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Clin Obes. 2016 Dec;6(6):365-375. doi: 10.1111/cob.12165. Epub 2016 Nov 21.

Association between ratio indexes of body composition phenotypes and metabolic risk in Italian adults.

Author information

1
School of Biomedical Sciences, Faculty of Medical Sciences, Newcastle University, Newcastle, UK.
2
Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle, UK.
3
Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle, UK.
4
Department of Agricultural, Food and Nutritional Sciences, University of Alberta, Edmonton, Canada.
5
International Center for the Assessment of Nutritional Status, (ICANS Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, Milano, Italy.
6
Human Nutrition and Eating Disorders Research Centre, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, Pavia, Italy.

Abstract

The ratio between fat mass (FM) and fat-free mass (FFM) has been used to discriminate individual differences in body composition and improve prediction of metabolic risk. Here, we evaluated whether the use of a visceral adipose tissue-to-fat-free mass index (VAT:FFMI) ratio was a better predictor of metabolic risk than a fat mass index to fat-free mass index (FMI:FFMI) ratio. This is a cross-sectional study including 3441 adult participants (age range 18-81; men/women: 977/2464). FM and FFM were measured by bioelectrical impedance analysis and VAT by ultrasonography. A continuous metabolic risk Z score and harmonised international criteria were used to define cumulative metabolic risk and metabolic syndrome (MetS), respectively. Multivariate logistic and linear regression models were used to test associations between body composition indexes and metabolic risk. In unadjusted models, VAT:FFMI was a better predictor of MetS (OR 8.03, 95%CI 6.69-9.65) compared to FMI:FFMI (OR 2.91, 95%CI 2.45-3.46). However, the strength of association of VAT:FFMI and FMI:FFMI became comparable when models were adjusted for age, gender, clinical and sociodemographic factors (OR 4.06, 95%CI 3.31-4.97; OR 4.25, 95%CI 3.42-5.27, respectively). A similar pattern was observed for the association of the two indexes with the metabolic risk Z score (VAT:FFMI: unadjusted b = 0.69 ± 0.03, adjusted b = 0.36 ± 0.03; FMI:FFMI: unadjusted b = 0.28 ± 0.028, adjusted b = 0.38 ± 0.02). Our results suggest that there is no real advantage in using either VAT:FFMI or FMI:FFMI ratios as a predictor of metabolic risk in adults. However, these results warrant confirmation in longitudinal studies.

KEYWORDS:

Metabolic syndrome; obesity; sarcopenia; visceral fat

PMID:
27869360
DOI:
10.1111/cob.12165
[Indexed for MEDLINE]

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