Fang Tingting, Zhang Qiuling, Wang Yanmei, Zha Hui
School of Public Health, Hangzhou Normal University, Hangzhou, 311121, Zhejiang Province, China.
Department of Endocrinology, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, 311121, Zhejiang Province, China.
Acta Diabetol. 2023 Jun;60(6):739-748. doi: 10.1007/s00592-023-02048-5. Epub 2023 Feb 21.
Several studies have revealed inconsistencies about the predictive properties of visceral adiposity index (VAI) in identifying chronic kidney disease (CKD). To date, it is unclear whether the VAI is a valuable diagnostic tool for CKD. This study intended to evaluate the predictive properties of the VAI in identifying CKD.
The PubMed, Embase, Web of Science, and Cochrane databases were searched for all studies that met our criteria from the earliest available article until November 2022. Articles were assessed for quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The heterogeneity was explored with the Cochran Q test and I test. Publication bias was detected using Deek's Funnel plot. Review Manager 5.3, Meta-disc 1.4, and STATA 15.0 were used for our study.
Seven studies involving 65,504 participants met our selection criteria and were therefore included in the analysis. Pooled sensitivity (Sen), specificity (Spe), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) were 0.67 (95%CI: 0.54-0.77), 0.75 (95%CI: 0.65-0.83), 2.7 (95%CI: 1.7-4.2), 0.44 (95%CI: 0.29-0.66), 6 (95%CI:3.00-14.00) and 0.77 (95%CI: 0.74-0.81), respectively. Subgroup analysis indicated that mean age of subjects was the potential source of heterogeneity. The Fagan diagram found that the predictive properties of CKD were 73% when the pretest probability was set to 50%.
The VAI is a valuable agent in predicting CKD and may be helpful in the detection of CKD. More studies are needed for further validation.
多项研究揭示了内脏脂肪指数（VAI）在识别慢性肾脏病（CKD）方面的预测特性存在不一致性。迄今为止，尚不清楚VAI是否是CKD的一种有价值的诊断工具。本研究旨在评估VAI在识别CKD方面的预测特性。
检索PubMed、Embase、Web of Science和Cochrane数据库，查找从最早的可用文章到2022年11月期间符合我们标准的所有研究。使用诊断准确性研究质量评估-2（QUADAS-2）对文章质量进行评估。采用Cochran Q检验和I²检验探索异质性。使用Deek漏斗图检测发表偏倚。本研究使用Review Manager 5.3、Meta-disc 1.4和STATA 15.0。
七项涉及65504名参与者的研究符合我们的纳入标准，因此被纳入分析。合并敏感度（Sen）、特异度（Spe）、阳性似然比（PLR）、阴性似然比（NLR）、诊断比值比（DOR）和曲线下面积（AUC）分别为0.67（95%CI：0.54 - 0.77）、0.75（95%CI：0.65 - 0.83）、2.7（95%CI：1.7 - 4.2）、0.44（95%CI：0.29 - 0.66）、6（95%CI：3.00 - 14.00）和0.77（95%CI：0.74 - 0.81）。亚组分析表明，研究对象的平均年龄是异质性的潜在来源。Fagan图发现，当检验前概率设定为50%时，CKD的预测特性为73%。
VAI是预测CKD的一种有价值的指标，可能有助于CKD的检测。需要更多研究进行进一步验证。
