Zhou Xueling, Dai Ning, Yu Dandan, Niu Tong, Wang Shaohua
School of Medicine, Southeast University, Nanjing, China.
Department of Endocrinology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China.
Front Med (Lausanne). 2024 Jul 31;11:1443133. doi: 10.3389/fmed.2024.1443133. eCollection 2024.
This study aimed to investigate the role of galectin-3 (Gal-3; coded by LGALS3 gene), as a biomarker for MCI in T2DM patients and to develop and validate a predictive nomogram integrating galectin-3 with clinical risk factors for MCI prediction. Additionally, microRNA regulation of LGALS3 was explored.
The study employed a cross-sectional design. A total of 329 hospitalized T2DM patients were recruited and randomly allocated into a training cohort ( = 231) and a validation cohort ( = 98) using 7:3 ratio. Demographic data and neuropsychological assessments were recorded for all participants. Plasma levels of galectin-3 were measured using ELISA assay. We employed Spearman's correlation and multivariable linear regression to analyze the relationship between galectin-3 levels and cognitive performance. Furthermore, univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for MCI in T2DM patients. Based on these analyses, a predictive nomogram incorporating galectin-3 and clinical predictors was developed. The model's performance was evaluated in terms of discrimination, calibration, and clinical utility. Regulatory miRNAs were identified using bioinformatics and their interactions with LGALS3 were confirmed through qRT-PCR and luciferase reporter assays.
Galectin-3 was identified as an independent risk factor for MCI, with significant correlations to cognitive decline in T2DM patients. The developed nomogram, incorporating Gal-3, age, and education levels, demonstrated excellent predictive performance with an AUC of 0.813 in the training cohort and 0.775 in the validation cohort. The model outperformed the baseline galectin-3 model and showed a higher net benefit in clinical decision-making. Hsa-miR-128-3p was significantly downregulated in MCI patients, correlating with increased Gal-3 levels, while Luciferase assays confirmed miR-128-3p's specific binding and influence on LGALS3.
Our findings emphasize the utility of Gal-3 as a viable biomarker for early detection of MCI in T2DM patients. The validated nomogram offers a practical tool for clinical decision-making, facilitating early interventions to potentially delay the progression of cognitive impairment. Additionally, further research on miRNA128's regulation of Gal-3 levels is essential to substantiate our results.
本研究旨在探讨半乳糖凝集素-3（Gal-3；由LGALS3基因编码）作为2型糖尿病患者轻度认知障碍（MCI）生物标志物的作用，并开发和验证一种将半乳糖凝集素-3与MCI预测的临床风险因素相结合的预测列线图。此外，还探讨了微小RNA对LGALS3的调控作用。
本研究采用横断面设计。共招募了329例住院2型糖尿病患者，并按照7:3的比例随机分为训练队列（n = 231）和验证队列（n = 98）。记录所有参与者的人口统计学数据和神经心理学评估结果。采用酶联免疫吸附测定法（ELISA）检测血浆半乳糖凝集素-3水平。我们采用Spearman相关性分析和多变量线性回归分析来研究半乳糖凝集素-3水平与认知表现之间的关系。此外，进行单变量和多变量逻辑回归分析以确定2型糖尿病患者MCI的独立危险因素。基于这些分析，开发了一种包含半乳糖凝集素-3和临床预测指标的预测列线图。从区分度、校准度和临床实用性方面评估该模型的性能。使用生物信息学方法鉴定调控性微小RNA，并通过定量逆转录聚合酶链反应（qRT-PCR）和荧光素酶报告基因测定法确认它们与LGALS3的相互作用。
半乳糖凝集素-3被确定为MCI的独立危险因素，与2型糖尿病患者的认知衰退显著相关。所开发的列线图纳入了Gal-3、年龄和教育水平，在训练队列中的曲线下面积（AUC）为0.813，在验证队列中的AUC为0.775，显示出优异的预测性能。该模型优于基线半乳糖凝集素-3模型，在临床决策中显示出更高的净效益。Hsa-miR-128-3p在MCI患者中显著下调，与Gal-3水平升高相关，而荧光素酶测定法证实了miR-128-3p与LGALS3的特异性结合及其对LGALS3的影响。
我们的研究结果强调了Gal-3作为早期检测2型糖尿病患者MCI的可行生物标志物的实用性。经过验证的列线图为临床决策提供了一种实用工具，有助于早期干预以潜在地延缓认知障碍的进展。此外，进一步研究miRNA128对Gal-3水平的调控对于证实我们的结果至关重要。
