Roberta Zupo1, Fabio Castellana1, Vito Guerra1, Rossella Donghia1, Ilaria Bortone1, Chiara Griseta1, Luisa Lampignano1, Vittorio Dibello2, Madia Lozupone3, Hélio José Coelho-Júnior4, Vincenzo Solfrizzi5, Gianluigi Giannelli6, Giovanni De Pergola6, Heiner Boeing1,7, Rodolfo Sardone1, Francesco Panza1. 1. Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology "Saverio de Bellis", Research Hospital, Castellana Grotte, 70013 Bari, Italy. 2. Department of Orofacial Pain and Dysfunction, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. 3. Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy. 4. University of Campinas, Campinas, Brazil. 5. "C. Frugoni" Internal and Geriatric Medicine and Memory Unit, University of Bari Aldo Moro, Bari, Italy. 6. Scientific Direction, Research Hospital, National Institute of Gastroenterology "Saverio de Bellis", Castellana Grotte, Bari, Italy. 7. German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany.
Abstract
INTRODUCTION: Preventive nutritional management of frailty, a multidimensional intermediate status in the ageing process, may reduce the risk of adverse health-related outcomes. We investigated the ability of a measure combining physical frailty with nutritional imbalance, defined as nutritional frailty, to predict all-cause mortality over a period of up to 8 years. METHODS: We analysed data on 1,943 older adults from the population-based 'Salus in Apulia Study'. Physical frailty was operationalized using Cardiovascular Health Study criteria and cognitive frailty by combining physical frailty with cognitive impairment. A novel five-item construct was built to assess the extent of nutritional imbalance identified with a machine learning algorithm. Cox models and Kaplan-Meier survival probability analyses of physical frailty, nutritional imbalance (two or more of the following: low body mass index, low skeletal muscle index, ≥2.3 g/day sodium intake, <3.35 g/day potassium intake and <9.9 g/day iron intake), cognitive frailty and the novel nutritional frailty phenotype (physical frailty plus nutritional imbalance) were applied to assess all-cause mortality risk, adjusted for age, sex, education and multimorbidity. RESULTS: The overall prevalence of nutritional frailty was 4.52% (95% confidence interval, CI:3.55-5.44), being more frequent in males. Subjects with nutritional frailty were at higher risk for all-cause mortality [hazard ratio (HR):2.31; 95%CI:1.41-3.79] than those with physical frailty (HR:1.45,95% CI:1.0-2.02), nutritional imbalance (HR:1.39; 95%CI:1.05-1.83) and cognitive frailty (HR:1.06; 95%CI:0.56-2.01). CONCLUSIONS: Efforts to identify, manage and prevent frailty should include the nutritional domain. The nutritional frailty phenotype may highlight major nutritional determinants that could drive survival and health trajectories in older adults.
INTRODUCTION: Preventive nutritional management of frailty, a multidimensional intermediate status in the ageing process, may reduce the risk of adverse health-related outcomes. We investigated the ability of a measure combining physical frailty with nutritional imbalance, defined as nutritional frailty, to predict all-cause mortality over a period of up to 8 years. METHODS: We analysed data on 1,943 older adults from the population-based 'Salus in Apulia Study'. Physical frailty was operationalized using Cardiovascular Health Study criteria and cognitive frailty by combining physical frailty with cognitive impairment. A novel five-item construct was built to assess the extent of nutritional imbalance identified with a machine learning algorithm. Cox models and Kaplan-Meier survival probability analyses of physical frailty, nutritional imbalance (two or more of the following: low body mass index, low skeletal muscle index, ≥2.3 g/day sodium intake, <3.35 g/day potassium intake and <9.9 g/day iron intake), cognitive frailty and the novel nutritional frailty phenotype (physical frailty plus nutritional imbalance) were applied to assess all-cause mortality risk, adjusted for age, sex, education and multimorbidity. RESULTS: The overall prevalence of nutritional frailty was 4.52% (95% confidence interval, CI:3.55-5.44), being more frequent in males. Subjects with nutritional frailty were at higher risk for all-cause mortality [hazard ratio (HR):2.31; 95%CI:1.41-3.79] than those with physical frailty (HR:1.45,95% CI:1.0-2.02), nutritional imbalance (HR:1.39; 95%CI:1.05-1.83) and cognitive frailty (HR:1.06; 95%CI:0.56-2.01). CONCLUSIONS: Efforts to identify, manage and prevent frailty should include the nutritional domain. The nutritional frailty phenotype may highlight major nutritional determinants that could drive survival and health trajectories in older adults.
Authors: Vittorio Dibello; Frank Lobbezoo; Madia Lozupone; Rodolfo Sardone; Andrea Ballini; Giuseppe Berardino; Anita Mollica; Hélio José Coelho-Júnior; Giovanni De Pergola; Roberta Stallone; Antonio Dibello; Antonio Daniele; Massimo Petruzzi; Filippo Santarcangelo; Vincenzo Solfrizzi; Daniele Manfredini; Francesco Panza Journal: Geroscience Date: 2022-10-15 Impact factor: 7.581
Authors: Simon Mazeaud; Fabio Castellana; Hélio José Coelho-Junior; Francesco Panza; Mariangela Rondanelli; Federico Fassio; Giovanni De Pergola; Roberta Zupo; Rodolfo Sardone Journal: Metabolites Date: 2022-07-15
Authors: Gloria Liquori; Aurora De Leo; Daniele De Nuzzo; Victoria D'Inzeo; Rosario Marco Arancio; Emanuele Di Simone; Sara Dionisi; Noemi Giannetta; Francesco Ricciardi; Fabio Fabbian; Giovanni Battista Orsi; Marco Di Muzio; Christian Napoli Journal: Nutrients Date: 2022-09-28 Impact factor: 6.706