title: Markers of metabolic and cardiovascular health in adults: Comparative analysis of DEXA-based body composition components and BMI categories.
abstract: OBJECTIVE
  To investigate how body composition components fit body mass index (BMI) categories and whether they could be considered as markers of metabolic and cardiovascular health.
  
  
  METHODS
  Prospective study.
  
  
  METHODS
  A center for preventive medicine.
  
  
  METHODS
  Six hundred and sixteen consecutive outpatients: mean age of 56.0±10.0 years; 74.6% aged ≥50 years and 61.4% were females.
  
  
  METHODS
  Fat mass (FM) and muscle mass (MM) were obtained by dual energy X-ray absorptiometry analyses. Metabolically unhealthy individuals were defined as people with biological features of dyslipidemia, hyperuricemia, diabetes, and/or hepatitis steatosis. Documented hypertension and/or atherosclerosis of one major artery, at least, defined individuals with cardiovascular complications.
  
  
  RESULTS
  According to BMI categories, 45.8% of the sample was of normal weight, while 19.2% and 16.5% were classified as overweight and obese. A total of 78.0% and 86.3% of overweight and obese individuals were metabolically unhealthy respectively, 46.8% and 52.6% of subjects classified into normal and underweight BMI categories were also diagnosed. Cardiovascular complications mainly concerned the two highest BMI groups (78.2%). In multifactorial analyses the overweight and obese BMI categories were predictive of health outcomes [respectively, odds ratio (OR)=8.05, 95% confidence interval (CI): 4.23-12.07 and 5.74, 95% CI: 3.41-8.98]. FM and MM indexes were significantly associated with metabolic (OR=1.30, 95% CI: 1.19-1.47; and 0.84, 95% CI: 0.78-0.91) and cardiovascular (OR=1.22, 95% CI: 1.13-1.32; and 0.72, 95% CI: 0.65-0.80) health respectively, and FM/MM (respectively, OR=15.45, 95% CI: 11.77-20.17; and 16.61, 95% CI: 10.49-21.33) as well.
  
  
  CONCLUSIONS
  Our findings suggest that FM and MM readouts are important measurements of nutritional status and they extend the analysis of its impact on health outcomes to all BMI categories. Moreover, they highlight the interest of measuring body composition in medical check-ups to predict metabolic and cardiovascular diseases.
authors.affiliation: Translational Medicine Research Group, Cranfield Health, Cranfield University, Cranfield, UK; Nescens Centre of Preventive Medicine, Clinic of Genolier, Genolier, Switzerland. Electronic address: polang@nescens.com.
authors.forename: Pierre-Olivier
authors.initials: PO
authors.lastname: Lang
authors.affiliation: Pôle Gériatrie, Hôpitaux Universitaires de Paris-Sud, Hôpital Paul Brousse, Assistance-Publique Hôpitaux de Paris, Villejuif, France.
authors.forename: Christophe
authors.initials: C
authors.lastname: Trivalle
authors.affiliation: Pôle de Gériatrie, Hôpitaux Universitaires de Strasbourg, Strasbourg, France.
authors.forename: Thomas
authors.initials: T
authors.lastname: Vogel
authors.affiliation: Nescens Centre of Preventive Medicine, Clinic of Genolier, Genolier, Switzerland.
authors.forename: Jacques
authors.initials: J
authors.lastname: Proust
authors.affiliation: Centre of Nuclear Medicine, Clinic of Genolier, Genolier, Switzerland.
authors.forename: Jean-Pierre
authors.initials: JP
authors.lastname: Papazian
mesh_terms: D000368:Aged; D015415:Biomarkers; D001823:Body Composition; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000175:diagnosis*; D005544:Forecasting; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D024821:Metabolic Syndrome / Q000175:diagnosis*; D008875:Middle Aged; D009752:Nutritional Status; D050177:Overweight
chemical_list: D015415:Biomarkers
keywords: Body composition; Cardiovascular diseases; Fat-free mass; Fat-mass; Metabolic health