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      Associations Between the Modified Food Standard Agency Nutrient Profiling System Dietary Index and Cardiovascular Risk Factors in an Elderly Population

      research-article
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      Frontiers in Nutrition
      Frontiers Media S.A.
      front of pack food labeling, cardiovascular risk factor, body weight, FSAm-NPS dietary index, PREDIMED-Plus study

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          Abstract

          Background

          Helping consumers to improve the nutritional quality of their diet is a key public health action to prevent cardiovascular diseases (CVDs). The modified version of the Food Standard Agency Nutrient Profiling System Dietary Index (FSAm-NPS DI) underpinning the Nutri-Score front-of-pack label has been used in public health strategies to address the deleterious consequences of poor diets. This study aimed to assess the association between the FSAm-NPS DI and some CVD risk factors including body mass index (BMI), waist circumference, plasma glucose levels, triglyceride levels, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol, and diastolic and systolic blood pressure.

          Materials and Methods

          Dietary intake was assessed at baseline and after 1 year of follow-up using a 143-item validated semi-quantitative food-frequency questionnaire. Dietary indices based on FSAm-NPS applied at an individual level were computed to characterize the diet quality of 5,921 participants aged 55–75 years with overweight/obesity and metabolic syndrome from the PREDIMED-plus cohort. Associations between the FSAm-NPS DI and CVD risk factors were assessed using linear regression models.

          Results

          Compared to participants with a higher nutritional quality of diet (measured by a lower FSAm-NPS DI at baseline or a decrease in FSAm-NPS DI after 1 year), those participants with a lower nutritional quality of diet (higher FSAm-NPS DI or an increase in score) showed a significant increase in the levels of plasma glucose, triglycerides, diastolic blood pressure, BMI, and waist circumference (β coefficient [95% confidence interval]; P for trend) (1.67 [0.43, 2.90]; <0.001; 6.27 [2.46, 10.09]; <0.001; 0.56 [0.08, 1.05]; 0.001; 0.51 [0.41, 0.60]; <0.001; 1.19 [0.89, 1.50]; <0.001, respectively). No significant associations in relation to changes in HDL and LDL-cholesterol nor with systolic blood pressure were shown.

          Conclusion

          This prospective cohort study suggests that the consumption of food items with a higher FSAm-NPS DI is associated with increased levels of several major risk factors for CVD including adiposity, fasting plasma glucose, triglycerides, and diastolic blood pressure. However, results must be cautiously interpreted because no significant prospective associations were identified for critical CVD risk factors, such as HDL and LDL-cholesterol, and systolic blood pressure.

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          Most cited references37

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          Dietary assessment methods in epidemiologic studies

          Diet is a major lifestyle-related risk factor of various chronic diseases. Dietary intake can be assessed by subjective report and objective observation. Subjective assessment is possible using open-ended surveys such as dietary recalls or records, or using closed-ended surveys including food frequency questionnaires. Each method has inherent strengths and limitations. Continued efforts to improve the accuracy of dietary intake assessment and enhance its feasibility in epidemiological studies have been made. This article reviews common dietary assessment methods and their feasibility in epidemiological studies.
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            Ultraprocessed food and chronic noncommunicable diseases: A systematic review and meta‐analysis of 43 observational studies

            This systematic review and meta-analysis investigated the association between consumption of ultraprocessed food and noncommunicable disease risk, morbidity and mortality. Forty-three observational studies were included (N = 891,723): 21 cross-sectional, 19 prospective, two case-control and one conducted both a prospective and cross-sectional analysis. Meta-analysis demonstrated consumption of ultraprocessed food was associated with increased risk of overweight (odds ratio: 1.36; 95% confidence interval [CI], 1.23-1.51; P < 0.001), obesity (odds ratio: 1.51; 95% CI, 1.34-1.70; P < 0.001), abdominal obesity (odds ratio: 1.49; 95% CI, 1.34-1.66; P < 0.0001), all-cause mortality (hazard ratio: 1.28; 95% CI, 1.11-1.48; P = 0.001), metabolic syndrome (odds ratio: 1.81; 95% CI, 1.12-2.93; P = 0.015) and depression in adults (hazard ratio: 1.22; 95% CI, 1.16-1.28, P < 0.001) as well as wheezing (odds ratio: 1.40; 95% CI, 1.27-1.55; P < 0.001) but not asthma in adolescents (odds ratio: 1.20; 95% CI, 0.99-1.46; P = 0.065). In addition, consumption of ultraprocessed food was associated with cardiometabolic diseases, frailty, irritable bowel syndrome, functional dyspepsia and cancer (breast and overall) in adults while also being associated with metabolic syndrome in adolescents and dyslipidaemia in children. Although links between ultraprocessed food consumption and some intermediate risk factors in adults were also highlighted, further studies are required to more clearly define associations in children and adolescents. STUDY REGISTRATION: Prospero ID: CRD42020176752.
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              Reproducibility of an FFQ validated in Spain.

              To evaluate the reproducibility of a semi-quantitative FFQ used in the Seguimiento Universidad de Navarra (SUN) project. The data that were analysed were collected from an FFQ answered twice by a 326-participant subsample of the SUN project (115 men, 35.3 %; 211 women, 64.7 %), with either less than 1 year or more than 1 year between responses. The questionnaire included 136 items. Pearson correlation coefficients (r) were calculated to evaluate the magnitude of the association between both measures after energy adjustment and correcting for within-person variability. We also evaluated misclassification by quintiles distribution. The highest corrected correlations among participants who answered before 1 year were found for PUFA (r = 0.99). Among participants who answered after 1 year between both questionnaires, olive oil had the highest corrected correlation (r = 0.99). The highest percentage of gross misclassification, lowest quintile in FFQ1 and highest quintile in FFQ2 or highest quintile in FFQ1 and lowest quintile in FFQ2 was for cereals, fish or seafood, and n-3 fatty acids (7.6 %). Alcoholic drinks had the highest percentage of reasonable classification, same or adjacent quintile, in FFQ1 and FFQ2 (86.4 %). Our study suggests that FFQ reproducibility is acceptable for participants who answered the same questionnaire twice less than 1 year apart. Participants who answered FFQ more than 1 year apart showed worse values on reproducibility. We consider this Spanish FFQ as an important, valid and reproducible tool in nutritional epidemiology.
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                Author and article information

                Contributors
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                Journal
                Front Nutr
                Front Nutr
                Front. Nutr.
                Frontiers in Nutrition
                Frontiers Media S.A.
                2296-861X
                14 July 2022
                2022
                : 9
                : 897089
                Affiliations
                [1] 1Universitat Rovira i Virgili, Departament de Bioquimica i Biotecnologia, Unitat de Nutrició Humana , Reus, Spain
                [2] 2Institut d’Investigació Sanitària Pere Virgili (IISPV) , Reus, Spain
                [3] 3Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III , Madrid, Spain
                [4] 4Department of Preventive Medicine and Public Health, IDISNA, University of Navarra , Pamplona, Spain
                [5] 5Department of Nutrition, Harvard T.H. Chan School of Public Health , Boston, MA, United States
                [6] 6Department of Preventive Medicine, University of Valencia , Valencia, Spain
                [7] 7Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d‘Investigació Médica (IMIM) , Barcelona, Spain
                [8] 8Department of Nutrition, Food Sciences, and Physiology, Center for Nutrition Research, University of Navarra , Pamplona, Spain
                [9] 9Precision Nutrition and Cardiometabolic Health Program, IMDEA Food, CEI UAM + CSIC , Madrid, Spain
                [10] 10Osakidetza Basque Health Service, Araba University Hospital, Bioaraba Health Research Institute, University of the Basque Country UPV/EHU , Vitoria-Gasteiz, Spain
                [11] 11Department of Public Health and Psychiatry, Biomedical Research Institute of Malaga (IBIMA), University of Málaga , Málaga, Spain
                [12] 12CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III , Madrid, Spain
                [13] 13Instituto de Investigación Sanitaria y Biomédica de Alicante, Universidad Miguel Hernández (ISABIAL-UMH) , Alicante, Spain
                [14] 14Health Research Institute of the Balearic Islands (IdISBa) , Palma de Mallorca, Spain
                [15] 15Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Córdoba , Córdoba, Spain
                [16] 16Department of Endocrinology, Virgen de la Victoria Hospital, Instituto de Investigación Biomédica de Málaga (IBIMA), University of Málaga , Málaga, Spain
                [17] 17Research Unit, Department of Family Medicine, Distrito Sanitario Atención Primaria Sevilla , Seville, Spain
                [18] 18Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria and Centro Hospitalario Universitario Insular Materno Infantil (CHUIMI), Canarian Health Service , Las Palmas de Gran Canaria, Spain
                [19] 19Department of Preventive Medicine and Public Health, University of Granada , Granada, Spain
                [20] 20Preventive Medicine Unit, Universitary Hospital Virgen de las Nieves , Granada, Spain
                [21] 21Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA) , Granada, Spain
                [22] 22Research Group on Community Nutrition and Oxidative Stress, University of Balearic Islands-IUNICS , Palma de Mallorca, Spain
                [23] 23Institute of Biomedicine (IBIOMED), University of León , León, Spain
                [24] 24Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge, Hospitalet de Llobregat , Barcelona, Spain
                [25] 25Division of Preventive Medicine, Faculty of Medicine, University of Jaén , Jaén, Spain
                [26] 26Department of Endocrinology and Nutrition, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdISSC) , Madrid, Spain
                [27] 27CIBER Diabetes y Enfermedades Metabólicas (CIBERDEM), Instituto de Salud Carlos III (ISCIII) , Madrid, Spain
                [28] 28Department of Endocrinology, Institut d‘Investigacions Biomédiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona , Barcelona, Spain
                [29] 29Department of Endocrinology and Nutrition, Hospital Fundación Jimenez Díaz, Instituto de Investigaciones Biomédicas IISFJD, University of Autonoma , Madrid, Spain
                [30] 30Nutritional Control of the Epigenome Group, Precision Nutrition and Obesity Program, IMDEA Food, CEI UAM + CSIC , Madrid, Spain
                [31] 31Department of Endocrinology and Nutrition, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, Lipid Clinic , Barcelona, Spain
                [32] 32School of Health Sciences, Blanquerna-Ramon Llull University , Barcelona, Spain
                [33] 33Department of Endocrinology and Nutrition, Hospital Universitario de Navarra , Pamplona, Spain
                Author notes

                Edited by: Xiongfei Pan, Sichuan University, China

                Reviewed by: Amanda Grech, The University of Sydney, Australia; Torsten Bohn, Luxembourg Institute of Health, Luxembourg

                *Correspondence: Nancy Babio, nancy.babio@ 123456urv.cat
                Jordi Salas Salvadó, jordi.salas@ 123456urv.cat

                These authors have contributed equally to this work

                These authors share senior authorship

                This article was submitted to Nutritional Epidemiology, a section of the journal Frontiers in Nutrition

                Article
                10.3389/fnut.2022.897089
                9364822
                35967785
                c26087b3-a58d-4daa-b5f6-a778c9140529
                Copyright © 2022 Khoury, Gómez-Donoso, Martínez, Martínez-González, Corella, Fitó, Martínez, Alonso-Gómez, Wärnberg, Vioque, Romaguera, León-Acuña, Tinahones, Santos-Lozano, Serra-Majem, Massó Guijarro, Tur, Martín Sánchez, Pintó, Delgado-Rodríguez, Matía-Martín, Vidal, Vázquez, Daimiel, Ros, Bes-Rastrollo, Barragan, Castañer, Torres-Peña, Notario-Barandiaran, Muñoz-Bravo, Abete, Prohens, Cano-Ibáñez, Tojal Sierra, Fernández-García, Sayon-Orea, Pascual, Sorli, Zomeño, Peña-Orihuela, Signes-Pastor, Basterra-Gortari, Schröeder, Salas Salvadó and Babio.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 March 2022
                : 22 June 2022
                Page count
                Figures: 2, Tables: 3, Equations: 1, References: 41, Pages: 12, Words: 9322
                Categories
                Nutrition
                Original Research

                front of pack food labeling,cardiovascular risk factor,body weight,fsam-nps dietary index,predimed-plus study

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