González-Palomo Ana K, Pérez-Vázquez Francisco J, Méndez-Rodríguez Karen B, Ilizaliturri-Hernández Cesar A, Cardona-Alvarado Monica I, Flores-Nicasio Mariana V, Kornhauser Carlos, Malacara Juan M, Figueroa-Vega Nicte
Departamento de Ciencias Médicas, Universidad de Guanajuato, León, Mexico.
Coordinación para la Innovación y Aplicación de la Ciencia y Tecnología (CIACyT), Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico.
Nephrology (Carlton). 2022 Jun;27(6):484-493. doi: 10.1111/nep.14039. Epub 2022 Mar 25.
Evaluate the expression of exomiRs-126, -146, and -155 in urinary exosomes of patients with T2DM and diabetic kidney disease to establish a predictive classification model with exomiRs and clinical variables in order to determine their contribution to DKD.
The study group included 92 subjects: 64 patients diagnosed with T2DM subclassified into two groups with albuminuria (T2DM with albuminuria, n = 30) and without albuminuria (TD2M, n = 34) as well as 28 healthy, non-diabetic participants. Exosomes were isolated from urine and identified by TEM and flow cytometry. Profile expression of exomiRs-126, -146 and -155 was evaluated by RT-qPCR. Data were analysed by permutational multivariate analysis of variance (PERMANOVA), similarity percentage (SIMPER), principal coordinate analysis (PCO), and canonical analysis of principal coordinates (CAP).
T2DM patients with and without albuminuria showed higher levels of miR-155 and miR-146 compared with controls. In addition, T2DM patients with albuminuria presented a significant increase in miR-126 contrasted to controls and patients without albuminuria. PCO analysis explained 34.6% of the total variability of the data (PERMANOVA; p < .0001). Subsequently, SIMPER analysis showed that miR-146, miR-155, and miR-126 together, with some clinical parameters, contributed to 50% of the between-group significance. Finally, the CAP analysis developed showed a correct classification of 89.01% with the analysed parameters.
A platform using a combination of clinical variables and exomiRs could be used to classify individuals with T2D as risk for developing DKD.
评估2型糖尿病（T2DM）和糖尿病肾病（DKD）患者尿外泌体中exomiR-126、-146和-155的表达，建立一个包含exomiR和临床变量的预测分类模型，以确定它们对DKD的影响。
研究组包括92名受试者：64例诊断为T2DM的患者，根据是否有蛋白尿分为两组，有蛋白尿的T2DM患者（T2DM伴蛋白尿，n = 30）和无蛋白尿的患者（TD2M，n = 34），以及28名健康的非糖尿病参与者。从尿液中分离出外泌体，并通过透射电子显微镜（TEM）和流式细胞术进行鉴定。通过逆转录定量聚合酶链反应（RT-qPCR）评估exomiR-126、-146和-155的表达谱。数据采用置换多变量方差分析（PERMANOVA）、相似性百分比分析（SIMPER）、主坐标分析（PCO）和主坐标典型分析（CAP）进行分析。
与对照组相比，有蛋白尿和无蛋白尿的T2DM患者miR-155和miR-146水平更高。此外，与对照组和无蛋白尿的患者相比，有蛋白尿的T2DM患者miR-126显著增加。PCO分析解释了数据总变异性的34.6%（PERMANOVA；p <.0001）。随后，SIMPER分析表明，miR-146、miR-155和miR-126以及一些临床参数共同导致了50%的组间显著性差异。最后，所开展的CAP分析显示，所分析参数的正确分类率为89.01%。
利用临床变量和exomiR相结合的平台可用于将T2D个体分类为发生DKD的风险人群。
