The Lipid Accumulation Product and All-cause Mortality in Patients at High Cardiovascular Risk: A PreCIS Database Study
The Lipid Accumulation Product and All-cause Mortality in Patients at High Cardiovascular Risk: A PreCIS Database Study
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DOI：
10.1038/oby.2009.453
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发表时间：
2010-09-01
期刊：
影响因子：
6.9
通讯作者：
Hoogwerf, Byron J.
中科院分区：
文献类型：
--
作者：
Ioachimescu, Adriana G.;Brennan, Danielle M.;Hoogwerf, Byron J.
BMI是评估肥胖相关风险最常用的指标。根据腰围(WC，cm)和空腹甘油三酯(Tgs)(摩尔/L)计算出的脂类累积产物(LAP)：(WC-65)×TG(男性)和(WC-58)×TG(女性)。在高危人群中，我们评估LAP和BMI作为死亡率的预测因子。研究人群包括1995至2006年间在一家预防心脏病诊所就诊的5924名连续新患者。58%的患者对他们的大腿和BMI四分位数不一致。腰围四分位数大于体重指数四分位数的患者与腰围四分位数低于体重指数四分位数的患者相比，死亡率更高(6年后8.2%对5.4%，P=0.007)。在调整了年龄、性别、吸烟、糖尿病、血压、低密度脂蛋白胆固醇和高密度脂蛋白胆固醇后，(In)LAP与死亡率独立相关(危险比=1.46P<；0.001)。体重指数与死亡率的增加无关(HR=1.06，P=0.39)。在包含动脉粥样硬化传统危险因素的模型中加入LAP可增加其预测值(C统计量为0.762比0.750，P=0.048)。将体重指数添加到同一模型中不会改变其预测值(0.749比0.750，P=0.29)。亚组分析显示，LAP预测非糖尿病患者的死亡率(调整后的HR为LAP1.64，P<；0.001)，但在糖尿病患者中未达到显著水平(HR=1.21，P=0.11)。总而言之，LAP和非BMI可以预测心血管疾病高危非糖尿病患者的死亡率。LAP可能会在临床实践中成为一种有用的工具，用于对与肥胖相关的不良结局的风险进行分层。
The BMI is the most frequently used marker to evaluate obesity-associated risks. An alternative continuous index of lipid over accumulation, the lipid accumulation product (LAP), has been proposed, which is computed from waist circumference (WC, cm) and fasting triglycerides (TGs) (mmol/l): (WC - 65) x TG (men) and (WC - 58) x TG (women). We evaluated LAP and BMI as predictors of mortality in a high-risk cohort. Study population included 5,924 new consecutive patients seen between 1995 and 2006 at a preventive cardiology clinic. Fifty-eight percent of patients were discordant for their LAP and BMI quartiles. Patients whose LAP quartile was greater than BMI quartile had higher mortality compared with those with LAP quartile was lower than BMI quartile (8.2 vs. 5.4% at 6 years, P = 0.007). After adjustment for age, gender, smoking, diabetes mellitus, blood pressure, low-density lipoprotein-cholesterol (LDL-C) and high-density lipoprotein-cholesterol (HDL-C), (In) LAP was independently associated with mortality (hazard ratio (HR) = 1.46, P < 0.001). BMI was not associated with increased mortality (HR = 1.06, P = 0.39). Adding LAP to a model including traditional risk factors for atherosclerosis increased its predictive value (C statistic 0.762 vs. 0.750, P = 0.048). Adding BMI to the same model did not change its predictive value (0.749 vs. 0.750, P = 0.29). Subgroup analyses showed that LAP predicted mortality in the nondiabetic patients (adjusted HR for (In) LAP 1.64, P < 0.001), but did not reach significance in the diabetic patients (HR = 1.21, P = 0.11). In conclusion, LAP and not BMI predicted mortality in nondiabetic patients at high risk for cardiovascular diseases. LAP may become a useful tool in clinical practice to stratify the risk of unfavorable outcome associated with obesity.