Department of Nephrology, The First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China.
Department of Nephrology, The First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China.
Diabetes Metab Syndr. 2024 Feb;18(2):102963. doi: 10.1016/j.dsx.2024.102963. Epub 2024 Feb 12.
Accumulating data demonstrated that the cortico-medullary difference in apparent diffusion coefficient (ΔADC) of diffusion-weighted magnetic resonance imaging (DWI) was a better correlation with kidney fibrosis, tubular atrophy progression, and a predictor of kidney function evolution in chronic kidney disease (CKD).
We aimed to assess the value of ΔADC in evaluating disease severity, differential diagnosis, and the prognostic risk stratification for patients with type 2 diabetes (T2D) and CKD.
Total 119 patients with T2D and CKD who underwent renal MRI were prospectively enrolled. Of them, 89 patients had performed kidney biopsy for pathological examination, including 38 patients with biopsy-proven diabetic kidney disease (DKD) and 51 patients with biopsy-proven non-diabetic kidney disease (NDKD) and Mix (DKD + NDKD). Clinicopathological characteristics were compared according to different ΔADC levels. Moreover, univariate and multivariate-linear regression analyses were performed to explore whether ΔADC was independently associated with estimated glomerular filtration rate (eGFR) and urinary albumin creatinine ratio (UACR). The diagnostic performance of ΔADC for discriminating DKD from NDKD + Mix was evaluated by receiver operating characteristic (ROC) analysis. In addition, an individual's 2- or 5-year risk probability of progressing to end-stage kidney disease (ESKD) was calculated by the kidney failure risk equation (KFRE). The effect of ΔADC on prognostic risk stratification was assessed. Additionally, net reclassification improvement (NRI) was used to evaluate the model performance.
All enrolled patients had a median ΔADC level of 86 (IQR 28, 155) × 10 mm/s. ΔADC significantly decreased across the increasing staging of CKD (P < 0.001). Moreover, those with pathological-confirmed DKD has a significantly lower level of ΔADC than those with NDKD and Mix (P < 0.001). It showed that ΔADC was independently associated with eGFR (β = 1.058, 95% CI = [1.002,1.118], P = 0.042) and UACR (β = -3.862, 95% CI = [-7.360, -0.365], P = 0.031) at multivariate linear regression analyses. Besides, ΔADC achieved an AUC of 0.707 (71% sensitivity and 75% specificity) and AUC of 0.823 (94% sensitivity and 67% specificity) for discriminating DKD from NDKD + Mix and higher ESKD risk categories (≥50% at 5 years; ≥10% at 2 years) from lower risk categories (<50% at 5 years; <10% at 2 years). Accordingly, the optimal cutoff value of ΔADC for higher ESKD risk categories was 66 × 10 mm/s, and the group with the low-cutoff level of ΔADC group was associated with 1.232 -fold (95% CI 1.086, 1.398) likelihood of higher ESKD risk categories as compared to the high-cutoff level of ΔADC group in the fully-adjusted model. Reclassification analyses confirmed that the final adjusted model improved NRI.
ΔADC was strongly associated with eGFR and UACR in patients with T2D and CKD. More importantly, baseline ΔADC was predictive of higher ESKD risk, independently of significant clinical confounding. Specifically, ΔADC <78 × 10 mm/s and <66 × 10 mm/s would help to identify T2D patients with the diagnosis of DKD and higher ESKD risk categories, respectively.
越来越多的证据表明，弥散加权磁共振成像（DWI）表观扩散系数（ADC）皮质-髓质差异与肾纤维化、肾小管萎缩进展及慢性肾脏病（CKD）患者肾功能演变的相关性更好。
本研究旨在评估ΔADC 在评估 2 型糖尿病（T2D）和 CKD 患者疾病严重程度、鉴别诊断和预后风险分层方面的价值。
前瞻性纳入 119 例 T2D 和 CKD 患者进行肾脏 MRI 检查。其中 89 例患者行肾脏活检进行病理检查，包括 38 例活检证实的糖尿病肾病（DKD）患者、51 例活检证实的非糖尿病肾病（NDKD）患者和 10 例 DKD 和 NDKD 混合患者。根据不同的 ΔADC 水平比较临床病理特征。此外，进行单变量和多变量线性回归分析，以探讨 ΔADC 是否与估计肾小球滤过率（eGFR）和尿白蛋白肌酐比（UACR）独立相关。通过受试者工作特征（ROC）曲线分析评估 ΔADC 鉴别 DKD 与 NDKD+Mix 的诊断性能。此外，通过肾衰竭风险方程（KFRE）计算个体 2 年或 5 年进展为终末期肾脏病（ESKD）的风险概率。评估 ΔADC 对预后风险分层的影响。此外，使用净重新分类改善（NRI）来评估模型性能。
所有入组患者的 ΔADC 中位数为 86（IQR 28，155）×10mm/s。随着 CKD 分期的增加，ΔADC 显著降低（P<0.001）。此外，与 NDKD 和 Mix 相比，病理证实的 DKD 患者的 ΔADC 水平显著降低（P<0.001）。多变量线性回归分析显示，ΔADC 与 eGFR（β=1.058，95%CI=[1.002，1.118]，P=0.042）和 UACR（β=-3.862，95%CI=[-7.360，-0.365]，P=0.031）独立相关。此外，ΔADC 对 DKD 与 NDKD+Mix 的鉴别诊断的 AUC 为 0.707（71%的敏感性和 75%的特异性），对鉴别更高 ESKD 风险类别（5 年时≥50%；2 年时≥10%）和更低风险类别的 AUC 为 0.823（94%的敏感性和 67%的特异性）。因此，更高 ESKD 风险类别的 ΔADC 的最佳截断值为 66×10mm/s，低截断值的 ΔADC 组与高截断值的 ΔADC 组相比，在完全调整后的模型中，发生更高 ESKD 风险类别的可能性高 1.232 倍（95%CI 1.086，1.398）。再分类分析证实，最终调整后的模型提高了 NRI。
ΔADC 与 T2D 和 CKD 患者的 eGFR 和 UACR 密切相关。更重要的是，基线 ΔADC 可独立预测更高的 ESKD 风险。具体来说，ΔADC<78×10mm/s 和 <66×10mm/s 有助于分别识别出患有 DKD 和更高 ESKD 风险类别的 T2D 患者。
