Obesity, metabolic syndrome, impaired fasting glucose, and microvascular dysfunction: a principal component analysis approach
1 Clinical and Experimental Research Laboratory on Vascular Biology (BioVasc),Biomedical Center, State University of Rio de Janeiro, Rio de Janeiro, Brazil
2 Female Endocrinology Sector, Hospital da Lagoa, Health Ministry, Rio deJaneiro, Brazil
3 Division of Endocrinology, Department of Internal Medicine, Medical Sciences Faculty, State University of Rio de Janeiro, Rio de Janeiro, Brazil
Citation and License
BMC Cardiovascular Disorders 2012, 12:102 doi:10.1186/1471-2261-12-102Published: 13 November 2012
We aimed to evaluate the multivariate association between functional microvascular variables and clinical-laboratorial-anthropometrical measurements.
Data from 189 female subjects (34.0±15.5 years, 30.5±7.1 kg/m2), who were non-smokers, non-regular drug users, without a history of diabetes and/or hypertension, were analyzed by principal component analysis (PCA). PCA is a classical multivariate exploratory tool because it highlights common variation between variables allowing inferences about possible biological meaning of associations between them, without pre-establishing cause-effect relationships. In total, 15 variables were used for PCA: body mass index (BMI), waist circumference, systolic and diastolic blood pressure (BP), fasting plasma glucose, levels of total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides (TG), insulin, C-reactive protein (CRP), and functional microvascular variables measured by nailfold videocapillaroscopy. Nailfold videocapillaroscopy was used for direct visualization of nutritive capillaries, assessing functional capillary density, red blood cell velocity (RBCV) at rest and peak after 1 min of arterial occlusion (RBCVmax), and the time taken to reach RBCVmax (TRBCVmax).
A total of 35% of subjects had metabolic syndrome, 77% were overweight/obese, and 9.5% had impaired fasting glucose. PCA was able to recognize that functional microvascular variables and clinical-laboratorial-anthropometrical measurements had a similar variation. The first five principal components explained most of the intrinsic variation of the data. For example, principal component 1 was associated with BMI, waist circumference, systolic BP, diastolic BP, insulin, TG, CRP, and TRBCVmax varying in the same way. Principal component 1 also showed a strong association among HDL-c, RBCV, and RBCVmax, but in the opposite way. Principal component 3 was associated only with microvascular variables in the same way (functional capillary density, RBCV and RBCVmax). Fasting plasma glucose appeared to be related to principal component 4 and did not show any association with microvascular reactivity.
In non-diabetic female subjects, a multivariate scenario of associations between classic clinical variables strictly related to obesity and metabolic syndrome suggests a significant relationship between these diseases and microvascular reactivity.