Department of Radiology, Auckland City Hospital, Auckland, New Zealand; Department of Radiology, Hospital Nueve de Octubre, Valencia, Spain.
Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
Nutrition. 2019 May;61:143-150. doi: 10.1016/j.nut.2018.10.023. Epub 2018 Oct 24.
The aim of this study was to review the existence and types of correlations between body composition densitometric parameters and laboratory values associated to cardiometabolic risk.
We retrospectively analyzed data from 316 individuals in the weight range from normality to super-obesity, submitted to total body dual-energy x-ray absorptiometry (DXA) scans and routine biochemistry at S.Orsola-Malpighi Hospital from June 2010 to March 2014. The study included 182 women, 45.8 ± 13.4 y of age, with a body mass index (BMI) of 31.5 (± 11) kg/m (group F) and 134 men, 45.4 ± 13.6 y of age, with a BMI of 27.6 (± 7.8) kg/m (group M). All patients underwent whole-body scan (Lunar iDXA, GE Healthcare, Madison, WI, USA) and laboratory analysis (blood fasting glucose, total cholesterol, high-density lipoprotein cholesterol, tricylglycerides [TGs], aspartate aminotransferase, and alanine aminotransferase). Correlation between laboratory values and total body and regional fat mass (including visceral adipose tissue [VAT] and subcutaneous adipose tissue in the android region), and lean mass parameters were analyzed with linear and stepwise regressions analysis (significance limit, P < 0.05). Receiver operating characteristic curves were performed to assess the accuracy of the best-fit DXA parameter (VAT) to identify at least one laboratory risk factor.
In both groups, BMI and densitometric parameters showed a linear correlation with fasting blood glucose and TG levels and an inverse correlation with high-density lipoprotein cholesterol (P < 0.05), whereas no correlation was observed with total cholesterol levels. The only densitometric parameter retained in the final model of stepwise multiple regression was VAT for fasting blood glucose (group F: β = 0.4627, P < 0.0001; group M: β = 0.6221, P < 0.0001) and TG levels (group F: β = 0.4931, P < 0.0001; group M: β = 0.1990, P < 0.0261) independently of BMI. The optimal cutoff points of VAT to identify the presence of at least one laboratory risk factor were >1395 g and >1479 cm for men and >1281 g and >1357 cm for women.
DXA analysis of VAT is associated with selected laboratory parameters used for the evaluation of cardiometabolic risk and could be per se a helpful parameter in the assessment of clinical risk.
本研究旨在回顾体成分密度参数与心血管代谢风险相关的实验室值之间存在的和类型的相关性。
我们回顾性分析了 2010 年 6 月至 2014 年 3 月期间在圣奥尔索拉-马尔皮吉医院接受全身双能 X 射线吸收法（DXA）扫描和常规生化检查的 316 名体重正常至超肥胖个体的数据。该研究包括 182 名女性，年龄 45.8±13.4 岁，体重指数（BMI）为 31.5（±11）kg/m2（F 组）和 134 名男性，年龄 45.4±13.6 岁，BMI 为 27.6（±7.8）kg/m2（M 组）。所有患者均行全身扫描（Lunar iDXA，GE Healthcare，Madison，WI，USA）和实验室分析（空腹血糖、总胆固醇、高密度脂蛋白胆固醇、三酰甘油[TGs]、天门冬氨酸氨基转移酶和丙氨酸氨基转移酶）。用线性和逐步回归分析（显著性限，P<0.05）分析实验室值与全身和局部脂肪量（包括内脏脂肪组织[VAT]和男性安卓区的皮下脂肪组织）和瘦体重参数之间的相关性。进行受试者工作特征曲线以评估最佳拟合 DXA 参数（VAT）识别至少一个实验室危险因素的准确性。
在两组中，BMI 和密度参数与空腹血糖和 TG 水平呈线性相关，与高密度脂蛋白胆固醇呈负相关（P<0.05），而与总胆固醇水平无相关性。逐步多元回归的最终模型中唯一保留的密度参数是 VAT 与空腹血糖（F 组：β=0.4627，P<0.0001；M 组：β=0.6221，P<0.0001）和 TG 水平（F 组：β=0.4931，P<0.0001；M 组：β=0.1990，P<0.0261）相关，独立于 BMI。男性 VAT 的最佳截断点为>1395g 和>1479cm，女性为>1281g 和>1357cm，用于识别至少一个实验室危险因素的存在。
DXA 分析 VAT 与用于评估心血管代谢风险的选定实验室参数相关，并且本身可能是评估临床风险的有用参数。
