Simone Perna1, Alessandro Faragli2, Daniele Spadaccini3, Gabriella Peroni4, Clara Gasparri4, Mariam Ahmed Al-Mannai5, Pietro Mariano Casali6, Edoardo La Porta7, Sebastian Kelle8, Alessio Alogna9, Mariangela Rondanelli10. 1. Department of Biology, College of Science, University of Bahrain, Sakhir Campus, Kingdom of Bahrain. 2. Department of Internal Medicine/Cardiology, Deutsches Herzzentrum Berlin, Berlin, Germany; Charité - Universitätsmedizin Berlin, Department of Internal Medicine and Cardiology, Campus Virchow-Klinikum, 13353 Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Germany; Berlin Institute of Health (BIH), Berlin, Germany. 3. Department of Health Sciences, University of Piemonte Orientale, 28100 Novara, Italy. Electronic address: daniele.spadaccini@uniupo.it. 4. Endocrinology and Nutrition Unit, Azienda di Servizi alla Persona "Istituto Santa Margherita", University of Pavia, Pavia, Italy. 5. Department of Mathematics, College of Science, University of Bahrain, Sakhir Campus, Kingdom of Bahrain. 6. Department of Public Health, Experimental and Forensic Medicine, University of Pavia, Pavia, Italy. 7. Department of Cardionephrology, Istituto Clinico Ligure Di Alta Specialità (ICLAS), GVM Care and Research, Rapallo, Italy; Department of Internal Medicine (DiMi), University of Genova, Genova, Italy. 8. Department of Internal Medicine/Cardiology, Deutsches Herzzentrum Berlin, Berlin, Germany; Charité - Universitätsmedizin Berlin, Department of Internal Medicine and Cardiology, Campus Virchow-Klinikum, 13353 Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Germany. 9. Charité - Universitätsmedizin Berlin, Department of Internal Medicine and Cardiology, Campus Virchow-Klinikum, 13353 Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Germany; Berlin Institute of Health (BIH), Berlin, Germany. Electronic address: alessio.alogna@charite.de. 10. Department of Public Health, Experimental and Forensic Medicine, University of Pavia, Pavia, Italy; IRCCS Mondino Foundation, Pavia, Italy.
Abstract
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 cm3) 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 r2 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.
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 cm3) 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 r2 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.