APPL1 concentration as a potential biomarker in early-onset preeclampsia: correlations with adiponectin-leptin dysregulation and adverse neonatal outcomes

APPL1浓度作为早发型子痫前期的潜在生物标志物:与脂联素-瘦素失调和不良新生儿结局的相关性

阅读:3

Abstract

BACKGROUND: Preeclampsia (PE) is a severe pregnancy complication associated with metabolic dysregulation. APPL1 (adaptor protein, phosphotyrosine interacting with PH domain and leucine zipper 1) is a critical adaptor protein in adiponectin (ADP) and insulin signaling. Its role in preeclampsia (PE) remains underexplored. This study investigates APPL1 as a potential biomarker for early-onset PE and its links to adipokine imbalance, glycolipid metabolism, and neonatal outcomes. METHODS: A case-control study enrolled 116 pregnant women (45 PE vs. 71 controls) from a single center. Serum APPL1, ADP, and leptin (LEP) levels of all 116 participants were measured. Glycolipid metabolism indices and neonatal outcomes were collected. The correlation was analyzed by simple linear regression, and the logistic regression was used to evaluate the predictive effects of APPL1, ADP and LEP on PE. RESULTS: (1) Prepregnancy BMI (22.15 ± 1.80 vs. 21.03 ± 1.99 kg/m(2), p = 0.0028), APPL1 (82.65 ± 8.27 vs. 62.21 ± 12.41 pg/mL, p = 0.0047) and LEP (12.60 ± 2.83 vs. 8.66 ± 1.77 ng/mL, p = 0.0004). (2) APPL1 positively correlated with ADP only in PE (r = 0.0921, p = 0.0456). (3) Logistic regression identified APPL1 (OR = 1.405, p = 0.003) and LEP (OR = 3.618, p = 0.006) as PE risk factors, with ADP as protective (OR = 0.299, p = 0.003). (4) APPL1 showed extensive relationships with glycolipid dysregulation in PE (p < 0.05). (5) In PE, APPL1 inversely predicted adverse neonatal outcomes, including neonatal birth weight (r=-0.0149, p = 0.0396), ponderal index (r=-0.0198, p = 0.0024) and Apgar score (r=-0.0368, p = 0.0225). CONCLUSIONS: Elevated APPL1 is a potential and novel biomarker for early-onset PE, and reflecting adiponectin-leptin dysregulation, glycolipid metabolic disturbances, and adverse neonatal prognosis. Its integration into clinical prediction models warrants further validation.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。