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

• Few epidemiologic studies have focused on nutritional status of nonagenarians.
• Many screening tools exist to identify the risk of malnutrition in the elderly.
• Two bioelectrical impedance vector analysis (BIVA) approaches exist: the classic method and the specific method.
• The specific BIVA was more correlated with other nutritional indicators.

ObjectivesThe 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.MethodsIn 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.ResultsAll 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.ConclusionsOur 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.