Predicting visceral adipose tissue in older adults: A pilot clinical study. Issue 4 (April 2022)

• Record Type: Journal Article
• Authors: Perna, Simone; Faragli, Alessandro; Spadaccini, Daniele; Peroni, Gabriella; Gasparri, Clara; Al-Mannai, Mariam Ahmed; Casali, Pietro Mariano; La Porta, Edoardo; Kelle, Sebastian; Alogna, Alessio; Rondanelli, Mariangela
• Is Part Of: Clinical nutrition. Volume 41:Issue 4(2022)
• Page Start: 810
• Page End: 816
• Publication Date: 2022-04
• Subjects: BIA -- DXA -- Obesity -- Adipose tissue -- Cardiometabolic disease; Critically ill -- Nutrition -- Periodicals; Diet therapy -- Periodicals; Parenteral feeding -- Periodicals; Enteral feeding -- Periodicals; Enteral Nutrition -- Periodicals; Parenteral Nutrition -- Periodicals; Metabolism -- Periodicals; Diétothérapie -- Périodiques; Alimentation parentérale -- Périodiques; Alimentation entérale -- Périodiques; Nutrition -- Périodiques; Diet therapy; Enteral feeding; Nutrition; Parenteral feeding; Electronic journals; Periodicals; Electronic journals; 615.854
• DOI: 10.1016/j.clnu.2022.02.008
• Languages: English
• ISSNs: 0261-5614

Abstract

Summary: Background & aims: Visceral adipose tissue (VAT) is a recognized risk factor for cardiometabolic disease, and dual energy X-ray absorptiometry (DXA) has been recently validated for the quantification of VAT. This study aims to explore VAT prediction by utilizing bioimpedance analysis (BIA), anthropometric measures and biochemical markers. Methods: Data from BIA, anthropometric measures, biochemical markers and DXA scans were collected in 1064 older adults participants (761 F, 303 M) with a mean age of 82 ± 6 years old. DXA-VAT was quantified at the android region (DXA-VAT – volume cm 3 ) using the enCore software. Multifactorial linear regression analysis was used to establish the proposed predicting equations and define the values more associated with VAT. Results: In our multivariate model, the main VAT predictable markers were in both genders, weight (kg), Triglycerides (mmol/L) and height (m). These models (stratified for gender) included the BIA outcomes as regressor factors. The VAT calculation equation formula was VAT = 148.89 + (weight (kg)∗33.81) + (Trg (mmol/L)∗1.41) + (height (m)∗−8.99) for females and VAT = 1481.22 + (weight (kg)∗43.94) + (Trg (mmol/L)∗−21.27) + (height (m)∗3.57) for males. In both equations, the r 2 was 0.76. The Network analysis showed a strong link network between weight and resistance (Rz). Conclusions: The independent and combined use of anthropometric measures and biochemical markers could accurately predict VAT in the older adults' population. Because of the strong link between Rz and weight, BIA might be included in future equations predicting VAT but different data pools and populations are needed.