Predicting total, abdominal, visceral and hepatic adiposity with circulating biomarkers in caucasian and Japanese american women

Background

Characterization of abdominal and intra-abdominal fat requires imaging, and thus is not feasible in large epidemiologic studies.

Objective

We investigated whether biomarkers may complement anthropometry (body mass index BMI], waist circumference WC], and waist-hip ratio WHR]) in predicting the size of the body fat compartments by analyzing blood biomarkers, including adipocytokines, insulin resistance markers, sex steroid hormones, lipids, liver enzymes and gastro-neuropeptides.

Methods

Fasting levels of 58 blood markers were analyzed in 60 healthy, Caucasian or Japanese American postmenopausal women who underwent anthropometric measurements, dual energy X-ray absorptiometry (DXA), and abdominal magnetic resonance imaging. Total, abdominal, visceral and hepatic adiposity were predicted based on anthropometry and the biomarkers using Random Forest models.

Results

Total body fat was well predicted by anthropometry alone (R²?=?0.85), by the 5 best predictors from the biomarker model alone (leptin, leptin-adiponectin ratio LAR], free estradiol, plasminogen activator inhibitor-1 PAI1], alanine transaminase ALT]; R²?=?0.69), or by combining these 5 biomarkers with anthropometry (R²?=?0.91). Abdominal adiposity (DXA trunk-to-periphery fat ratio) was better predicted by combining the two types of predictors (R²?=?0.58) than by anthropometry alone (R²?=?0.53) or the 5 best biomarkers alone (25(OH)-vitamin D₃, insulin-like growth factor binding protein-1 IGFBP1], uric acid, soluble leptin receptor sLEPR], Coenzyme Q10; R²?=?0.35). Similarly, visceral fat was slightly better predicted by combining the predictors (R²?=?0.68) than by anthropometry alone (R²?=?0.65) or the 5 best biomarker predictors alone (leptin, C-reactive protein CRP], LAR, lycopene, vitamin D₃; R²?=?0.58). Percent liver fat was predicted better by the 5 best biomarker predictors (insulin, sex hormone binding globulin SHBG], LAR, alpha-tocopherol, PAI1; R²?=?0.42) or by combining the predictors (R²?=?0.44) than by anthropometry alone (R²?=?0.29).

Conclusion

The predictive ability of anthropometry for body fat distribution may be enhanced by measuring a small number of biomarkers. Studies to replicate these data in men and other ethnic groups are warranted.