García et al. Globalization and Health (2023) 19:50 Globalization and Health https://doi.org/10.1186/s12992-023-00948-3 RESEARCH Open Access Metabolic syndrome criteria and severity and carbon dioxide ­(CO2) emissions in an adult population Silvia García1,2,3, Rosario Pastor2,4, Margalida Monserrat‑Mesquida1,2,3, Laura Álvarez‑Álvarez5,6, María Rubín‑García5,6, Miguel Ángel Martínez‑González1,7, Jordi Salas‑Salvadó1,8, Dolores Corella1,9, Albert Goday1,10,11, J. Alfredo Martínez1,12,13, Ángel M. Alonso‑Gómez1,14, Julia Wärnberg1,15, Jesús Vioque4,16, Dora Romaguera1,3, José Lopez‑Miranda1,17, Ramon Estruch1,18, Francisco J. Tinahones1,19, José Lapetra1,20, Lluís Serra‑Majem1,21, Blanca Riquelme‑Gallego4,22, Xavier Pintó1,23, José J. Gaforio4,24, Pilar Matía25, Josep Vidal26, Clotilde Vázquez27, Lidia Daimiel28, Emilio Ros1,29, Carmen Sayón‑Orea1,5,30, Patricia Guillem‑Saiz1,9, Cristina Valle‑Hita1,8, Robert Cabanes1,10, Itziar Abete1,13, Leire Goicolea‑Güemez1,14, Enrique Gómez‑Gracia1,31, Cristina Tercero‑Maciá16,32, Antoni Colom1,3, Antonio García‑Ríos1,17, Sara Castro‑Barquero1,18, José C. Fernández‑García1,19, José Manuel Santos‑Lozano1,20, Juan Carlos Cenoz1,33, Rocío Barragán1,9, Nadine Khoury1,8, Olga Castañer1,10, María Ángeles Zulet1,13, Jessica Vaquero‑Luna1,14, Maira Bes‑Rastrollo1,7, Sara de las Heras‑Delgado1,8, Ramon Ciurana1,10, Vicente Martín‑Sánchez5,6, Josep A. Tur1,2,3*   and Cristina Bouzas1,2,3 Abstract Background Metabolic syndrome (MetS) has become a growing risk factor of some non-communicable diseases. Increase of greenhouse gas emissions affects the planet. Aims To assess the association between MetS severity and amount of carbon dioxide (­CO2) emitted in an adult population. Design Cross-sectional study (n = 6646; 55-76-year-old-men; 60-75-year-old-women with MetS). Methods Dietary habits were assessed using a pre-validated semi quantitative 143-item food frequency question‑ naire. The amount of ­CO2 emitted due to the production of food consumed by person and day was calculated using a European database, and the severity of the MetS was calculated with the MetS Severity Score. Results Higher glycaemia levels were found in people with higher ­CO2 emissions. The risk of having high severe MetS was related to high ­CO2 emissions. Conclusions Low ­CO2 emissions diet would help to reduce MetS severity. Advantages for both health and the envi‑ ronment were found following a more sustainable diet. Trial registration ISRCTN, ISRCT​N8989​8870. Registered 05 September 2013. Keywords Metabolic syndrome, Environment, CO2 emissions, Non-communicable diseases, Glycaemia, Diet *Correspondence: Josep A. Tur pep.tur@uib.es Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​ mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. García et al. Globalization and Health (2023) 19:50 Page 2 of 11 Introduction with age [18, 26]. Its prevalence is 31% in Spain [27], Several factors have a detrimental impact on the ecosys- and it increasingly becoming a global epidemic [11, 12]. tem, including the amount of greenhouse gas emissions Since food production and dietary consumption are (GHGs) in the atmosphere, especially carbon diox- related to climate change and globalization-related ide ­(CO2) [1]. The intergovernmental Panel on Climate drivers of MetS are related to climate change, it could Change (IPCC) aimed to reduce emissions by 45% by be inferred that MetS could be related to environmen- 2030 and achieve zero emissions by 2050 [2]. There is a tal change. The risk of MetS is increasing and reaching vital need for a more in-depth evaluation to assess the epidemic proportions worldwide [28] and a detrimen- impact of health and related factors on climate change, tal impact on the environment is also happening [29]; and vice versa, considering the different scenarios of cli- then, the dietary intake or food consumption could be mate change and predictions of the demographic struc- part of the problem. ture of the countries [1]. Food system emissions are The risk of MetS and diet-related ­ CO2 emissions around one third of the global GHG emissions and repre- should be both attenuated changing food habits and sents 34% of total C ­ O2 equivalents in 2015 [3]. Therefore, following certain diets such as the MedDiet which is a food production and dietary consumption are related to well-studied model in terms of healthiness and sustain- climate change, and they should be changed [4] to reduce ability [30, 31]. Sustainable diets were defined as those emissions caused from it. diets with low environmental impacts which contrib- Metabolic syndrome (MetS) is a condition of inter- ute to food and nutrition security and to healthy life related risk factors including high glycaemia levels for present and future generations. Sustainable diets (> 100 mg/dL), hypertension (> 130/85 mmHg), raised are protective and respectful of biodiversity and eco- triglyceride levels (> 150 mg/dL), low high-density lipo- systems, culturally acceptable, accessible, economically protein cholesterol levels (< 40 mg/dL in men; <50 mg/ fair and affordable; nutritionally adequate, safe and dL in women), and abdominal obesity (waist circumfer- healthy; while optimizing natural and human resources ence of > 102 cm in men; >88 cm in women) [5–7]. Three [32]. Few papers linking MetS and sustainability, or or more of these factors are considered as having MetS GHGs, have been found. and lead to increased risk of cardiovascular disease, ath- Therefore, the aim of this study was to assess the erosclerosis, cancer, and type 2 diabetes mellitus (T2DM) association between the MetS severity, and the amount [8] which are some of the main causes of death in Spain of ­CO2 emitted from the production of food consumed, [9] but also worldwide [10]. The severity of having one or in an adult population. more aggregated cardiovascular risk factors for metabolic syndrome is known as the metabolic syndrome severity (MetSS), and can be measured with the metabolic syn- Methods drome severity score (MetSSS). This is a tool created to Study design assess cardio-metabolic risk considering the joint effect The current research was a cross-sectional analysis of of metabolic syndrome factors, and not their separated the baseline data within an ongoing 8-year multicenter, presence. It can be used for the treatment and manage- parallel-group, randomized trial, carried out in 23 Span- ment of this pathology [11]. ish recruitment centers. More details of the study design MetS is closely interrelated with food habits. A high have been extensively described [33]. The trial was regis- consumption of high calorie and low-density food tered in 09/05/2013 at the International Standard Rand- and a reduction of physical activity (PA) has led to omized Controlled Trial (ISRCT; http://​www.​isrctn.​com/​ an increase of MetS in Western countries but also in ISRCT​N8989​8870) with the number 89898870. developing countries which have changed to a Western lifestyle [12]. Moderate or vigorous exercise has been Participants, recruitment, randomization, and ethics inversely associated with MetS in children and adoles- Participant flow-chart eligibility was shown in Fig. 1. cent population, while a sedentary behavior has been Among the 9677 participants who were contacted, 6874 positively associated [13, 14], and the same effects have participants met the inclusion criteria of being men aged been observed in adult population [15–17]. Dietary 55–76 or women aged 60–75, overweight or obese (body habits [17, 18] and weight interventions [19] have been mass index (BMI) between 27 and 40 kg/m2) and meeting related to MetS. Different diet interventions such as at least three criteria for the MetS according to the Asso- time-restricted eating [20] or high-protein diet [21] has ciation and National Heart, Lung, and Blood Institute been studied, but the Mediterranean diet (MedDiet) [5]. Participants with incomplete food frequency ques- seemed to be one of the most effective interventions tionnaire (FFQ) data or reporting extreme total energy [14, 17, 22–25]. MetS is also influenced and increases intakes (< 500 or > 3500 kcal/day in women or < 800 or García et al. Globalization and Health (2023) 19:50 Page 3 of 11 Fig. 1 Flow chart of participant inclusion > 4000 kcal/day in men) were excluded and 6646 partici- agricultural production and processing steps (consider- pants were left for the analysis. Informed written consent ing defaults for cooking, storing, and packing and letting was provided by all participants and the study protocol transportation out of the calculations) [41]. Kilograms of and procedures were approved by ethical committees ­CO2 emitted per consumed food were calculated by mul- according to the ethical standards of the Declaration of tiplying grams of each consumed food reported from the Helsinki by all the 23 participating institutions. FFQ per kg of C ­ O2 emitted per kg of each food from the database. The sum of all kilograms of ­CO2 emitted for Dietary assessment all the products was done to determine the total emis- Registered dietitians assessed dietary habits, at baseline, sions a day from diet. Once the C ­ O2 emitted for each using a semi quantitative 143-item FFQ [34] previously participant was known, an adjustment per 1 kg of food validated in the Spanish population [35, 36]. A regular consumed was completed. The adjustment was done to portion size was established for each item, and 9 catego- consider the energy intake confounder. Depending on the ries (ranging from “never or almost never” to “≥6 times/ individual needs, the dietary intake could be higher in day”) were used to assess consumption frequencies. terms of quantity meaning higher emissions, even when Energy and nutrient intakes were calculated as frequency comparing diets based on the same products. Therefore, multiplied by nutrient composition of specified portion an adjustment per 1 kg of food product per person offers size for each food item, using a computer program based a better comparison between the emissions of the par- on available information in Spanish food composition ticipants’ diets and avoids bias for people who could eat Tables [37, 38]. The results were used to determine the higher amounts due to their personal needs. Data was specific amount of food (in grams) each participant had distributed in quartiles according to the amount of ­CO2 eaten per day [39, 40]. emissions: Quartile 1 (Q1) represented participants with Adherence to MedDiet was assessed using the 17-item the lowest emissions (≤ 2.01 kg ­CO2), quartile 2 (Q2) MedDiet validated questionnaire. Each item was related represented participants with low-moderate emissions to a food habit and the compliance with food habits (2.02–2.34 kg ­CO2), quartile 3 (Q3) represented partici- scored 1 for every item, otherwise scored 0; it ranged pants with moderate-high emissions (2.35–2.79 kg ­CO2) between 0 and 17 [39, 40]. and quartile 4 (Q4) represented participants with the highest emissions (≥ 2.80 kg ­CO2). Q1 was established as CO2 emitted per kg of food the reference. The amount of ­CO2 emitted per kg of consumed food per participant and day was calculated using a Euro- MetS assessment pean database from 2016 that described kg of ­CO2 emit- MetS components were determined at baseline con- ted per kg of food consumed. This database was based sidering the following criteria [5]: high glucose levels on life cycle assessment of recent studies and included (> 100 mg/dL), hypertension (> 130/85 mmHg), raised García et al. Globalization and Health (2023) 19:50 Page 4 of 11 triglyceride levels (> 150 mg/dL), low high-density lipo- Other health variables protein cholesterol levels (< 40 mg/dL in men; <50 mg/dL Information related to sociodemographic characteristics in women), and abdominal obesity (waist circumference such as sex, age, and education levels were self-reported. of > 102 cm in men; >88 cm in women). The criteria are Anthropometric measurements (weight, height, waist, described as follows: and hip circumference) were obtained. The validated Minnesota-REGICOR short PA questionnaire [49–51] • High glucose blood level, or hyperglycemia, is a situ- and the validated Spanish version of the Nurses’ Health ation when the body is not able to produce enough Study questionnaire [52] were used to assess PA and sed- insulin to transport glucose from blood to cells and entary behaviors respectively and data was showed in it remains excessively in the bloodstream [42]. To Metabolic Equivalents of Task (METs). assess glycemia levels, overnight fasting (at least 8 h) blood collections were analyzed in local laboratory Statistics using standard enzymatic methods. Analyses were performed with the SPSS statistical soft- • Hypertension is the high blood pressure exerted on ware version 27.0 (SPPS Inc., Chicago, IL, USA). Data are the blood vessels [43]. Blood pressure was measured shown as mean and standard deviation (SD). Prevalence in seated position with a validated semi-automatic was expressed in sample size and percentage. Differences oscillometer (Omron HEM-705CP, Lake Forest, IL, among groups were tested with one-way ANOVA (fol- USA). Three measures were taken after 5 min sitting lowed by a post-hoc and Bonferroni analysis) for con- at rest, waiting one minute between each take. tinuous variables and chi-squared test for categorical • Dyslipidemia is the altered blood lipid concentration. variables. Logistic regression was fitted to assess associa- There are two MetS components related with dyslipi- tion between the MetS parameters and its severity, and demia: High blood level of triglycerides, or hypertri- the kg of emitted C­ O2. Odds Ratio (OR) value, crude and glyceridemia, and low concentration of high-density adjusted, and its interval was calculated. Quartiles were lipoproteins (HDL), or low HDL-cholesterol. Over- created to separate the sample according to the amount night fasting blood collections were analyzed in local of emitted ­CO2 and Q1 was considered as the reference laboratory using standard enzymatic methods [44]. value. The first OR adjustment was made based on soci- • Abdominal obesity or excessive accumulation of fat odemographic characteristics (sex, education level, age) at abdomen [45] was assessed by measuring waist cir- and the second OR adjustment included sociodemo- cumference two times using an anthropometric tape, graphic characteristics (sex, education level and age) and halfway between the last rib and the iliac crest [46]. adherence to the MedDiet. The first OR adjustment was done to consider sociodemographic characteristic effects Each risk factor was assessed separately, as previ- on the sample. Sex, educational level, and age can alter ously described, and its severity was calculated applying metabolic syndrome severity outcomes [11]. The sec- the MetSSS [11]. MetSSS was calculated using standard ond OR adjustment added the MedDiet variable because deviations and weight of Principal Component Analysis it was seen inversely related with metabolic syndrome of all participants data from The Healthy Hearts (HH) severity [10] and inversely related to C ­ O2 emissions [53]. study, which was a cross-sectional observational study OR was calculated between each one of the items of the aimed to establish age and sex cardiometabolic risk fac- MetS, the MetS severity (low or high) and the quartiles tors in an Australian population [47]. Its formula uses all of the amount of C ­ O2 emitted in kg. Predictive margins MetS biomarker values for everyone (who are not using were calculated with a 95% confidence interval (CI) and any prescribed medications), measuring the statistical a linear prediction was created between quartiles of ­CO2 distance from clinical thresholds for the MetS using the emissions and the MetSSS. Statistical significance was set Mahalanobis Distance [48]. Specific steps for the MetSSS at a two-tailed p value < 0.05. Our hypothesis was that a development are explained elsewhere [11]. MetSSS ena- more sustainable diet, in terms of ­CO2 emissions, would bles the conversion of the categorical variables, associ- be related to the risk of metabolic syndrome. ated to MetS criteria, into a single continuous variable, facilitating the assessment not only of the presence of each risk factor but also the severity level associated with Results them. Instead of assessing each risk factor separately, Table 1 shows sociodemographic characteristics of the MetSSS is used because it allows the assessment of the sample. The relation between age, BMI, PA, sex, MetS combined effects of having one or more aggregated risk parameters, and the severity of MetS according to the factors, rather than solely considering their individual amount (kg) of ­CO2 emissions is described in Table 2. effects on a person’s state of health. When comparing Q1 and Q4, age and BMI proved to García et al. Globalization and Health (2023) 19:50 Page 5 of 11 Table 1 Sociodemographic characteristics of the sample be significant, with little changes across groups. Those individuals with higher levels of ­CO2 emissions were n (%) more likely to be men, younger and had lower BMI. Sex Only glucose was significant (p = 0.039). Higher gly- Men 3429 (51.6%) caemia levels (> 100 mg/dL) were identified in those Women 3218 (48.4%) with higher ­CO2 emissions (Q4: >2.80 kg ­CO2/day), Highest school level completed with lower glycaemia levels observed in Q1 and Q2. Bachelor’s degree 858 (12.9%) The relationship between the severity of MetS and ­CO2 College School Technician 601 (9%) emissions was significant (p = 0.025). The percentage of Secondary School 1918 (28.9%) people with higher MetS severity was higher in people Primary School 3270 (49.2%) with higher CO2 emissions (Q1: 42.7%; Q4: 47.7%) and mean (± SD) the percentage of those with low severity was higher in Age (years) 64.9 (± 4.9) groups with low CO2 emissions (Q1: 48.4%; Q4: 43.9%). Weight (Kg) 86.5 (± 12.9) The association between risk of MetS and the amount BMI (Kg/m2) 32.5 (± 3.4) of ­CO2 emissions is shown in Table 3. Crude OR showed Energy intake (Kcal/day) 2365 (± 551) that glycaemia was significantly associated with Q2 (OR Categorical variables are shown in sample size and percentage, and 1.16; 95%CI: 0.99–1.36), Q3 (OR 1.20; 95%CI: 1.03–1.41) continuous variables by mean and standard deviation (SD) and Q4 (OR 1.23; 95%CI: 1.05–1.44) in respect to Q1 Abbreviation: BMI Body Mass Index (reference), meaning that people with higher glycaemia were more likely to have higher ­CO2 emissions. Results were also significant for glycaemia (p = 0.009) after adjustment in Q2 (OR 1.17; 95%CI: 1.00-1.37), Q3 (OR Table 2 SES factors, physical activity and MetS criteria and severity according to C ­ O2 emissions (quartiles) Q1 n = 1661 Q2 n = 1662 Q3 n = 1663 Q4 n = 1660 p-value Mean (SD) Mean (SD) Mean (SD) Mean (SD) Age 65.2 (4.9) c 65.1 (4.9) 64.8 (4.9) 64.8 (4.9) c 0.020 c BMI 32.4 (3.5) 32.5 (3.4) 32.5 (3.5) 32.7 (3.5) c 0.039 Total PA (METs) 3328 (2789.6) 3019.7 (2995) 2349 (2552.4) 2785.9 (2699.3) 0.641 Light PA (METs) 788.2 (1064) 732.6 (918.8) 596.4 (740.9) 762.9 (980.7) 0.436 Moderate PA (METs) 1484.8 (2167.3) 1641.1 (2970.1) 1105.3 (2099.2) 1132.1 (1586.9) 0.294 Intense PA (METs) 1054.9 (1697.5) 645.9 (947.4) 647.2 (1275.5) 890.9 (1648.3) 0.231 n (%) n (%) n (%) n (%) Sex Men 788 (47.4) 824 (49.6) 884 (53.2) 932 (56.1) < 0.001 Women 873 (52.6) 838 (50.4) 779 (46.8) 728 (43.9) MS Inclusion Criteria TG ≥ 150 mg/dL 939 (56.5) 891 (53.6) 940 (56.5) 927 (55.8) 0.276 Glycaemia ≥ 100 mg/dL 1210 (72.8) 1258 (75.7) 1270 (76.4) 1274 (76.7) 0.039 Hypertension ≥ 130/85mmHg 1516 (91.3) 1534 (92.3) 1535 (92.3) 1524 (91.8) 0.655 HDL cholesterol [< 40 mg/dL (M) 707 (42.6) 689 (41.5) 737 (44.3) 709 (42.7) 0.419 /<50 mg/dL (W)] Waist > 102 cm (M) > 88 cm (W) 1590 (95.7) 1600 (96.3) 1603 (96.4) 1591 (95.8) 0.712 MetS ­Severityg Low severity (≤ 3.31) 804 (48.4) 749 (45.1) 732 (44.0) 728 (43.9) 0.025 High severity (> 3.31) 709 (42.7) 759 (45.7) 765 (46.0) 791 (47.7) §Kg of CO2 per product= (Kg of the product from FFQ*Kg CO2 of the product in EU data base) / 1 kg of product. Adjustments for 1 kg of product were done later to be able to compare diets at the same level. Measurements were separated into four groups; Four groups were considered according to C ­ O2 emissions: Q1: ≤2.01 kg ­CO2/day; Q2: 2.02–2.34 kg C ­ O2/day; Q3: 2.35–2.80 kg C ­ O2/day; Q4: >2.80 kg C ­ O2/day. Difference in means between groups were tested by one-way ANOVA and Bonferroni’s post-hoc for age, BMI and physical activity. Differences in prevalence’s across groups were examined using χ2. Different letters indicate statistically significant differences between groups (a, b, c, d, e, f ) according to Bonferroni’s post-hoc analysis Abbreviations: BMI Body Mass Index, MetS Metabolic Syndrome, TG Triglycerides, HDL High Density lipoprotein g MetS Severity is calculated with a Metabolic Syndrome Severity Score (MetSSS) [39] García et al. Globalization and Health (2023) 19:50 Page 6 of 11 Table 3 Association between the risk of Metabolic Syndrome and ­CO2 emissions Q1 § Q2 § Q3 § Q4 § p-value n = 1661 n = 1662 n = 1663 n = 1660 OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) MetS Criteria TG ≥ 150 mg/dL Crude OR 1.000 (Ref.) 0.89 (0.78–1.02) 1.00 (0.87–1.15) 0.97 (0.85–1.12) 0.277 OR adjusted 1 1.000 (Ref.) 0.88 (0.88–1.01) 0.98 (0.85–1.12) 0.94 (0.82–1.08) 0.300 OR adjusted 2 1.000 (Ref.) 0.88 (0.76–1.01) 0.96 (0.83–1.10) 0.91 (0.79–1.04) 0.240 Glucose < 100 mg/dL Crude OR 1.000 (Ref.) 1.16 (0.99–1.36) 1.20 (1.03–1.41) 1.23 (1.05–1.44) 0.039 OR adjusted 1 1.000 (Ref.) 1.16 (0.99–1.36) 1.20 (1.03–1.40) 1.22 (1.05–1.44) 0.045 OR adjusted 2 1.000 (Ref.) 1.17 (1.00-1.37) 1.23 (1.05–1.44) 1.29 (1.10–1.52) 0.009 Hypertension ≥ 130/85mmHg Crude OR 1.000 (Ref.) 1.15 (0.89–1.47) 1.15 (0.90–1.47) 1.07 (0.84–1.37) 0.655 OR adjusted 1 1.000 (Ref.) 1.15 (0.90–1.48) 1.16 (0.91–1.50) 1.09 (0.85–1.40) 0.609 OR adjusted 2 1.000 (Ref.) 1.15 (0.90–1.47) 1.16 (0.90–1.48) 1.07 (0.84–1.38) 0.635 HDL cholesterol Crude OR 1.000 (Ref.) 0.96 (0.83–1.10) 1.07 (0.94–1.23) 1.00 (0.88–1.15) 0.419 < 40 mg/dL (M) OR adjusted 1 1.000 (Ref.) 0.96 (0.84–1.10) 1.09 (0.95–1.25) 1.03 (0.90–1.18) 0.360 < 50 mg/dL (W) OR adjusted 2 1.000 (Ref.) 0.96 (0.83–1.10) 1.07 (0.94–1.23) 1.01 (0.87–1.16) 0.435 Waist circumference Crude OR 1.000 (Ref.) 1.15 (0.81–1.63) 1.19 (0.84–1.69) 1.03 (0.73–1.44) 0.712 > 102 cm (M) OR adjusted 1 1.000 (Ref.) 1.21 (085-1.72) 1.33 (0.93–1.89) 1.21 (0.86–1.71) 0.455 > 88 cm (W) OR adjusted 2 1.000 (Ref.) 1.21 (0.85–1.72) 1.33 (0.93–1.90) 1.21 (0.87–1.72) 0.455 MetS Severity* Crude OR 1.000 (Ref.) 1.15 (1.00-1.33) 1.19 (1.03–1.37) 1.23 (1.07–1.42) 0.025 OR adjusted 1 1.000 (Ref.) 1.18 (1.02–1.36) 1.25 (1.08–1.44) 1.33 (1.16–1.55) 0.001 OR adjusted 2 1.000 (Ref.) 1.18 (1.02–1.36) 1.23 (1.06–1.42) 1.30 (1.13–1.51) 0.003 §Kg of CO2 per product= (Kg of the product from FFQ*Kg ­CO2 of the product in EU data base) / 1 kg of product. Adjustments for 1 kg of product were done later to be able to compare diets at the same level. Measurements were separated into four groups; Four groups were considered according to C ­ O2 emissions: Q1: ≤2.01 kg ­CO2/ day; Q2: 2.02–2.34 kg ­CO2/day; Q3: 2.35–2.80 kg C ­ O2/day; Q4: >2.80 kg C ­ O2/day Abbreviations: MetS Metabolic Syndrome, TG Triglycerides, HDL High Density lipoprotein, OR Odds Ratio, OR adjusted 1: Odds Ratio adjusted by sociodemographic characteristics (sex, education level, age). OR adjusted 2: Odds Ratio adjusted by sociodemographic characteristics (sex, education level, age) and adherence to the Mediterranean Diet * MetS Severity is calculated with a Metabolic Syndrome Severity Score (MetSSS) [39] 1.23; 95%CI: 1.05–1.44) and Q4 (OR 1.29; 95%CI: 1.10– reduce GHGs such as C ­ O2 [24] which is the greenhouse 1.52). Crude and adjusted OR were also calculated for gas used for the calculations in this paper. The major the severity of MetS and it shows how people with higher published papers tended to relate environmental effect ­CO2 emissions were more likely to have higher severity with the type of food consumed, occasionally analyz- of MetS compared to those with lower severity (Q2, OR ing the environmental impact of the entire diet [54]. The 1.15; 95%CI: 1.00-1.33; Q3, OR 1.19; 95%CI: 1.03–1.37; vegetarian diet has received attention not just in terms Q4, OR 1.23; 95%CI: 1.07–1.42). of sustainability but also in terms of health. It is based Figure 2 shows a linear prediction between the on products that have a lesser environmental effect and amounts of ­CO2 emitted (separated in quartiles) and the implies a reduction in high GHG emitting foods which are MetS severity score. A direct relation can be seen in the principally meat and animal products. It is a plant-based graph showing that as CO2 emissions increase, the level diet that could store carbon into the soil due to photo- of severity of the MetS increases as well. The greater dif- synthetic effects and release oxygen reducing the overall ference may be noticed in the graph when comparing Q1 amount of atmospheric ­CO2. Moreover, individuals who and Q4. followed these diets appeared to have lower incidence of several non-communicable diseases [55]. When compar- Discussion ing different diets in terms of specific food choices, high The current study showed the relation between MetS meat-eaters result to be those who eat foods with a higher and ­CO2 emissions. Following a diet which emitted less carbon footprint. Reductions in meat consumption, par- ­CO2 to the atmosphere appeared to be related with lower ticularly red and processed meat, dairy products, and oth- MetS severity. Especially, people with high glycaemia ers like sweets, savory snacks, white bread, and beverages, were highly related to high ­CO2 emissions. may lead to optimized diets in terms of sustainability. A A systematic review published in 2019 showed that reduction in energy intake has also been identified as a most of the studies assessing sustainability aimed to key component in lowering GHGs [56, 57]. García et al. Globalization and Health (2023) 19:50 Page 7 of 11 Fig. 2 Predictive margins between kg ­CO2 quartiles and MetS severity with 95%CI The research on relating GHGs and MetS is limited. those figures will rise. A reduced consumption of meat However, relevant studies relating GHGs, and healthy or and higher consumption of fruits and vegetables would unhealthy situations have been found. The Lancet Com- lead to a reduction in mortality numbers and a 19–30% mission established in 2019 that ‘The food we eat and lower prevalence of being overweight or obese, associ- how we produce it will determine the health of people and ated with a limited energy intake [60]. Adopting a diet planet, and major changes must be made to avoid both scenario in line with health and sustainable recommenda- reduced life expectancy and continued environmental tions resulted in 45–47% prevented deaths from reduced degradation’ [4]. Accordingly, several institutions like the coronary heart disease, 26% from stroke, 16–18% from World Health Organization had policies to emphasize cancer and 10–12% from T2DM [60]. the importance of change to a sustainable lifestyle, being Given MetS is specifically a risk factor of those dis- sustainable healthy diets a starting point [58]. The Paris eases, a low ­CO2 emissions-diet could be a safe and agreement is another known consensus aiming to limit useful strategy. It was pointed out that the adoption of global warming below 2ºC and closer to 1.5ºC. To achieve different diets like the Mediterranean, pescatarian, and it, the adhered countries should limit their GHGs to a vegetarian would reduce emissions by 30%, 45% and predetermined level [4]. 55% respectively [61]. Depending on the diet followed, One study assessing health and environmental impacts the risk of type 2 diabetes mellitus, cancer, coronary of the food-based dietary guidelines from 85 different mortality, or all-cause mortality could be reduced [62, countries compared the strategies done with the WHO 63]. The current study has also shown that a low ­CO2 and the EAT-Lancet Commission recommendations emissions-diet, which could be related to a lower risk [59]. A reduction of premature mortality (15%) and a of having high glycaemia levels in a Mediterranean reduction in GHGs (13%) were found when the national population. food based dietary guidelines were adopted. The biggest Another study, where non-communicable diseases improvement was following the EAT-Lancet Commis- were related to several diet scenarios, showed that the sion recommendations; 34% lower premature mortality, current Swiss diet appeared to be the most beneficial more than three times greater reductions in GHGs and, in terms of health and sustainability; on the contrary, a in general, greater achievement of the global targets. meat-oriented diet could result in adverse health out- Even though these differences are not causal, they are comes, a higher environmental footprint, an increase in associated with better dietary choices [59]. daily food expenditure and a lack of some essential nutri- Non-communicable diseases were mentioned in some ents [64]. An ecological study from United States showed studies as being related to sustainability [60, 61]. Stand- associations between ­CO2 emissions and the prevalence ard dietary guidelines were compared to a 2050 reference of obesity and diabetes [65], which agrees with the cur- scenario which revealed that switching to plant-based rent study, although those associations were weaker after diets and reducing animal-source foods would prevent some adjustments. 5.1 million deaths per year and preserve 79 million years Food dietary choices influence MetSSS and C ­ O2 of life. Moreover, if a vegetarian or vegan diet is selected, emissions, but they can also be influenced by social, García et al. Globalization and Health (2023) 19:50 Page 8 of 11 economic, commercial, and political factors. Meta- emissions while other studies have considered water, bolic syndrome is classified as a non-communicable land, or energy use as well as marine eutrophication, disease and, even if it started being more prevalent atmospheric acidification, and nitrogen or phospho- in developed countries, it spread due to presence of rous release [55, 72]. The database used to calculate C ­ O2 the western lifestyle around the world [12]. Moreo- emissions is from 2016, which is the data collected in the ver, healthy foods have become more expensive than current paper. This makes sense as data analyzed accord- unhealthy ones, having ultra-processed products as an ing to the situation at current time. Moreover, a while example [66], driving people with fewer resources to has passed from 2016 and some data might not be the purchase them [67] and affecting planet and people’s latest available. MetSSS was originally created using data health. Commercial determinants of health can also from an Australian population which is another limita- be affecting both dimensions, including various sec- tion, since our population is Spanish. The fact that the tors such as the food and beverage industry, tobacco population studied were 55 to 75 years old, limits the industry, alcohol industry, pharmaceutical industry, possibility to apply the current findings on younger pop- and advertising and marketing industries. These sec- ulations. Finally, causal effects cannot be set since the tors often employ strategies to promote their prod- study presents a cross-sectional design. ucts, maximize profits and achieve economic growth, having significant implications for public health and Conclusions the environment [68]. The regulation of these deter- CO2 emission could be related with the risk of having a minants needs to be addressed by political systems, more severe MetS and with glycaemia. Following a diet policies, and governance structures, being the political which emitted less ­CO2 to the atmosphere could be help- factor also a health-environment determinant itself. ful for those participants with high MetS severity. Further There is a need of developing policies focused on the research is needed to assess the relation between sustain- sustainable development goals [69] and integrating cli- ability and some specific non-communicable diseases, mate change and health systems [70, 71]. to find more ways to reduce the mortality and morbidity It is true that there is already some research on the they are causing worldwide. relation of what we eat, the environment and some specific health conditions, but very few information is Abbreviations found when relating diet, environment, and MetS. This BMI Body mass index new study on this subject showed how being more con- CO2 Carbon Dioxide scious and modifying our eating habits and could be FFQ Food frequency questionnaire GHGs Greenhouse gas emissions related both with the planet’s health and the people’s HDL High Density lipoprotein quality of life. MedDiet The Mediterranean Diet MetS Metabolic Syndrome MetSS Metabolic Syndrome Severity Strengths and limitations of the study MetSSS Metabolic Syndrome Severity Score The first strength is that the current study contributes OR Odds Ratio Q1 Quartile 1 to the very limited evidence relating sustainability, in Q2 Quartile 2 terms of ­CO2 emissions, and MetS. The second strength Q3 Quartile 3 is the large sample size used. Once the C ­ O2 calculations Q4 Quartile 4 SD Standard deviations were done for each participant, an adjustment per 1 kg TG Triglycerides of food product was done. This is a strength because it T2DM Type 2 diabetes mellitus avoids the effect of the energy intake confounder. The Acknowledgements amount in grams of food consumed is closely related We thank all PREDIMED-Plus participants and investigators. CIBEROBN, to individual caloric requirements. In other words, CIBERESP, and CIBERDEM are initiatives of the Instituto de Salud Carlos III high energy requirements are met by high amounts of (ISCIII), Madrid, Spain. The Hojiblanca (Lucena, Spain) and Patrimonio Comunal Olivarero (Madrid, Spain) food companies donated extra-virgin olive oil. food, and accordingly low requirements relate to lower The Almond Board of California (Modesto, CA), American Pistachio Growers amounts of food. Quantity of food is directly related to (Fresno, CA), and Paramount Farms (Wonderful Company, LLC, Los Angeles, ­CO2 emissions. To avoid this bias, an adjustment per one CA) donated nuts for the PREDIMED-Plus pilot study. kg of food product was performed. Calculating only the Authors’ contributions parameter of ­CO2 emissions for assessing the sustainabil- All the PREDIMED-Plus investigators (SG, RP, MM-M, LA, MR, MAM-G, JS-S, DC, ity of a diet allows the impact to be observed indepen- AG, JV, DR, JL-M, RE, FJT, JL, LS-M, BR-G, XP, JJG, PM, JV, CV, LD, ER, CS-O, PG-S, CV-H, RC, IA, LG-G, EG-G, SC.B, CT-M, AC, JMS-L, JCC, RB, NK, OC, MAZ, JV-L, dently from other parameters. MB-R, SH-D, RC, JAT, VM-S, CB) contributed to study concept and design and The current study has also limitations. The envi- to data extraction from the participants. SG, and CB performed the statistical ronmental impact is just calculated in terms of ­ C O2 analyses. SG, CB, and JAT drafted the manuscript. All authors reviewed the García et al. Globalization and Health (2023) 19:50 Page 9 of 11 manuscript for important intellectual content and approved the final version Institute International. D.C. reported receiving grants from Instituto de to be published. Salud Carlos III. R.E. reported receiving grants from Instituto de Salud Carlos III, Fundación Dieta Meditarránea and Cerveza y Salud and olive oil for the Funding trial from Fundación Patrimonio Comunal Olivarero and personal fees from This work was supported by the official Spanish Institutions for funding Brewers of Europe, Fundación Cerveza y Salud, Interprofesional del Aceite de scientific biomedical research, CIBER Fisiopatología de la Obesidad y Nutrición Oliva, Instituto Cervantes in Albuquerque, Milano and Tokyo, Pernod Ricard, (CIBEROBN) and Instituto de Salud Carlos III (ISCIII), through the Fondo Fundación Dieta Mediterránea (Spain), Wine and Culinary International de Investigación para la Salud (FIS), which is co-funded by the European Forum and Lilly Laboratories; non-financial support from Sociedad Española Regional Development Fund (six coordinated FIS projects leaded by JS-S and de Nutrición and Fundación Bosch y Gimpera; and grants from Uriach Labo‑ JVi, including the following projects: PI13/00673, PI13/00492, PI13/00272, ratories. The rest of the authors have declared that no competing interests PI13/01123, PI13/00462, PI13/00233, PI13/02184, PI13/00728, PI13/01090, exist. The funders had no role in the design of the study; in the collection, PI13/01056, PI14/01722, PI14/00636, PI14/00618, PI14/00696, PI14/01206, analyses, or interpretation of data; in the writing of the manuscript, or in the PI14/01919, PI14/00853, PI14/01374, PI14/00972, PI14/00728, PI14/01471, decision to publish the results. PI16/00473, PI16/00662, PI16/01873, PI16/01094, PI16/00501, PI16/00533, PI16/00381, PI16/00366, PI16/01522, PI16/01120, PI17/00764, PI17/01183, Author details 1 PI17/00855, PI17/01347, PI17/00525, PI17/01827, PI17/00532, PI17/00215, CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de PI17/01441, PI17/00508, PI17/01732, PI17/00926, PI19/00957, PI19/00386, Salud Carlos III (ISCIII), Madrid 28029, Spain. 2 Research Group on Community PI19/00309, PI19/01032, PI19/00576, PI19/00017, PI19/01226, PI19/00781, Nutrition & Oxidative Stress, University of the Balearic Islands & CIBEROBN, PI19/01560, PI19/01332, PI20/01802, PI20/00138, PI20/01532, PI20/00456, Guillem Colom Bldg, Campus, E‑07122 Palma de Mallorca, Spain. 3 Health PI20/00339, PI20/00557, PI20/00886, PI20/01158); the Especial Action Project Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca 07120, entitled: Implementación y evaluación de una intervención intensiva sobre Spain. 4 Faculty of Health Sciences, Catholic University of Avila, Avila 05005, la actividad física Cohorte PREDIMED-Plus grant to JS-S; the European Spain. 5 CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Research Council (Advanced Research Grant 2014–2019; agreement #340918) Carlos III (ISCIII), Madrid 28029, Spain. 6 Institute of Biomedicine (IBIOMED), granted to MÁM-G.; the Recercaixa (number 2013ACUP00194) grant to JS-S; University of León, Leon 24071, Spain. 7 Department of Preventive Medicine grants from the Consejería de Salud de la Junta de Andalucía (PI0458/2013, and Public Health, University of Navarra, IDISNA, Pamplona 31008, Spain. 8 PS0358/2016, PI0137/2018); the PROMETEO/2017/017 grant from the Gen‑ Biochemistry and Biotechnology Department. Human Nutrition Unit, eralitat Valenciana; the SEMERGEN grant. J.S-S is partially supported by ICREA Universitat Rovira i Virgili, IISPV, Hospital Universitari de Sant Joan, Reus 43201, under the ICREA Academia programme. C.B. was granted by Juan de la Cierva Spain. 9 Department of Preventive Medicine, University of Valencia, Valen‑ grant. None of the funding sources took part in the design, collection, analysis, cia 46100, Spain. 10 Unit of Cardiovascular Risk and Nutrition, Institut Hospital interpretation of the data, or writing the report, or in the decision to submit del Mar de Investigaciones Médicas Municipal d’Investigació Mèdica (IMIM), the manuscript for publication. Barcelona 08003, Spain. 11 Departament of Medicine, Universitat Autònoma de Barcelona, Barcelona 08003, Spain. 12 Cardiometabolics Precision Nutrition Availability of data and materials Program, IMDEA Food, CEI UAM + CSIC, Madrid 28049, Spain. 13 Department Data described in the manuscript, code book, and analytic code will be made of Nutrition, Food Sciences, and Physiology, Center for Nutrition Research, available upon request pending application and approval of the PREDIMED- University of Navarra, Pamplona 31008, Spain. 14 Osakidetza Basque Health Plus Steering Committee. There are restrictions on the availability of data for Service, Bioaraba Health Research Institute, Araba University Hospital, the PREDIMED-Plus trial, due to the signed consent agreements around data University of the Basque Country UPV/EHU, Vitoria‑Gasteiz 48013, Spain. 15 sharing, which only allow access to external researchers for studies following Department of Nursing, School of Health Sciences, University of Málaga- the project purposes. Requestors wishing to access the PREDIMED-Plus trial IBIMA, Málaga 29071, Spain. 16 Instituto de Investigación Sanitaria y Biomédica data used in this study can make a request to the PREDIMED-Plus trial Steering de Alicante, Universidad Miguel Hernández (ISABIAL-UMH), Alicante 03550, Committee chair: jordi.salas@urv.cat. The request will then be passed to mem‑ Spain. 17 Lipids and Atherosclerosis Unit, Department of Internal Medicine, bers of the PREDIMED-Plus Steering Committee for deliberation. Reina Sofia University Hospital, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), University of Cordoba, Córdoba 14004, Spain. 18 Depart‑ ment of Internal Medicine, IDIBAPS, Hospital Clinic, University of Barcelona, Declarations Barcelona 08036, Spain. 19 Department of Endocrinology, Virgen de la Victoria Hospital, University of Málaga, Málaga 29010, Spain. 20 Department of Fam‑ Ethics approval and consent to participate ily Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Informed written consent was provided by all participants and the study Sevilla 41013, Spain. 21 Institute for Biomedical Research, University of Las protocol and procedures were approved by ethical committees according to Palmas de Gran Canaria, Las Palmas 35016, Spain. 22 Department of Preventive the ethical standards of the Declaration of Helsinki by all the 23 participating Medicine, University of Granada, Granada 18071, Spain. 23 Lipids and Vascular institutions. Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge, Hospitalet de Llobregat, Barcelona 08907, Spain. 24 Department of Health Sciences, Consent for publication Center for Advanced Studies in Olive Grove and Olive Oils, University of Jaen, Informed consent was obtained from all subjects involved in the study. The Jaen 23071, Spain. 25 Department of Endocrinology and Nutrition, Instituto de results and writing of this manuscript followed the Committee on Publication Investigación Sanitaria San Carlos (IdISSC), Madrid 28040, Spain. 26 Department Ethics (COPE) guidelines on how to deal with potential acts of misconduct, of Endocrinology, IDIBAPS, Hospital Clinic, University of Barcelona, Barce‑ maintaining the integrity of the research and its presentation following the lona 08036, Spain. 27 Department of Endocrinology, Fundación Jiménez-Díaz, rules of good scientific practice, the trust in the journal, the professionalism Madrid 28040, Spain. 28 Precision Nutrition and Obesity Program.IMDEA Food, of scientific authorship, and the entire scientific endeavour. Written informed Nutritional Control of the Epigenome Group, CEI UAM + CSIC, Madrid 28049, consent has been obtained from the patient(s) to publish this paper. Spain. 29 Department of Endocrinology and Nutrition, Lipid Clinic, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, Competing interests Barcelona 08036, Spain. 30 Navarra Institute of Public Health. Regional Health J.S.-S. reported receiving research support from the Instituto de Salud Carlos Service of Navarra, Pamplona, Spain. 31 Department of Public Health and Psy‑ III, Ministerio de Educación y Ciencia, the European Commission, the USA chiatry, School of Medicine, Biomedical Research Institute of Malaga (IBIMA), National Institutes of Health; receiving consulting fees or travel expenses University of Malaga, Málaga 29010, Spain. 32 Centro Salud Raval, Elche‑Ali‑ from Eroski Foundation and Instituto Danone, receiving nonfinancial cante 03203, Spain. 33 Navarra Regional Health Service, Primary Health Care support from Hojiblanca, Patrimonio Comunal Olivarero, the California Services, Pamplona, Spain. Almond Board of California, Pistachio Growers and Borges S.A; serving on the board of and receiving grant support through his institution from the Received: 9 January 2023 Accepted: 27 June 2023 International Nut and Dried Foundation and the Eroski Foundation; and personal fees from Instituto Danone Spain; Serving in the Board of Danone García et al. Globalization and Health (2023) 19:50 Page 10 of 11 References 20. Campos-Nonato I, Hernandez L, Barquera S. Effect of a high-protein Diet 1. Moreno Rodríguez JM. (coordinator). A Preliminary Assessment of the versus Standard-Protein Diet on Weight loss and biomarkers of metabolic Impacts in Spain due to the Effects of Climate Change. ECCE project, final syndrome: a Randomized Clinical Trial. Obes Facts. 2017;10:238–51. report. Madrid: Ministerio de Medio Ambiente, 765 p., 2005. 21. Velázquez-López L, Santiago-Díaz G, Nava-Hernández J, Muñoz-Torres 2. Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R et al. Cli‑ AV, Medina-Bravo P, Torres-Tamayo M. Mediterranean-style diet reduces mate Change 2014: Synthesis Report. Contribution of Working Groups I, II metabolic syndrome components in obese children and adolescents and III to the Fifth Assessment Report of the Intergovernmental Panel on with obesity. BMC Pediatr. 2014;14:175. Climate Change. Geneva, Switzerland. R. Pachauri and L. Meyer, editors, 22. Sayón-Orea C, Razquin C, Bulló M, Corella D, Fitó M, Romaguera D, et al. Effect 2015. Available from: https://​www.​ipcc.​ch/​site/​assets/​uploa​ds/​2018/​05/​ of a nutritional and behavioral intervention on Energy-Reduced Mediterra‑ SYR_​AR5_​FINAL_​full_​wcover.​pdf. Accessed 19 Nov 2021. nean Diet adherence among patients with metabolic syndrome: interim anal‑ 3. United Nations (UN). UN News. Global perspective Human stories; 09 Mar ysis of the PREDIMED-Plus Randomized Clinical Trial. JAMA. 2019;322:1486–99. 2019. Food systems account for over one-third of global greenhouse gas 23. Fortin A, Rabasa-Lhoret R, Lemieux S, Labonté ME, Gingras V. Comparison emissions. Available from: https://​news.​un.​org/​en/​story/​2021/​03/​10868​ of a Mediterranean to a low-fat diet intervention in adults with type 1 22. Accessed 19 Nov 2021. diabetes and metabolic syndrome: a 6–month randomized trial. Nutr 4. Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S, et al. Metab Cardiovasc Dis. 2018;28:1275–84. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets 24. Babio N, Toledo E, Estruch R, Ros E, Martínez-González MA, Castañer O, from sustainable food systems. Lancet. 2019;393:447–92. et al. Mediterranean diets and metabolic syndrome status in the PRED‑ 5. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA IMED randomized trial. CMAJ. 2014;186:E649–57. et al. Harmonizing the metabolic syndrome: a joint interim statement of 25. Wilson N, Cleghorn CL, Cobiac LJ, Mizdrak A, Nghiem N. Achieving the International Diabetes Federation Task Force on Epidemiology and healthy and sustainable diets: a review of the results of recent Math‑ Prevention; National Heart, Lung, and Blood Institute; American Heart ematical optimization studies. Adv Nutr. 2019;10:389–S403. Association; World Heart Federation; International Atherosclerosis Soci‑ 26. Riediger ND, Clara I. Prevalence of metabolic syndrome in the Canadian ety; and International Association for the Study of Obesity. Circulation. adult population. CMAJ. 2011;183:E1127-E1134. 2009;120. 1640–1645. 27. Fernández-Bergés D, Cabrera de León A, Sanz H, Elosua R, Guembe MJ, 6. Engin A. The definition and prevalence of obesity and metabolic syn‑ Alzamora M, et al. Metabolic syndrome in Spain: prevalence and coronary drome. Adv Exp Med Biol. 2017;960:1–17. risk associated with harmonized definition and WHO proposal. DARIOS 7. Sherling DH, Perumareddi P, Hennekens CH. Metabolic syndrome: clinical study. Rev Esp Cardiol (Engl Ed). 2012;65:241–8. and policy implications of the New Silent Killer. J Cardiovasc Pharmacol 28. Beltrán-Sánchez H, Harhay MO, Harhay MM, McElligott S. Prevalence and Therap. 2017;22:365–7. Trends of metabolic syndrome in the adult U.S. Population, 1999–2010. J 8. Moreira GC, Cipullo JP, Souza-Ciorlia LA, Bernardi-Cesarino C, Vilela-Martin Am Coll Cardiol. 2013;62:697–703. JF. Prevalence of metabolic syndrome: Association with risk factors 29. Salas-Salvadó J, Díaz-López A, Ruiz-Canela M, Basora J, Fitó M, Corella D, et al. and cardiovascular complications in an urban population. PLoS ONE. Effect of a lifestyle intervention program with energy-restricted Mediter‑ 2014;9:e105056. ranean diet and exercise on weight loss and cardiovascular risk factors: 9. Instituto Nacional de Estadística. INE. Defunciones según causa de one-year results of the PREDIMED-Plus trial. Diabetes Care. 2019;42:777–88. muerte. Avance enero-mayo 2020. Available from: https://​www.​ine.​es/​ 30. IEA. Global CO2 emissions in 2019. Available from: https://​www.​iea.​org/​ mapas/​svg/​indic​adore​sDefu​ncion​Causa.​htm. Accessed 19 Nov 2021. artic​les/​global-​co2-​emiss​ions-​in-​2019. Accessed 19 Nov 2021. 10. O’Neill S, O’Driscoll L. Metabolic syndrome: a closer look at the growing 31. Fresán U, Martínez-Gonzalez MA, Sabaté J, Bes-Rastrollo M. The Mediter‑ epidemic and its associated pathologies. Obes Rev. 2015;16:1–12. ranean diet, an environmentally friendly option: evidence from the 11. Wiley JF, Carrington MJ. A metabolic syndrome severity score: a tool to Seguimiento Universidad de Navarra (SUN) cohort. Public Health Nutr. quantify cardio-metabolic risk factors. Prev Med. 2016;88:189–95. 2018;21:1573–82. 12. Saklayen MG. The global epidemic of the metabolic syndrome. Curr 32. Burlingame B, Dernini S. Sustainable Diets and Biodiversity: Direc‑ Hypertens Rep. 2018;20:12. tions and Solutions for Policy, Research and Action. Proceedings of the 13. Renninger M, Hansen BH, Steene-Johannessen J, Kriemler S, Froberg International Scientific Symposium ‘Biodiversity and Sustainable Diets K, Northstone K, et al. Associations between accelerometry measured United Against Hunger’. Rome: FAO Headquarters; 3–5 November 2010 physical activity and sedentary time and the metabolic syndrome: a [accessed September 2022]. Available: http://​www.​fao.​org/​docrep/​016/​ meta-analysis of more than 6000 children and adolescents. Pediatr Obes. i3004e/​i3004e.​pdf. 2020;15:e12578. 33. Martínez-González MA, Buil-Cosiales P, Corella D, Bulló M, Fitó M, Vioque 14. Gallardo-Alfaro L, Bibiloni MM, Mascaró CM, Montemayor S, Ruiz-Canela J, et al. Cohort profile: design and methods of the PREDIMED-Plus rand‑ M, Salas-Salvado J, et al. Leisure-time physical activity, sedentary behav‑ omized trial. Int J Epidemiol. 2019;48:387–388o. iour and diet quality are associated with metabolic syndrome severity: 34. Fernández-Ballart JD, Piñol JL, Zazpe I, Corella D, Carrasco P, Toledo E, et al. the PREDIMED-plus study. Nutrients. 2020;12:1013. Relative validity of a semi-quantitative food-frequency questionnaire in an 15. Earnest CP, Johannsen NM, Swift DL, Gillison FB, Mikus CR, Lucia A, et al. elderly Mediterranean population of Spain. Br J Nutr. 2010;103:1808–16. Aerobic and strength training in concomitant metabolic syndrome and 35. Martin-Moreno JM, Boyle P, Gorgojo L, Maisonneuve P, Fernandez-Rodri‑ type 2 diabetes. Med Sci Sports Exerc. 2014;46:1293–301. guez JC, Salvini S, et al. Development and validation of a food frequency 16. Tai HC, Tzeng IS, Liang YC, Liao HH, Su CH, Kung WM. Interventional questionnaire in Spain. Int J Epidemiol. 1993;22:512–9. effects of weight-loss policy in a healthy city among participants with 36. de la Fuente-Arrillaga C, Ruiz ZV, Bes-Rastrollo M, Sampson L, Martinez- metabolic syndrome. Int J Environ Res Public Health. 2019;16:323. González MA. Reproducibility of an FFQ validated in Spain. Public Health 17. Dieli-Conwright CM, Courneya KS, Demark-Wahnefried W, Sami N, Lee Nutr. 2010;13:1364–72. K, Buchanan TA et al. Effects of Aerobic and Resistance Exercise on 37. Lupiañez-Barbero A, González Blanco C, de Leiva Hidalgo A. Spanish food Metabolic Syndrome, Sarcopenic Obesity, and Circulating Biomark‑ composition tables and databases: need for a gold standard for health‑ ers in Overweight or Obese Survivors of Breast Cancer: A Randomized care professionals. Tablas y bases de datos de composición de alimentos Controlled Trial [published correction appears in J Clin Oncol. 2020 Apr españolas: necesidad de un referente para los profesionales de la salud. 20;38(12):1370] [published correction appears in J Clin Oncol. 2020 Jun Endocrinol Diabetes Nutr (Engl Ed). 2018;65:361–73. 20;38(18):2115]. J Clin Oncol. 2018;36:875–883. 38. Mataix J, Mañas M, Llopis J, Martínez de Victoria E, Juan J, Borregón A. 18. Julibert A, Bibiloni MM, Mateos D, Angullo E, Tur JA. Dietary fat intake and Tablas de Composición de Alimentos (Spanish Food Composition tables). metabolic syndrome in older adults. Nutrients. 2019;11:1901. 5th ed. Granada: Universidad de Granada; 2013. 19. Wilkinson MJ, Manoogian ENC, Zadourian A, Lo H, Fakhouri S, Shoghi A, 39. Álvarez-Álvarez I, Martínez-González M, Sánchez-Tainta A, Corella D, Díaz- et al. Ten-hour time-restricted eating reduces weight, blood pressure, López A, Fitó M, et al. Adherence to an energy-restricted Mediterranean and atherogenic lipids in patients with metabolic syndrome. Cell Metab. Diet score and prevalence of cardiovascular risk factors in the PREDIMED- 2020;31:92–104e5. Plus: a cross-sectional study. Rev Esp Cardiol (Engl Ed). 2019;72:925–34. García et al. Globalization and Health (2023) 19:50 Page 11 of 11 40. Galilea-Zabalza I, Buil-Cosiales P, Salas-Salvadó J, Toledo E, Ortega-Azorín 62. Esposito K, Chiodini P, Colao A, Lenzi A, Giugliano D. Metabolic syndrome C, Díez-Espino J et al. Mediterranean diet and quality of life: Baseline and risk of cancer: a systematic review and meta-analysis. Diabetes Care. cross-sectional analysis of the PREDIMED-PLUS trial. PLoS ONE 2018, 13, 2012;35:2402–11. e0198974. 63. Moreira GC, Cipullo JP, Ciorlia LAS, Cesarino CB, Vilela-Martin JF. Preva‑ 41. Hartikainen H, Pulkkinen H. Summary of the chosen methodologies lence of metabolic syndrome: Association with risk factors and cardiovas‑ and practices to produce GHGE-estimates for an average European cular complications in an urban population. PLoS ONE 2014, 9, e105056. diet. 2016 Available from: http://​luke.​juven​espri​nt.​fi. Accessed 19 64. Chen C, Chaudhary A, Mathys A. Dietary change scenarios and implica‑ Nov2021. tions for environmental, nutrition, human health and economic dimen‑ 42. American Diabetes Association. 5. Lifestyle Management: Standards of sions of food sustainability. Nutrients. 2019;11:856. Medical Care in Diabetes-2019. Diabetes Care. 2019;42(Suppl 1):S46-S60. 65. Zheutlin AR, Adar SD, Park SK. Carbon dioxide emissions and change in 43. Mancia G, Fagard R, Narkiewicz K, Redón J, Zanchetti A, Böhm M, et al. prevalence of obesity and diabetes in the United States: an ecological 2013 ESH/ESC Guidelines for the management of arterial hyperten‑ study. Environ Int. 2014;73:111–6. sion: the Task Force for the management of arterial hypertension of the 66. García S, Pastor R, Monserrat-Mesquida M, Álvarez-Álvarez L, Rubín-García European Society of Hypertension (ESH) and of the European Society of M, Martínez-González MA et al. Ultra-processed foods consumption as a Cardiology (ESC). J Hypertens. 2013;31(7):1281–357. promoting factor of greenhouse gas emissions, water, energy, and land 44. Lozano JA. Dislipidemias. Pautas para su abordaje terapéutico. Offarm. use: A longitudinal assessment. Sci Total Environ. 2023;891:164417. 2005;24(9):100–8. 67. Gupta S, Hawk T, Aggarwal A, Drewnowski A. Characterizing Ultra-Pro‑ 45. World Health Organization; 9. June 2021. Obesity and overweight. Avail‑ cessed Foods by Energy Density, Nutrient Density, and cost. Front Nutr. able from: https://​www.​who.​int/​es/​news-​room/​facts​heets/​detail/​obesi​ 2019;6:70. ty-​and-​overw​eight. Accessed 12 June 2023. 68. World Health Organization; 21. March 2023. Commercial determinants 46. Martínez-González MA, Buil-Cosiales P, Corella D, Bulló M, Fitó M, Vioque of health. Available from: https://​www.​who.​int/​news-​room/​fact-​sheets/​ J, et al. Cohort profile: design and methods of the PREDIMED-Plus rand‑ detail/​comme​rcial-​deter​minan​ts-​of-​health. Accessed 12 June 2023. omized trial. Int J Epidemiol. 2019;48(2):387–8. 69. World Health Organization. ; 2023. THE 17 GOALS. Available from: https://​ 47. Carrington MJ, Jennings GL, Clark RA, Stewart S. Assessing cardiovascular sdgs.​un.​org/​goals. Accessed 12 June 2023. risk in regional areas: the healthy Hearts - Beyond City limits program. 70. Anstey MH. Climate change and health–what’s the problem? Global BMC Health Serv. 2012;12:296. Health. 2013;9:4. 48. Mahalanobis PC. On the generalized distance in statistics. Proc Natl Inst 71. Hutton G. The economics of health and climate change: key evidence for Sci. 1936;2:49–55. decision making. Global Health. 2011;7:18. 49. Elosua R, Garcia M, Aguilar A, Molina L, Covas MI, Marrugat J. Validation 72. Fresán U, Martínez-González MA, Sabaté J, Bes-Rastrollo M. Global of the Minnesota Leisure Time Physical Activity Questionnaire in Spanish sustainability (health, environment and monetary costs) of three dietary Women. Med Sci Sports Exerc. 2000;32(8):1431–7. patterns: results from a spanish cohort (the SUN project). BMJ Open 50. Elosua R, Marrugat J, Molina L, Pons S, Pujol E. Validation of the Minnesota 2019,9, e021541. Leisure Time Physical Activity Questionnaire in spanish men. Am J Epide‑ miol. 1994;139(12):1197–209. 51. Molina L, Sarmiento M, Peñafiel J, Donaire D, Garcia-Aymerich J, Gomez Publisher’s Note M, et al. Validation of the Regicor Short Physical Activity Questionnaire for Springer Nature remains neutral with regard to jurisdictional claims in pub‑ the Adult Population. PLoS ONE. 2017;12(1):e0168148. lished maps and institutional affiliations. 52. Martínez-González MA, López-Fontana C, Varo JJ, Sánchez-Villegas A, Martinez JA. Validation of the spanish version of the physical activity questionnaire used in the Nurses’ Health Study and the Health Profession‑ als’ follow-up study. Public Health Nutr. 2005;8(7):920–7. 53. García S, Bouzas C, Mateos D, Pastor R, Álvarez L, Rubín M, et al. Carbon dioxide ­(CO2) emissions and adherence to Mediterranean diet in an adult population: the Mediterranean diet index as a pollution level index. Environ Health. 2023;22(1):1. 54. Behrens P, Kiefte-De Jong JC, Bosker T, Rodrigues JFD, de Koning A, Tukker A. Evaluating the environmental impacts of dietary recommendations. Proc Natl Acad Sci USA. 2017;114:13412–7. 55. Grosso G, Fresán U, Bes-Rastrollo M, Marventano S, Galvano F. Environ‑ mental impact of dietary choices: role of the Mediterranean and other dietary patterns in an italian cohort. Int J Environ Res Public Health. 2020;17:1468. 56. Perignon M, Vieux F, Soler LG, Masset G, Darmon N. Improving diet sus‑ tainability through evolution of food choices: review of epidemiological studies on the environmental impact of diets. Nutr Rev. 2017;75:2–17. 57. Springmann M, Wiebe K, Mason-D’Croz D, Sulser TB, Rayner M, Scarbor‑ ough P. Health and nutritional aspects of sustainable diet strategies and Ready to submit your research ? Choose BMC and benefit from: their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet Health. 2018;2:e451–61. • fast, convenient online submission 58. World Health Organization, Food and Agriculture Organization of the United Nations; 29. October 2019. Sustainable healthy diets: guiding prin‑ • thorough peer review by experienced researchers in your field ciples. Available from: https://​www.​who.​int/​publi​catio​ns/i/​item/​97892​ • rapid publication on acceptance 41516​648. Accessed 19 Nov 2021. • support for research data, including large and complex data types 59. Springmann M, Spajic L, Clark MA, Poore J, Herforth A, Webb P, et al. The healthiness and sustainability of national and global food based dietary • gold Open Access which fosters wider collaboration and increased citations guidelines: modelling study. BMJ. 2020;370:2322. • maximum visibility for your research: over 100M website views per year 60. Springmann M, Charles H, Godfray J, Rayner M, Scarborough P. Analysis and valuation of the health and climate change co-benefits of dietary At BMC, research is always in progress. change. Proc Natl Acad Sci USA. 2016;113:4146–4151. 61. Tilman D, Clark M. Global diets link environmental sustainability and Learn more biomedcentral.com/submissions human health. Nature. 2014;515:518–22.