Montserrat Rabassa1,2, Raul Zamora-Ros3, Magalí Palau-Rodriguez1,2, Sara Tulipani1, Antonio Miñarro2,4, Stefania Bandinelli5, Luigi Ferrucci6, Antonio Cherubini7, Cristina Andres-Lacueva1,2. 1. Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, Food Technology Reference Net (XaRTA), Nutrition and Food Safety Research Institute (INSA), Faculty of Pharmacy and Food Sciences, University of Barcelona, 08028, Barcelona, Spain. 2. CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 08028, Barcelona, Spain. 3. Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), 08098, Barcelona, Spain. 4. Department of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain. 5. Geriatric Rehabilitation Unit, Azienda Sanitaria Firenze, 40125, Florence, Italy. 6. Clinical Research Branch, National Institute on Aging, NIH, 21224, Baltimore, MD, USA. 7. Geriatria, Accettazione geriatrica e Centro di ricerca per l'invecchiamento, IRCCS INRCA, 60124, Ancona, Italy.
Abstract
SCOPE: The association between self-reported dietary intake and urinary metabolomic markers of habitual nut exposure with cognitive decline over a 3-year follow-up in an older Italian population is prospectively evaluated. METHODS AND RESULTS: A total of 119 older participants are selected, based on self-referred nut intake: the non-nut consumer (n = 72) and the regular consumer (≥2.9 g d-1 , n = 47). Nut exposure is measured at baseline either with the use of a validated food frequency questionnaire or with an HPLC-Q-ToF-MS metabolomic approach. Three years after, 28 from the nonconsumers and 10 from the consumers experienced cognitive decline. Dietary nut exposure is characterized by urinary metabolites of polyphenols and fatty acids pathways. Nut consumption estimated either by the dietary marker or by the urinary marker model is in both cases associated with less cognitive decline (OR: 0.78, 95% CI: 0.61,0.99; p = 0.043 and OR: 0.995, 95% CI: 0.991,0.999; p = 0.016, respectively) with AUCs 73.2 (95% CI: 62.9, 83.6) and 73.1 (62.5, 83.7), respectively. CONCLUSIONS: A high intake of nuts may protect older adults from cognitive decline. Metabolomics provides accurate and complementary information of the nut exposure and reinforces the results obtained using dietary information.
SCOPE: The association between self-reported dietary intake and urinary metabolomic markers of habitual nut exposure with cognitive decline over a 3-year follow-up in an older Italian population is prospectively evaluated. METHODS AND RESULTS: A total of 119 older participants are selected, based on self-referred nut intake: the non-nut consumer (n = 72) and the regular consumer (≥2.9 g d-1 , n = 47). Nut exposure is measured at baseline either with the use of a validated food frequency questionnaire or with an HPLC-Q-ToF-MS metabolomic approach. Three years after, 28 from the nonconsumers and 10 from the consumers experienced cognitive decline. Dietary nut exposure is characterized by urinary metabolites of polyphenols and fatty acids pathways. Nut consumption estimated either by the dietary marker or by the urinary marker model is in both cases associated with less cognitive decline (OR: 0.78, 95% CI: 0.61,0.99; p = 0.043 and OR: 0.995, 95% CI: 0.991,0.999; p = 0.016, respectively) with AUCs 73.2 (95% CI: 62.9, 83.6) and 73.1 (62.5, 83.7), respectively. CONCLUSIONS: A high intake of nuts may protect older adults from cognitive decline. Metabolomics provides accurate and complementary information of the nut exposure and reinforces the results obtained using dietary information.
Authors: Ying Bao; Jiali Han; Frank B Hu; Edward L Giovannucci; Meir J Stampfer; Walter C Willett; Charles S Fuchs Journal: N Engl J Med Date: 2013-11-21 Impact factor: 91.245
Authors: Colin A Smith; Grace O'Maille; Elizabeth J Want; Chuan Qin; Sunia A Trauger; Theodore R Brandon; Darlene E Custodio; Ruben Abagyan; Gary Siuzdak Journal: Ther Drug Monit Date: 2005-12 Impact factor: 3.681
Authors: Lloyd W Sumner; Alexander Amberg; Dave Barrett; Michael H Beale; Richard Beger; Clare A Daykin; Teresa W-M Fan; Oliver Fiehn; Royston Goodacre; Julian L Griffin; Thomas Hankemeier; Nigel Hardy; James Harnly; Richard Higashi; Joachim Kopka; Andrew N Lane; John C Lindon; Philip Marriott; Andrew W Nicholls; Michael D Reily; John J Thaden; Mark R Viant Journal: Metabolomics Date: 2007-09 Impact factor: 4.290
Authors: Juan Carlos Espín; Mar Larrosa; María Teresa García-Conesa; Francisco Tomás-Barberán Journal: Evid Based Complement Alternat Med Date: 2013-05-28 Impact factor: 2.629
Authors: Talha Rafiq; Sandi M Azab; Koon K Teo; Lehana Thabane; Sonia S Anand; Katherine M Morrison; Russell J de Souza; Philip Britz-McKibbin Journal: Adv Nutr Date: 2021-12-01 Impact factor: 8.701
Authors: Lauren E Theodore; Nicole J Kellow; Emily A McNeil; Evangeline O Close; Eliza G Coad; Barbara R Cardoso Journal: Adv Nutr Date: 2021-06-01 Impact factor: 8.701