Visceral Adiposity and the Risk of Metabolic Syndrome Across Body Mass Index

Abstract

OBJECTIVES This study sought to evaluate differential effects of visceral fat (VF) and subcutaneous fat and their effects on metabolic syndrome (MetS) risk across body mass index (BMI) categories. BACKGROUND The regional distribution of adipose tissue is an emerging risk factor for cardiometabolic disease, although serial changes in fat distribution have not been extensively investigated. VF and its alterations over time may be a better marker for risk than BMI in normal weight and overweight or obese individuals. METHODS We studied 1,511 individuals in the MESA (Multi-Ethnic Study of Atherosclerosis) with adiposity assessment by computed tomography (CT). A total of 253 participants without MetS at initial scan underwent repeat CT (median interval 3.3 years). We used discrete Cox regression with net reclassification to investigate whether baseline and changes in VF area are associated with MetS. RESULTS Higher VF was associated with cardiometabolic risk and coronary artery calcification, regardless of BMI. After adjustment, VF was more strongly associated with incident MetS than subcutaneous fat regardless of weight, with a 28% greater MetS hazard per 100 cm2/m VF area and significant net reclassification (net reclassification index: 0.44, 95% confidence interval [CI]: 0.29 to 0.60) over clinical risk. In individuals with serial imaging, initial VF (hazard ratio: 1.24 per 100 cm2/m, 95% CI: 1.08 to 1.44 per 100 cm2/m, p = 0.003) and change in VF (hazard ratio: 1.05 per 5% change, 95% CI: 1.01 to 1.08 per 5% change, p = 0.02) were associated with MetS after adjustment. Changes in subcutaneous fat were not associated with incident MetS after adjustment for clinical risk and VF area. CONCLUSIONS VF is modestly associated with BMI. However, across BMI, a single measure of and longitudinal change in VF predict MetS, even accounting for weight changes. Visceral adiposity is essential to assessing cardiometabolic risk, regardless of age, race, or BMI, and may serve as a marker and target of therapy in cardiometabolic disease.

Body Mass Index; Visceral Fat and Total Body Fat Distribution and Its Relation to Body Mass Index in Clinical Setting Using Bio-Impedance Body Composition Monitor

“…19 However it is a modifiable risk factor. Many studies have shown that abdominal visceral fat plays a key role in insulin resistance.…”

“…19,22,23 Thus accurate measurement of visceral fat area(VFA) is crucial in the assessment of cardio metabolic risk in an individual. 24 MRI is considered the most accurate method for assessment of VFA ,without exposure to radiation, however it is very expensive.…”

Obesity phenotypes: normal-weight individuals with metabolic disorders versus metabolically healthy obese

“…The interesting finding is that there are significant differences in the metabolic profiles between people assigned to the same categories of BMI. It shows that the major problems are composition and distribution of body fat instead of the amount of adipose tissue [5,[10][11][12]. Therefore, it became essential to distinguish the particular type of accumulated fat mass.…”

Cut-off Point of Waist-circumference for Metabolic Syndrome Components among Turkman and Non-Turkman Ethnic Adults in the North of Iran

“…In the recent years, changing of lifestyle and feeding behaviours extensively increases the cardiovascular disease, diabetes mellitus, and other MetS indices in worldwide, especially in developing countries (Alwan, 2010 (Shah , 2014). In some studies, WC (Waist Circumference) has been reported as a better predictor of CVD risk factor (Ali , 2014), whereas others had recommended the WHR (Waist-hip ratio) and WHtR (WC-to-height ratio) indices (Ko ., 1999).…”

“…In the other hand, WC is well known as a better predictor of CVD than BMI (Body Mass Index) and WHR (Lin , 2002). Visceral adiposity is also helpful in assessing cardiovascular risk, not considering of age, race, or BMI, and can serve as an indicator and target of therapy in cardiometabolic disease (Shah ., 2014). Area under the ROC (receiveroperating characteristic curve) is useful in evaluating the discriminatory ability of a test to properly select positive and negative disease subjects; detection optimal cut-off point to the minimum amount misclassify diseased or non-diseased subjects; comparing the eff icacy of two or more tests for assessing the same disease and comparing two or more observers measuring the same test (Kumar & Indrayan, 2011).…”