根据3月的《糖尿病治疗》（Diabetes Care 2007;30:638-643.）杂志上的一项报告，尿蛋白组谱可预测正常蛋白尿2型糖尿病患者的糖尿病性肾病。
美国麻萨诸塞州总医院和哈佛医学院的Ravi Thadhani博士及其同事比较了有2型糖尿病和正常尿白蛋白排泄的62例美国Pima印地安人的尿蛋白组谱，这些患者被随访10年，观察糖尿病性肾病的发生。
作者确定了12峰预测标记，其预测糖尿病性肾病发生的敏感性为93%，特异性为86%，准确性为93%。将标记在验证系列的所有样本（17例样本和17例匹配对照）进行检测时，总体准确性为74%，敏感性为71%，特异性为76%。在调整了基线血红蛋白A1c的多变量尿logistic回归模型中，12峰标记是糖尿病性肾病的独立预测因素，而血红蛋白A1c不再与以后的糖尿病性肾病显著相关。
Thadhani博士说，虽然这些发现需要大量的工作确定和检测这些标志物的稳定性，但是它们为临床医生在1天内确定糖尿病患者是否将发生肾功能衰竭提供了可能，因此，为防止这种疾病发生的早期干预提供了希望。
部分英文原文：
Diabetes Care 30:638-643, 2007
Pathophysiology/Complications
Original Article
Prediction of Diabetic Nephropathy Using Urine Proteomic Profiling 10 Years Prior to Development of Nephropathy
Hasan H. Otu, PHD1,2, Handan Can, PHD1,2, Dimitrios Spentzos, MD1, Robert G. Nelson, MD, PHD3, Robert L. Hanson, MD, MPH3, Helen C. Looker, MBBS3, William C. Knowler, MD, DRPH3, Manuel Monroy, MD4, Towia A. Libermann, PHD1, S. Ananth Karumanchi, MD5 and Ravi Thadhani, MD, MPH4
1 Genomics Center and DF/HCC Cancer Proteomics Core, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
2 Department of Genetics and Bioengineering, Yeditepe University, Istanbul, Turkey
3 Diabetes Epidemiology and Clinical Research Section, Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona
4 Department of Medicine and Renal Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
5 Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
Address correspondence and reprint requests to Ravi Thadhani, MD, MPH, Bullfinch 127, 55 Fruit St., Massachusetts General Hospital, Boston, MA 02114. E-mail: thadhani.r@mgh.harvard.edu
OBJECTIVE—We examined whether proteomic technologies identify novel urine proteins associated with subsequent development of diabetic nephropathy in subjects with type 2 diabetes before evidence of microalbuminuria.
RESEACH DESIGN AND METHODS—In a nested case-control study of Pima Indians with type 2 diabetes, baseline (serum creatinine <1.2 mg/dl and urine albumin excretion <30 mg/g) and 10-year urine samples were examined. Case subjects (n = 31) developed diabetic nephropathy (urinary albumin–to–creatinine ratio >300 mg/g) over 10 years. Control subjects (n = 31) were matched to case subjects (1:1) according to diabetes duration, age, sex, and BMI but remained normoalbuminuric (albumin–to–creatinine ratio <30 mg/g) over the same 10 years. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was performed on baseline urine samples, and training (14 cases:14 controls) and validation (17:17) sets were tested.
RESULTS—At baseline, A1C levels differed between case and control subjects. SELDI-TOF MS detected 714 unique urine protein peaks. Of these, a 12-peak proteomic signature correctly predicted 89% of cases of diabetic nepropathy (93% sensitivity, 86% specificity) in the training set. Applying this same signature to the independent validation set yielded an accuracy rate of 74% (71% sensitivity, 76% specificity). In multivariate analyses, the 12-peak signature was independently associated with subsequent diabetic nephropathy when applied to the validation set (odds ratio [OR] 7.9 [95% CI 1.5–43.5], P = 0.017) and the entire dataset (14.5 [3.7–55.6], P = 0.001), and A1C levels were no longer significant.
CONCLUSIONS—Urine proteomic profiling identifies normoalbuminuric subjects with type 2 diabetes who subsequently develop diabetic nephropathy. Further studies are needed to characterize the specific proteins involved in this early prediction.
Abbreviations: SELDI-TOF MS, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry
