用循环生物标志物预测白种人和日裔美国女性的全身、腹部、内脏和肝脂肪量。
Predicting total, abdominal, visceral and hepatic adiposity with circulating biomarkers in Caucasian and Japanese American women.
机构信息
Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America.
出版信息
PLoS One. 2012;7(8):e43502. doi: 10.1371/journal.pone.0043502. Epub 2012 Aug 17.
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 (R(2) = 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]; R(2) = 0.69), or by combining these 5 biomarkers with anthropometry (R(2) = 0.91). Abdominal adiposity (DXA trunk-to-periphery fat ratio) was better predicted by combining the two types of predictors (R(2) = 0.58) than by anthropometry alone (R(2) = 0.53) or the 5 best biomarkers alone (25(OH)-vitamin D(3), insulin-like growth factor binding protein-1 [IGFBP1], uric acid, soluble leptin receptor [sLEPR], Coenzyme Q10; R(2) = 0.35). Similarly, visceral fat was slightly better predicted by combining the predictors (R(2) = 0.68) than by anthropometry alone (R(2) = 0.65) or the 5 best biomarker predictors alone (leptin, C-reactive protein [CRP], LAR, lycopene, vitamin D(3); R(2) = 0.58). Percent liver fat was predicted better by the 5 best biomarker predictors (insulin, sex hormone binding globulin [SHBG], LAR, alpha-tocopherol, PAI1; R(2) = 0.42) or by combining the predictors (R(2) = 0.44) than by anthropometry alone (R(2) = 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.
背景
腹部和腹腔内脂肪的特征需要通过影像学进行评估，因此在大型流行病学研究中并不可行。
目的
我们通过分析包括脂肪细胞因子、胰岛素抵抗标志物、性激素、脂质、肝酶和胃肠肽在内的血液生物标志物，研究生物标志物是否可以补充人体测量学（体重指数[BMI]、腰围[WC]和腰臀比[WHR]），从而预测体脂肪的大小。
方法
对 60 名健康的白种或日裔美国绝经后妇女进行人体测量学测量、双能 X 射线吸收法（DXA）和腹部磁共振成像，分析 58 种血液标志物的空腹水平。使用随机森林模型，根据人体测量学和生物标志物预测总、腹部、内脏和肝脂肪。
结果
仅人体测量学就能很好地预测总体体脂（R² = 0.85），仅 5 种最佳生物标志物模型预测因子（瘦素、瘦素-脂联素比[LAR]、游离雌二醇、纤溶酶原激活物抑制剂-1[PAI1]、丙氨酸氨基转移酶[ALT]；R² = 0.69），或结合这 5 种生物标志物与人体测量学（R² = 0.91）也能很好地预测总体体脂。仅结合这两种预测因子（R² = 0.58）就能更好地预测腹部脂肪（DXA 躯干-外周脂肪比），而仅人体测量学（R² = 0.53）或 5 种最佳生物标志物单独预测（25-羟维生素 D3（25(OH)-vitamin D3）、胰岛素样生长因子结合蛋白-1[IGFBP1]、尿酸、可溶性瘦素受体[sLEPR]、辅酶 Q10；R² = 0.35）则效果不佳。同样，仅结合预测因子（R² = 0.68）也能更好地预测内脏脂肪，而仅人体测量学（R² = 0.65）或 5 种最佳生物标志物预测因子单独预测（瘦素、C 反应蛋白[CRP]、LAR、番茄红素、维生素 D3；R² = 0.58）效果不佳。仅 5 种最佳生物标志物预测因子（胰岛素、性激素结合球蛋白[SHBG]、LAR、α-生育酚、PAI1；R² = 0.42）或结合预测因子（R² = 0.44）对肝脂肪百分比的预测效果优于仅人体测量学（R² = 0.29）。
结论
人体测量学对体脂分布的预测能力可以通过测量少量生物标志物来增强。有必要在男性和其他种族群体中复制这些数据。
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