OBJECTIVE To assess the ability of plasma apolipoprotein (apo) A-IV (apoA4), apo C-III, CD5 antigen-like (CD5L), complement C1q subcomponent subunit B (C1QB), complement factor H–related protein 2, and insulin-like growth factor binding protein 3 (IBP3) to predict rapid decline in estimated glomerular filtration rate (eGFR) in type 2 diabetes.

RESEARCH DESIGN AND METHODS Mass spectrometry was used to measure baseline biomarkers in 345 community-based patients (mean age 67.0 years, 51.9% males) from the Fremantle Diabetes Study Phase II. Multiple logistic regression was used to determine clinical predictors of rapid eGFR decline trajectory defined by semiparametric group-based modeling over a 4-year follow-up period. The incremental benefit of each biomarker was then assessed. Similar analyses were performed for a ≥30% eGFR fall, incident chronic kidney disease (eGFR <60 mL/min/1.73 m²), and eGFR decline of ≥5 mL/min/1.73 m²/year.

RESULTS Based on eGFR trajectory analysis, 35 participants (10.1%) were defined as “rapid decliners” (mean decrease 2.9 mL/min/1.73 m²/year). After adjustment for clinical predictors, apoA4, CD5L, and C1QB independently predicted rapid decline (odds ratio 2.40 [95% CI 1.24–4.61], 0.52 [0.29–0.93], and 2.41 [1.14–5.11], respectively) and improved model performance and fit (P < 0.001), discrimination (area under the curve 0.75–0.82, P = 0.039), and reclassification (net reclassification index 0.76 [0.63–0.89]; integrated discrimination improvement 6.3% [2.1–10.4%]). These biomarkers and IBP3 contributed to improved model performance in predicting other indices of rapid eGFR decline.

CONCLUSIONS The current study has identified novel plasma biomarkers (apoA4, CD5L, C1QB, and IBP3) that may improve the prediction of rapid decline in renal function independently of recognized clinical risk factors in type 2 diabetes.

目的评估血浆载脂蛋白（apo）A-IV（apoA4），apo C-III，CD5抗原样（CD5L），补体C1q亚成分B亚基（C1QB），补体因子H相关蛋白2和胰岛素的能力样生长因子结合蛋白3（IBP3）预测2型糖尿病的估计肾小球滤过率（eGFR）迅速下降。

研究设计和方法质谱用于测量Fremantle糖尿病研究第二阶段的345位以社区为基础的患者（平均年龄67.0岁，男性51.9％）的基线生物标志物。多元逻辑回归用于确定eGFR迅速下降轨迹的临床预测指标，该预测指标是在4年的随访期内通过基于半参数组的模型定义的。然后评估每种生物标志物的增量益处。对eGFR下降≥30％，慢性肾脏疾病（eGFR <60 mL / min / 1.73 m ²）和eGFR下降≥5mL / min / 1.73 m ² /年进行了类似的分析。

结果根据eGFR轨迹分析，将35名参与者（10.1％）定义为“快速下降者”（平均下降2.9 mL / min / 1.73 m ² /年）。调整临床预测指标后，apoA4，CD5L和C1QB独立预测快速下降（赔率分别为2.40 [95％CI 1.24-4.61]，0.52 [0.29-0.93]和2.41 [1.14-5.11]）并改善了模型性能拟合度（P <0.001），辨别力（曲线下面积0.75–0.82，P = 0.039）和重分类（净重分类指数0.76 [0.63-0.89]；综合辨别力改善6.3％[2.1-10.4％]）。这些生物标志物和IBP3在预测eGFR迅速下降的其他指标方面有助于改善模型性能。

结论当前的研究已经确定了新颖的血浆生物标志物（apoA4，CD5L，C1QB和IBP3），这些标志物可以改善肾功能快速下降的预测，而与2型糖尿病公认的临床危险因素无关。