Author information
1 Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain.
2 Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
3 CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Institute of Health Carlos III, Madrid, Spain; Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, Palma de Mallorca, Spain.
4 CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Institute of Health Carlos III, Madrid, Spain; Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge, Hospitalet de Llobregat, Barcelona, Spain.
5 CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Institute of Health Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain.
6 CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Institute of Health Carlos III, Madrid, Spain; Department of Preventive Medicine, University of Valencia, Valencia, Spain.
7 CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Institute of Health Carlos III, Madrid, Spain; Virgen de la Victoria Hospital, Department of Endocrinology, University of Málaga, Málaga, Spain.
8 CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Institute of Health Carlos III, Madrid, Spain; Cardiovascular Risk and Nutrition research group (CARIN), Hospital del Mar Research Institute (IMIM), Barcelona, Spain.
9 CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Institute of Health Carlos III, Madrid, Spain; Department of Internal Medicine, IDIBAPS, Hospital Clinic, University of Barcelona, Barcelona, Spain.
10 CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Institute of Health Carlos III, Madrid, Spain; Lipid Clinic, Department of Endocrinology and Nutrition, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain.
11 CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Institute of Health Carlos III, Madrid, Spain; Rovira i Virgili University, Department of Biochemistry and Biotechnology, Human Nutrition Unit, University Hospital of Sant Joan de Reus, Pere Virgili Institute for Health Research, Reus, Spain.
12 Nutritional Genomics and Epigenomics Program, IMDEA Food, CEI UAM + CSIC, Madrid, Spain.
13 Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Institute of Health Carlos III, Madrid, Spain. Electronic address: mazulet@unav.es.
Abstract
OBJECTIVE: Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver morbidity. This condition often is accompanied by obesity, diabetes, and metabolic syndrome (MetS). The aim of this study was to evaluate the connection between lifestyle factors and NAFLD in individuals with MetS.
METHODS: A cross-sectional study with 328 participants (55-75 y of age) diagnosed with MetS participating in the PREDIMED-Plus trial was conducted. NAFLD status was evaluated using the non-invasive hepatic steatosis index (HSI). Sociodemographic, clinical, and dietary data were collected. Adherence to the Mediterranean diet (mainly assessed by the consumption of olive oil, nuts, legumes, whole grain foods, fish, vegetables, fruits, and red wine) and physical activity were assessed using validated questionnaires.
RESULTS: Linear regression analyses revealed that HSI values tended to be lower with increasing physical activity tertiles (T2, β = -1.47; 95% confidence interval [CI], -2.73 to -0.20; T3, β = -1.93; 95% CI, -3.22 to -0.65 versus T1, Ptrend = 0.001) and adherence to the Mediterranean diet was inversely associated with HSI values: (moderate adherence β = -0.70; 95% CI, -1.92 to 0.53; high adherence β = -1.57; 95% CI, -3.01 to -0.13 versus lower, Ptrend = 0.041). Higher tertiles of legume consumption were inversely associated with the highest tertile of HSI (T2, relative risk ratio [RRR], 0.45; 95% CI, 0.22-0.92; P = 0.028; T3, RRR, 0.48; 95% CI, 0.24-0.97; P = 0.041 versus T1).
CONCLUSION: Physical activity, adherence to the Mediterranean diet, and consumption of legumes were inversely associated with a non-invasive marker of NAFLD in individuals with MetS. This data can be useful in implementing precision strategies aimed at the prevention, monitoring, and management of NAFLD.