Carlos Celis-Morales1,2, Katherine M Livingstone1, Alexander Affleck2, Santiago Navas-Carretero3, Rodrigo San-Cristobal3, J Alfredo Martinez3, Cyril F M Marsaux4, Wim H M Saris4, Clare B O'Donovan5, Hannah Forster5, Clara Woolhead5, Eileen R Gibney5, Marianne C Walsh5, Lorraine Brennan5, Mike Gibney5, George Moschonis6, Christina-Paulina Lambrinou6, Christina Mavrogianni6, Yannis Manios6, Anna L Macready7, Rosalind Fallaize7, Julie A Lovegrove7, Silvia Kolossa8, Hannelore Daniel8, Iwona Traczyk9, Christian A Drevon10, John C Mathers11. 1. Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle Upon Tyne, UK. 2. BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, G12 8TA, UK. 3. Department of Nutrition, Food Science and Physiology, University of Navarra, Pamplona, Spain. 4. Department of Human Biology, NUTRIM, School for Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre, Maastricht, The Netherlands. 5. UCD Institute of Food and Health, UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland. 6. Department of Nutrition and Dietetics, Harokopio University, Athens, Greece. 7. Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research, University of Reading, Reading, UK. 8. ZIEL Research Center of Nutrition and Food Sciences, Biochemistry Unit, Technische Universität München, Munchen, Germany. 9. Human Nutrition Department, Faculty of Health Sciences, Medical University of Warsaw, Warsaw, Poland. 10. Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway. 11. Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle Upon Tyne, UK. john.mathers@newcastle.ac.uk.
Abstract
BACKGROUND/ OBJECTIVES: To identify predictors of obesity in adults and investigate to what extent these predictors are independent of other major confounding factors. SUBJECTS/ METHODS: Data collected at baseline from 1441 participants from the Food4Me study conducted in seven European countries were included in this study. A food frequency questionnaire was used to measure dietary intake. Accelerometers were used to assess physical activity levels (PA), whereas participants self-reported their body weight, height and waist circumference via the internet. RESULTS: The main factors associated (p < 0.05) with higher BMI per 1-SD increase in the exposure were age (β:1.11 kg/m2), intakes of processed meat (β:1.04 kg/m2), red meat (β:1.02 kg/m2), saturated fat (β:0.84 kg/m2), monounsaturated fat (β:0.80 kg/m2), protein (β:0.74 kg/m2), total energy intake (β:0.50 kg/m2), olive oil (β:0.36 kg/m2), sugar sweetened carbonated drinks (β:0.33 kg/m2) and sedentary time (β:0.73 kg/m2). In contrast, the main factors associated with lower BMI per 1-SD increase in the exposure were PA (β:-1.36 kg/m2), intakes of wholegrains (β:-1.05 kg/m2), fibre (β:-1.02 kg/m2), fruits and vegetables (β:-0.52 kg/m2), nuts (β:-0.52 kg/m2), polyunsaturated fat (β:-0.50 kg/m2), Healthy Eating Index (β:-0.42 kg/m2), Mediterranean diet score (β:-0.40 kg/m2), oily fish (β:-0.31 kg/m2), dairy (β:-0.31 kg/m2) and fruit juice (β:-0.25 kg/m2). CONCLUSIONS: These findings are important for public health and suggest that promotion of increased PA, reducing sedentary behaviours and improving the overall quality of dietary patterns are important strategies for addressing the existing obesity epidemic and associated disease burden.
BACKGROUND/ OBJECTIVES: To identify predictors of obesity in adults and investigate to what extent these predictors are independent of other major confounding factors. SUBJECTS/ METHODS: Data collected at baseline from 1441 participants from the Food4Me study conducted in seven European countries were included in this study. A food frequency questionnaire was used to measure dietary intake. Accelerometers were used to assess physical activity levels (PA), whereas participants self-reported their body weight, height and waist circumference via the internet. RESULTS: The main factors associated (p < 0.05) with higher BMI per 1-SD increase in the exposure were age (β:1.11 kg/m2), intakes of processed meat (β:1.04 kg/m2), red meat (β:1.02 kg/m2), saturated fat (β:0.84 kg/m2), monounsaturated fat (β:0.80 kg/m2), protein (β:0.74 kg/m2), total energy intake (β:0.50 kg/m2), olive oil (β:0.36 kg/m2), sugar sweetened carbonated drinks (β:0.33 kg/m2) and sedentary time (β:0.73 kg/m2). In contrast, the main factors associated with lower BMI per 1-SD increase in the exposure were PA (β:-1.36 kg/m2), intakes of wholegrains (β:-1.05 kg/m2), fibre (β:-1.02 kg/m2), fruits and vegetables (β:-0.52 kg/m2), nuts (β:-0.52 kg/m2), polyunsaturated fat (β:-0.50 kg/m2), Healthy Eating Index (β:-0.42 kg/m2), Mediterranean diet score (β:-0.40 kg/m2), oily fish (β:-0.31 kg/m2), dairy (β:-0.31 kg/m2) and fruit juice (β:-0.25 kg/m2). CONCLUSIONS: These findings are important for public health and suggest that promotion of increased PA, reducing sedentary behaviours and improving the overall quality of dietary patterns are important strategies for addressing the existing obesity epidemic and associated disease burden.
Authors: Alicia Julibert; Maria Del Mar Bibiloni; Cristina Bouzas; Miguel Ángel Martínez-González; Jordi Salas-Salvadó; Dolores Corella; Maria Dolors Zomeño; Dora Romaguera; Jesús Vioque; Ángel M Alonso-Gómez; Julia Wärnberg; J Alfredo Martínez; Luís Serra-Majem; Ramon Estruch; Francisco J Tinahones; José Lapetra; Xavier Pintó; José Lopez-Miranda; Laura García-Molina; José Juan Gaforio; Pilar Matía-Martín; Lidia Daimiel; Vicente Martín-Sánchez; Josep Vidal; Clotilde Vázquez; Emili Ros; Estefanía Toledo; Nerea Becerra-Tomás; Olga Pórtoles; Karla A Pérez-Vega; Miquel Fiol; Laura Torres-Collado; Lucas Tojal-Sierra; Rosa Carabaño-Moral; Itziar Abete; Almudena Sanchez-Villegas; Rosa Casas; María Rosa Bernal-López; José Manuel Santos-Lozano; Ana Galera; Lucía Ugarriza; Miguel Ruiz-Canela; Nancy Babio; Oscar Coltell; Helmut Schröder; Jadwiga Konieczna; Domingo Orozco-Beltrán; Carolina Sorto-Sánchez; Sonia Eguaras; Laura Barrubés; Montserrat Fitó; Josep A Tur Journal: Nutrients Date: 2019-06-29 Impact factor: 5.717
Authors: Weiyan Gong; Fan Yuan; Ganyu Feng; Yanning Ma; Yan Zhang; Caicui Ding; Zheng Chen; Ailing Liu Journal: Int J Environ Res Public Health Date: 2020-02-04 Impact factor: 3.390