separated, or widowed | 33 (11.5) | 28 (9.8) |
| Single | 59 (20.6) | 48 (16.8) |
| Educational level | ||
| None | 29 (10.1) | 30 (10.5) |
| Elementary | 120 (41.8) | 103 (35.9) |
| Secondary | 91 (31.7) | 103 (35.9) |
| University or higher | 47 (16.4) | 51 (17.7) |
| Employment status | ||
| Employed | 148 (51.6) | 139 (48.4) |
| Retired | 40 (13.9) | 56 (19.5) |
| Otherb | 99 (34.5) | 92 (32.1) |
| Smoking status | ||
| Never | 128 (44.6) | 120 (41.8) |
| Former | 50 (17.4) | 71 (24.7) |
| Current | 109 (38.0) | 96 (33.5) |
| Alcohol intake | ||
| Abstinent | 125 (43.5) | 124 (43.2) |
| Light (1-140 g/wk) | 134 (46.6) | 130 (45.3) |
| Heavy (>140 g/wk) | 28 (9.9) | 33 (11.5) |
| Comorbidities | ||
| Cardiovascular disease | 42 (16.0) | 49 (18.3) |
| Cerebrovascular accident | 3 (1.1) | 3 (1.1) |
| Diabetes | 16 (5.9) | 16 (5.9) |
| Current medications | ||
| Cholesterol lowering | 23 (8.7) | 32 (12.5) |
| Antihypertensives | 44 (18.1) | 56 (24.2) |
| International Physical Activity Questionnaire | ||
| Inactive | 56 (19.8) | 44 (15.3) |
| Minimally active | 68 (24.6) | 85 (29.6) |
| Active | 163 (55.6) | 158 (55.1) |
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aNo significant differences were found between the 2 groups, with the exception of age, which was significantly higher in the intervention group (χ2 test for differences between categorical variables and Student t test for continuous variables).
bThe other category accounts for diverse occupational situations not explicitly classified under employed or retired (eg, students, subsistence farmers, individuals with disabilities).
Effect of the GALIAT Intervention on MetS and Its Components
Of the 457 participants without MetS at the beginning of the trial, 23 developed MetS during the 6-month follow-up (6 [2.7%] in the intervention group; 17 [7.3%] in the control group). There was a significant reduction in incident MetS cases for the intervention group (RR, 0.32; 95% CI, 0.13-0.79) compared with the control group (Table 2).
Table 2.
Incidence Rate, Prevalence Rate, and Rate Ratios (RRs) of Metabolic Syndrome (MetS) and Its Components by Treatment Groupa
| Criterionb | % (95% CI) | RR (95% CI)c | P value | |||
|---|---|---|---|---|---|---|
| Control group (n = 287) | Intervention group (n = 287) | |||||
| Baseline | 6 mo | Baseline | 6 mo | |||
| Abdominal obesity | 40.1 (34.5-45.8) | 41.8 (36.1-47.5) | 51.9 (46.1-57.7) | 47.0 (41.3-52.8) | 0.90 (0.82-1.00) | .04 |
| Hypertriglyceridemia | 18.5 (14.0-23.0) | 17.4 (13.0-21.8) | 13.9 (9.9-18.0) | 13.9 (9.9-18.0) | 0.95 (0.69-1.30) | .74 |
| Low HDL cholesterol | 18.8 (14.3-23.3) | 27.2 (22.0-32.3) | 19.2 (14.6-23.3) | 21.6 (16.8-23.7) | 0.79 (0.64-0.97) | .03 |
| High blood pressure | 40.8 (35.1-46.5) | 42.9 (37.1-48.6) | 49.8 (44.0-55.6) | 44.3 (38.5-50.0) | 0.87 (0.76-1.00) | .05 |
| Hyperglycemia | 17.4 (13.0-21.8) | 15.0 (10.8-19.1) | 17.4 (13.0-21.8) | 13.9 (9.9-18.0) | 0.92 (0.69-1.23) | .56 |
| MetS prevalence rate | 18.8 (14.2-23.3) | 19.2 (14.6-23.7) | 22.0 (17.2-26.8) | 17.8 (13.3-22.2) | 0.82 (0.64-1.06) | .13 |
| MetS incidence rate | NA | 7.3 (3.9-10.6) | NA | 2.7 (0.6-4.8) | 0.32 (0.13-0.79) | .01 |
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Abbreviations: HDL, high-density lipoprotein; NA, not applicable.
aValues used from the intention-to-treat data set.
bCriteria from the National Cholesterol Education Program Adult Treatment Panel III for the definition of MetS are as follows: abdominal obesity (waist circumference >102 cm in males or >88 cm in females), hypertriglyceridemia (fasting serum triglycerides ≥150 mg/dL), low HDL cholesterol levels (fasting HDL cholesterol <40 mg/dL in males or <50 mg/dL in females), high blood pressure (≥130/85 mm Hg or current antihypertensive medication use), and hyperglycemia (fasting blood glucose ≥110 mg/dL or current antidiabetic therapy). Individuals meeting at least 3 of these criteria were considered to have MetS.
cAdjusted for sex and age and estimated using Poisson regression models for incident cases of MetS per 6 mo.
Initially, 117 participants (20.4%) met the ATP III criteria for MetS, with 63 (22.0%) in the intervention group and 54 (18.8%) in the control group (Figure 2). After the 6-month follow-up, 18 participants in the intervention group (28.6%) and 16 (29.6%) in the control group no longer met these criteria. Therefore, in the whole sample (eg, participants with and without MetS), the intervention was not significantly associated with a reduced risk in overall MetS prevalence (RR, 0.82; 95% CI, 0.64-1.06) (Table 2). The most common MetS features were abdominal obesity (46.0%; 95% CI, 41.9%-50.1%) and high blood pressure (45.3%; 41.2%-49.4%). The intervention group saw a significant decrease in waist circumference (mean [SD] change, −1.79 [0.40] cm; P < .001) but no significant decreases in blood pressure (systolic: mean [SD] change, −1.4 [1.0] mmHg [P = .17]; diastolic: mean [SD] change, −0.72 [0.64] mmHg [P = .27]).
Figure 2.
Distribution of Metabolic Syndrome Component Score by Treatment Group
For individual MetS components, the intervention reduced the risk of central obesity (RR, 0.90; 95% CI, 0.82-1.00) and low high-density lipoprotein cholesterol (RR, 0.79; 95% CI, 0.64-0.97) compared with the control condition (Table 2). The intervention had no significant effect on high blood pressure, high triglyceride levels, or high fasting serum glucose levels compared with the control condition. Additional modeling details are provided in eTable 5 in Supplement 3. Per-protocol analysis showed similar results, with the exception of a significant reduction in hypertension risk associated with the intervention (eTable 1 in Supplement 2).
Multivariable ordered logistic regression analysis revealed that participants in the intervention group had a proportional odds ratio of 0.58 (95% CI, 0.42-0.82) for having more MetS components compared with those in the control group (Table 3), which means that those in the intervention group were approximately 42% less likely to exhibit an additional MetS component compared with the control group. Sensitivity analysis using the per-protocol population showed similar results in magnitude and direction (eTable 2 in Supplement 2).
Table 3.
Distribution of Metabolic Syndrome (MetS) Component Score by Treatment Group Among 574 Participants
| No. of fulfilled components of MetS | Control group (n = 287) | Intervention group (n = 287) | POR (95% CI)a | P value | ||
|---|---|---|---|---|---|---|
| Baseline | 6 mo | Baseline | 6 mo | |||
| 0 | 0.32 (0.27-0.38) | 0.30 (0.25-0.35) | 0.25 (0.20-0.31) | 0.31 (0.26-0.36) | 0.58 (0.42-0.82) | .002 |
| 1 | 0.26 (0.21-0.31) | 0.23 (0.18-0.28) | 0.30 (0.25-0.35) | 0.26 (0.21-0.31) | ||
| 2 | 0.23 (0.18-0.28) | 0.28 (0.22-0.33) | 0.23 (0.18-0.28) | 0.25 (0.20-0.30) | ||
| 3 | 0.13 (0.09-0.17) | 0.12 (0.08-0.15) | 0.12 (0.08-0.16) | 0.09 (0.06-0.12) | ||
| 4 | 0.05 (0.02-0.07) | 0.07 (0.04-0.10) | 0.08 (0.05-0.12) | 0.06 (0.04-0.09) | ||
| 5 | 0.01 (0.00-0.03) | 0.01 (0.00-0.02) | 0.01 (0.00-0.03) | 0.02 (0.01-0.04) | ||
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Abbreviation: POR, proportional odds ratio.
aOrdered logistic regression modeling was performed to estimate the POR and 95% CI, adjusted for age and sex.
Effect of the GALIAT Intervention on Carbon Footprint Emissions
After 6 months, the carbon footprint score was reduced in the control group (baseline: mean [SD], 3.71 [1.55] kg CO2 equivalents [kgCO2eq]/person/d; after 6 months: mean [SD], 3.56 [1.50] kgCO2eq/person/d) and the intervention group (baseline: mean [SD], 3.60 [1.44] kgCO2eq/person/d; after 6 months: mean [SD], 3.38 [1.39] kgCO2eq/ person/d) (eTable 3 and eFigure 3 in Supplement 2). Linear mixed regression analysis did not show a significant between-group difference (−0.17 kgCO2eq/person/d; 95% CI, −0.46 to 0.12), after adjusting for baseline values, age, and sex and with family as the random effect. The sensitivity analysis for the carbon footprint using the per-protocol population showed similar results (eTable 4 in Supplement 2). The ICC revealed that approximately 45% of the variability in the carbon footprint score was due to family membership, revealing the importance of the family belongingness in the emission of greenhouse gases associated with food.
Discussion
In a community-based sample from the GALIAT randomized clinical trial, we jointly examined the effects of a nutritional intervention based on a traditional Atlantic diet on MetS and associated changes in its components and the environmental outcomes associated with the Atlantic diet in terms of carbon footprint. Our main finding was that the 6-month nutritional intervention showed a lower risk of developing MetS among participants in the intervention group compared with the control group. Furthermore, individuals in the intervention group were approximately 42% less likely to exhibit an additional MetS component than those in the control group, although there was no evidence of a reduction in the RRs of high blood pressure, hypertriglyceridemia, or hyperglycemia, separately. Finally, the comparison between food consumption in the intervention and control groups did not achieve a statistically significant reduction in environmental impact.
Our study provides important contributions to the field. First, the positive outcomes for MetS and carbon footprint emissions were observed in a community-based sample following a diet that aligns with their cultural heritage. Second, the evidence provided for a randomized clinical trial that includes environmental parameters offers unique and valuable insight into how traditional diets can promote both health outcomes and environmental sustainability in line with the United Nations’s 2030 Agenda.26 Finally, the ICCs indicate that the cluster (family) factor had an important influence on the mean change of greenhouse gas emissions, supporting the effectiveness of family-based approaches for mitigating the impact of food-related greenhouse gas emissions.
It is well established that weight loss has a great benefit for the treatment of all the components of the MetS, which are excessive adiposity, dyslipidemia, hypertension, insulin resistance, and hyperglycemia.27,28,29,30 Approximately 20% of the study participants had MetS at baseline, consistent with the prevalence observed in a large study of 24 670 individuals from the general Spanish population, aged 35 to 74 years, without diabetes or cardiovascular disease.31 Similarly, our study’s findings on abdominal obesity closely match the 51% prevalence reported in a sample of 17 980 individuals aged 18 to 80 years in Spain according to ATP III criteria.32
On the other hand, our study is also critical to understanding the implications of dietary choices on environment-related SDGs, especially SDG 13 (health and climate action). This relationship was shown through the randomized controlled nutritional intervention, where we observed a reduction of 0.17 kgCO2eq/person/d in the intervention group compared with the control group. We believe that the lack of statistical significance can be attributed to the study’s limited statistical power, since the sample size was primarily determined to detect changes in cholesterol levels (Supplement 1). We have estimated that a study with approximately 2000 participants would be necessary for this reduction to reach statistical significance with a P < .05. Therefore, the observed outcome supports our expectation that adherence to the Atlantic diet in the broader target population could significantly enhance the carbon footprint metric, contributing to efforts to achieve net-zero CO2 emissions by 2050 in line with SDG 13.33
These findings are broadly consistent with research showing that the traditional Atlantic diet is a climate-friendly diet, well ranked under environmental criteria.7 Moreover, results from previous work that mainly focused on carbon footprint emissions of the Atlantic diet based on bibliographic recommendations7,18 fall within the range of our average carbon footprint calculated for the intervention group after 6 months, taking into consideration associated deviations. For example, González-García et al7 designed a dietary scenario based on serving sizes as defined by the Spanish Society of Community Nutrition and data on current food consumption in Galician households and obtained a carbon footprint for the Atlantic diet of 3.62 kgCO2eq/person/d.
Strengths and Limitations
This study has several strengths, including a randomized design, high retention rates, objective clinical and environmental measures, and a representative random sample from the general population. Our data offer valuable clinical insight into MetS and its prevention in a population-based context. We intentionally chose a community with moderate socioeconomic and educational levels to enhance generalizability, aligning with Organisation for Economic Co-operation and Development indicators at the time of study design.34 Baseline data suggest minimal bias, as randomization resulted in only slight group differences.
The study also has several limitations. Various unknown factors aside from the dietary intervention could have influenced clinical outcomes. In addition, the study intervention was complex, and it is not possible to determine which actions of the intervention may have contributed to the results. Thus, unmeasured aspects may exist. Contamination bias might be present, as the study received media attention, potentially influencing individuals’ lifestyles. The strategy of providing food baskets served as an incentive for session attendance and adherence but may limit generalizability to populations with food access challenges. The broad variety of carbon footprint emissions usually reported in published LCAs of food products, together with the diversity of food items in participant data, could have contributed to the environmental outcome. Moreover, 6 months may not have been long enough to properly assess metabolic changes. Follow-up of participants over a number of years could strengthen our results. Finally, our study’s sample size might lack the statistical power required to detect a significant reduction in CO2 emissions, indicating a potential need for larger-scale studies.
Conclusions
As far as we are aware, the GALIAT study is the first community-based randomized clinical trial that, taking into account the identity and diversity of the region’s food system and culture, has shown the positive effect of a traditional diet intervention on the incidence of new cases of MetS, as well as on limiting greenhouse gas emissions. Our findings provide important evidence for the potential of traditional diets to accelerate progress toward achieving SDGs. Further research is needed to thoroughly understand the underlying mechanisms behind the observed outcomes and to determine the generalizability of these findings to other populations, taking into account the cultural and dietary variations of each region.
Notes
Supplement 1.
Trial Protocol
Click here for additional data file.(1.0M, pdf)
Supplement 2.
eMethods.
eFigure 1. Intervention Components
eFigure 2. System Boundaries Considered in the Carbon Footprint Assessment
eTable 1. Incidence Rate, Prevalence Rate, and Rate Ratios of Metabolic Syndrome and Its Components by Treatment Group (Per-Protocol Data set)
eTable 2. Distribution of MetS Component Score by Treatment Group (Per-Protocol Data set)
eFigure 3. Frequency Distribution of Carbon Footprint at Baseline
eTable 3. Differences in Dietary Carbon Footprint Between the Intervention and Control Groups (Intention-to-Treat Data set)
eTable 4. Differences in Dietary Carbon Footprint Between the Intervention and Control Groups (Per-Protocol Data set)
eReferences
Click here for additional data file.(1.1M, pdf)
Supplement 3.
eTable 5. Carbon Footprint (CF) per Foodstuff and Their Corresponding Source, Origin, and Percentages of Food Loss and Food Waste
eReferences
Click here for additional data file.(33K, xlsx)
Supplement 4.
Data Sharing Statement
Click here for additional data file.(17K, pdf)
References
1. World Health Organization . Sustainable Healthy Diets: Guiding Principles. Food & Agriculture Org; 2019. [Google Scholar]
2. Guallar-Castillón P, Oliveira A, Lopes C, López-García E, Rodríguez-Artalejo F. The Southern European Atlantic Diet is associated with lower concentrations of markers of coronary risk. Atherosclerosis. 2013;226(2):502-509. doi: 10.1016/j.atherosclerosis.2012.11.035 [PubMed] [CrossRef] [Google Scholar]
3. Moreira C, Santos R, Moreira P, et al.. Cardiorespiratory fitness is negatively associated with metabolic risk factors independently of the adherence to a healthy dietary pattern. Nutr Metab Cardiovasc Dis. 2013;23(7):670-676. doi: 10.1016/j.numecd.2012.01.011 [PubMed] [CrossRef] [Google Scholar]
4. Agostinis-Sobrinho C, Dias AF, Brand C, et al.. Adherence to Southern European Atlantic Diet and physical fitness on the atherogenic index of plasma in adolescents. Cad Saude Publica. 2019;35(12):e00200418. doi: 10.1590/0102-311x00200418 [PubMed] [CrossRef] [Google Scholar]
5. Rodríguez-Martín C, Garcia-Ortiz L, Rodriguez-Sanchez E, et al.. The relationship of the Atlantic diet with cardiovascular risk factors and markers of arterial stiffness in adults without cardiovascular disease. Nutrients. 2019;11(4):1-13. doi: 10.3390/nu11040742 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
6. Calvo-Malvar M, Benítez-Estévez AJ, Sánchez-Castro J, Leis R, Gude F. Effects of a community-based behavioral intervention with a traditional Atlantic diet on cardiometabolic risk markers: a cluster randomized controlled trial (“The Galiat study”). Nutrients. 2021;13(4):1211. doi: 10.3390/nu13041211 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
7. González-García S, Green RF, Scheelbeek PF, Harris F, Dangour AD. Dietary recommendations in Spain -affordability and environmental sustainability?J Clean Prod. 2020;254:120125. doi: 10.1016/j.jclepro.2020.120125 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
8. Calvo-Malvar MdM, Leis R, Benítez-Estévez AJ, Sánchez-Castro J, Gude F. A randomised, family-focused dietary intervention to evaluate the Atlantic diet: the GALIAT study protocol. BMC Public Health. 2016;16(1):820. doi: 10.1186/s12889-016-3441-y [PMC free article] [PubMed] [CrossRef] [Google Scholar]
9. Calvo-Malvar M, Benítez-Estévez AJ, Leis R, Sánchez-Castro J, Gude F. Changes in dietary patterns through a nutritional intervention with a traditional Atlantic diet: the Galiat randomized controlled trial. Nutrients. 2021;13(12):4233. doi: 10.3390/nu13124233 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
10. Schulz KF, Altman DG, Moher D; CONSORT Group . CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c332. doi: 10.1136/bmj.c332 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
11. Hoffmann TC, Glasziou PP, Boutron I, et al.. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014;348:g1687. doi: 10.1136/bmj.g1687 [PubMed] [CrossRef] [Google Scholar]
12. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol Use Disorders Identification Test. Arch Intern Med. 1998;158(16):1789-1795. doi: 10.1001/archinte.158.16.1789 [PubMed] [CrossRef] [Google Scholar]
13. Vaz Velho M, Pinheiro R, Rodrigues AS. The Atlantic diet—origin and features. Int J Food Stud. 2016;5:106-119. doi: 10.7455/ijfs/5.1.2016.a10 [CrossRef] [Google Scholar]
14. Craig CL, Marshall AL, Sjöström M, et al.. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381-1395. doi: 10.1249/01.MSS.0000078924.61453.FB [PubMed] [CrossRef] [Google Scholar]
15. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) . Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002;106(25):3143-3421. doi: 10.1161/circ.106.25.3143 [PubMed] [CrossRef] [Google Scholar]
16. Ortega RM, López AM, Carvajales PA, Requejo AM, Aparicio A, Molinero LM. Programa Dial v. 3.3.5.0. 2016.
17. International Organization for Standardization . Environmental Management-Life Cycle Assessment. International Organization for Standardization; 2006. [Google Scholar]
18. Esteve-Llorens X, Darriba C, Moreira MT, Feijoo G, González-García S. Towards an environmentally sustainable and healthy Atlantic dietary pattern: life cycle carbon footprint and nutritional quality. Sci Total Environ. 2019;646:704-715. doi: 10.1016/j.scitotenv.2018.07.264 [PubMed] [CrossRef] [Google Scholar]
19. Castañé S, Antón A. Assessment of the nutritional quality and environmental impact of two food diets: a Mediterranean and a vegan diet. J Clean Prod. 2017;167:929-937. doi: 10.1016/j.jclepro.2017.04.121 [CrossRef] [Google Scholar]
20. Werner G, Bauer C, Steubing B, Reinhard J, Moreno-Ruiz E, Weidema B. The ecoinvent Database version 3 (part I): overview and methodology. Int J Life Cycle Assess. 2016;21:1218-1230. doi: 10.1007/s11367-016-1087-8 [CrossRef] [Google Scholar]
21. Gustavsson J, Cederberg C, Sonesson U, Emanuelsson A. The methodology of the FAO study: “Global Food Losses and Food Waste - extent, causes and prevention” - FAO, 2011. Food and Agriculture Organization of the United Nations; 2013. Accessed December 22, 2023. https://www.fao.org/3/i2697e/i2697e.pdf [Google Scholar]
22. Batlle-Bayer L, Bala A, García-Herrero I, et al.. The Spanish dietary guidelines: a potential tool to reduce greenhouse gas emissions of current dietary patterns. J Clean Prod. 2019;213:588-598. doi: 10.1016/j.jclepro.2018.12.215 [CrossRef] [Google Scholar]
23. Garcia-Herrero I, Hoehn D, Margallo M, et al.. On the estimation of potential food waste reduction to support sustainable production and consumption policies. Food Policy. 2018;80:24-38. doi: 10.1016/j.foodpol.2018.08.007 [CrossRef] [Google Scholar]
24. Royston P, White I. Multiple imputation by chained equation (mice): implementation in Stata. J Stat Softw. 2011;45. doi: 10.18637/jss.v045.i04 [CrossRef] [Google Scholar]
25. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702-706. doi: 10.1093/aje/kwh090 [PubMed] [CrossRef] [Google Scholar]
26. United Nations . Transforming Our World: The 2030 Agenda for Sustainable Development. Department of Economic and Social Affairs, United Nations; 2015. [Google Scholar]
27. Van Gaal LF, Wauters MA, De Leeuw IH. The beneficial effects of modest weight loss on cardiovascular risk factors. Int J Obes Relat Metab Disord. 1997;21(suppl 1):S5-S9. [PubMed] [Google Scholar]
28. Stevens VJ, Obarzanek E, Cook NR, et al.; Trials for the Hypertension Prevention Research Group . Long-term weight loss and changes in blood pressure: results of the Trials of Hypertension Prevention, phase II. Ann Intern Med. 2001;134(1):1-11. doi: 10.7326/0003-4819-134-1-200101020-00007 [PubMed] [CrossRef] [Google Scholar]
29. Case CC, Jones PH, Nelson K, O’Brian Smith E, Ballantyne CM. Impact of weight loss on the metabolic syndrome. Diabetes Obes Metab. 2002;4(6):407-414. doi: 10.1046/j.1463-1326.2002.00236.x [PubMed] [CrossRef] [Google Scholar]
30. Wing RR, Koeske R, Epstein LH, Nowalk MP, Gooding W, Becker D. Long-term effects of modest weight loss in type II diabetic patients. Arch Intern Med. 1987;147(10):1749-1753. doi: 10.1001/archinte.1987.00370100063012 [PubMed] [CrossRef] [Google Scholar]
31. Fernández-Bergés D, Cabrera de León A, Sanz H, et al.. Metabolic syndrome in Spain: prevalence and coronary risk associated with harmonized definition and WHO proposal. DARIOS study. Rev Esp Cardiol (Engl Ed). 2012;65(3):241-248. doi: 10.1016/j.rec.2011.10.017 [PubMed] [CrossRef] [Google Scholar]
32. Casanueva FF, Moreno B, Rodríguez-Azeredo R, et al.. Relationship of abdominal obesity with cardiovascular disease, diabetes and hyperlipidaemia in Spain. Clin Endocrinol (Oxf). 2010;73(1):35-40. doi: 10.1111/j.1365-2265.2009.03727.x [PubMed] [CrossRef] [Google Scholar]
33. United Nations . Appendix: transforming our world: the 2030 agenda for sustainable development. In: Rosa W, ed. A New Era in Global Health: Nursing and the United Nations 2030 Agenda for Sustainable Development. Springer; 2018. doi: 10.1891/9780826190123.ap02 [CrossRef] [Google Scholar]
34. Education at a Glance 2011: OECD Indicators. Organisation for Economic Co-operation and Development; 2011. Accessed December 22, 2023. https://www.oecd.org/education/skills-beyond-school/48631582.pdf [Google Scholar]
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