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Risk of malnutrition in a sample of nonagenarians: Specific versus classic bioelectrical impedance vector analysis

Bonaccorsi, Guglielmo, Santomauro, Francesca, Lorini, Chiara, Indiani, Laura, Pellegrino, Elettra, Pasquini, Guido, Molino-Lova, Raffaele, Epifani, Francesco, Macchi, Claudio
Nutrition 2016 v.32 no.3 pp. 368-374
arm circumference, bioelectrical impedance, body composition, body mass index, calf circumference, malnutrition, men, nutrition risk assessment, nutritional status, screening, surveys, women, Italy
The aims were to describe the body composition and the risk of malnutrition in a sample of nonagenarians and to identify what bioelectrical impedance vector analysis (BIVA) approach (classic or specific) is more correlated with either the risk of malnutrition or various anthropometric parameters.In the Mugello area (Italy), a representative sample of nonagenarians was enrolled in a survey aimed at investigating various health issues, including those related to nutritional status. The nutritional status was investigated using body mass index (BMI), mid-upper arm circumference (MUAC), calf circumference (CC), Malnutrition Universal Screening Tool (MUST), and BIVA. Two different approaches were used for the BIVA data analysis: the classic method and the specific method.All measurements were obtained from 321 nonagenarians (65.7% of the enrolled sample; 92 men and 229 women); 74.8% of the subjects were at low risk of malnutrition according to MUST, 62.5% exhibited CC values higher than 31 cm, and 86.8% exhibited MUAC values higher than 22 cm. The bioelectrical parameters varied with nutritional status, independent of the nutritional indicator used; the parameters also varied based on sex and BIVA approach. The bioelectrical parameters obtained by the specific BIVA approach were more strongly correlated with MUST score, MUAC, and CC values compared with the parameters calculated using the classic approach.Our study produced findings relevant to particular aspects of population aging. Above all, the specific BIVA was more effective at assessing nutritional status based on both anthropometric parameters and the risk of malnutrition.