Relationships of Thickness of Perirenal Fat with Urinary Levels of MCP-1 and NGAL in Patients with Hypertension.

IF 4.7 Q1 ENDOCRINOLOGY & METABOLISM Journal of Obesity & Metabolic Syndrome Pub Date : 2024-09-13 DOI:10.7570/jomes24002

Anna Bragina, Yulia Rodionova, Konstantin Osadchiy, Daria Bayutina, Maria K Vasilchenko, Alexander Fomin, Valeriy Podzolkov

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Abstract

Background: We conducted a study to determine the relationships between perirenal fat (PRF) thickness and urinary levels of monocyte chemoattractant protein-1 (MCP-1) and neutrophil gelatinase-associated lipocalin (NGAL) in patients with hypertension (HTN).

Methods: In 338 HTN patients (aged 63.51±12.3 on average), MCP-1 and NGAL levels were studied using enzyme-linked immunosorbent assay (ELISA). To measure PRF thickness, all patients underwent CT scans.

Results: We considered PRF thickness ≥1.91 cm as the diagnostic threshold for perirenal obesity. Patients with excessive PRF thickness exhibited significantly lower levels of MCP-1 and NGAL compared with those with PRF thickness ≥1.91 cm: 0.98 pg/mL (interquartile range, 0.21 to 2.05) vs. 2.35 pg/mL (0.37 to 5.22) for MCP-1 and 50.0 pg/mL (48.9 to 67.8) vs. 98.3 pg/mL (68.4 to 187.1) for NGAL. We found a relationship of PRF thickness with both MCP-1 (r=0.46, P<0.05) and NGAL (r=0.53, P<0.05), the levels of which were significantly different in patients with first- and third-stage chronic kidney disease: 0.33 pg/mL (0.21 to 1.35) vs. 4.47 pg/mL (0.23 to 10.81); 50.0 pg/mL (49.4 to 85.5) vs. 126.45 pg/mL (57.5 to 205.15), respectively (P=0.04). Patients with metabolically healthy obesity (MHO) had significantly lower MCP-1 levels than those with metabolically unhealthy obesity (MUHO): 0.65 pg/mL (0.21 to 2.15) vs. 3.28 pg/mL (2.05 to 5.22) (P=0.014). MHO patients showed significantly lower NGAL levels than MUHO patients: 50.0 pg/mL (49.4 to 62.2) vs. 98.3 pg/mL (50.0 to 174.8) (P=0.04). Multiple linear regression analysis revealed significant relationships of MCP-1 with PRF thickness (β±standard error, 0.41±0.15; P<0.001) and smoking (0.26±0.13; P=0.01) and of NGAL with age (0.45±0.16; P<0.01) and PRF thickness (0.49±0.15; P<0.001).

Conclusion: We identified higher concentrations of renal fibrosis markers in patients with perirenal and metabolically unhealthy obesity as well as a link between PRF thickness and MCP-1 and NGAL levels in urine.

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高血压患者肾周脂肪厚度与尿液中 MCP-1 和 NGAL 水平的关系

研究背景我们进行了一项研究，以确定高血压（HTN）患者肾周脂肪（PRF）厚度与尿液中单核细胞趋化蛋白-1（MCP-1）和中性粒细胞明胶酶相关脂联素（NGAL）水平之间的关系：采用酶联免疫吸附试验（ELISA）对338名高血压患者（平均年龄为63.51±12.3岁）的MCP-1和NGAL水平进行研究。为了测量 PRF 厚度，所有患者都接受了 CT 扫描：我们将 PRF 厚度≥1.91 厘米视为肾周肥胖的诊断阈值。与 PRF 厚度≥1.91 厘米的患者相比，PRF 厚度过大的患者的 MCP-1 和 NGAL 水平明显较低：MCP-1的水平为0.98 pg/mL（四分位数间距为0.21至2.05）对2.35 pg/mL（0.37至5.22），NGAL的水平为50.0 pg/mL（48.9至67.8）对98.3 pg/mL（68.4至187.1）。我们发现 PRF 厚度与 MCP-1 都有关系（r=0.46，PPP=0.04）。代谢健康型肥胖（MHO）患者的 MCP-1 水平明显低于代谢不健康型肥胖（MUHO）患者：0.65 pg/mL (0.21 to 2.15) vs. 3.28 pg/mL (2.05 to 5.22) (P=0.014)。MHO 患者的 NGAL 水平明显低于 MUHO 患者：50.0皮克/毫升（49.4至62.2）对98.3皮克/毫升（50.0至174.8）（P=0.04）。多元线性回归分析显示，MCP-1 与 PRF 厚度（β± 标准误差，0.41±0.15；PP=0.01）和 NGAL 与年龄（0.45±0.16；PPConclusion）有显著关系：我们发现肾周肥胖和代谢不健康肥胖患者的肾脏纤维化标记物浓度较高，而且PRF厚度与尿液中MCP-1和NGAL水平之间存在联系。

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来源期刊

Journal of Obesity & Metabolic Syndrome ENDOCRINOLOGY & METABOLISM-

CiteScore

8.30

自引率

9.60%

发文量

审稿时长

19 weeks

期刊介绍： The journal was launched in 1992 and diverse studies on obesity have been published under the title of Journal of Korean Society for the Study of Obesity until 2004. Since 2017, volume 26, the title is now the Journal of Obesity & Metabolic Syndrome (pISSN 2508-6235, eISSN 2508-7576). The journal is published quarterly on March 30th, June 30th, September 30th and December 30th. The official title of the journal is now "Journal of Obesity & Metabolic Syndrome" and the abbreviated title is "J Obes Metab Syndr". Index words from medical subject headings (MeSH) list of Index Medicus are included in each article to facilitate article search. Some or all of the articles of this journal are included in the index of PubMed, PubMed Central, Scopus, Embase, DOAJ, Ebsco, KCI, KoreaMed, KoMCI, Science Central, Crossref Metadata Search, Google Scholar, and Emerging Sources Citation Index (ESCI).