Investigating causal effects of HDL-C on cognitive function through cross-sectional and Mendelian randomization analyses: concentration-response patterns and clues for Alzheimer's disease prevention.

IF 6.1 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Longmin Fan, Haitao Jiang, Zheyu Zhang
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引用次数: 0

Abstract

Background: Disrupted cholesterol homeostasis may accelerate cognitive aging. This study investigated the relationship between serum HDL-C levels and cognitive function, utilizing cross-sectional data and Mendelian randomization (MR) analysis.

Methods: A cross-sectional study was conducted using data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014, including 19,931 participants. Among them, 2,777 individuals aged 60 years and older with complete HDL-C levels and cognitive function data were included. Cognitive function was assessed using tests such as the Consortium to Establish a Registry for Alzheimer's Disease Immediate and Delayed Recall, the Animal Fluency Test, and the Digit Symbol Substitution Test. Additionally, MR analysis was employed to assess the causal relationship between genetically predicted HDL-C and dementia.

Results: Gender-stratified analyses revealed sex-specific patterns in the relationship between HDL-C and cognitive function. In fully adjusted linear models, men showed consistently positive associations across all cognitive domains, including delayed recall (β = 0.10, 95% CI 0.04-0.17, p < 0.001), immediate recall (β = 0.06, 95% CI 0.00-0.12, p = 0.047), verbal fluency (β = 0.20, 95% CI 0.14-0.26, p < 0.001), processing speed (β = 0.09, 95% CI 0.05-0.14, p < 0.001), and overall composite score (β = 0.45, 95% CI 0.29-0.62, p < 0.001). In women, these associations were attenuated or non-significant for immediate recall, delayed recall, and composite cognition, suggesting non-linearity. Further concentration-response analyses revealed a linear positive association in men and an inverted U-shaped relationship in women. MR analyses indicated a protective association between genetically predicted HDL-C and Alzheimer's disease risk (OR = 0.51, 95% CI 0.29-0.89, p = 0.019). However, sensitivity analyses revealed attenuation after MR-PRESSO outlier correction (β=-0.013, p = 0.756), and inconsistent estimates across methods, with significant heterogeneity (Q-test p < 0.001) and evidence of pleiotropy. In multivariable analysis, adjusting for LDL-C and TG, IVW (β = 0.290, p = 0.048) and Lasso regression (β = 0.752, p = 0.008) indicated weak positive correlations. However, MR-Egger (β = 0.752, p = 0.008) revealed potential pleiotropic interference (intercept p = 0.050).

Conclusions: Our findings suggest that maintaining optimal serum HDL-C levels may help preserve cognitive function in older adults. Notably, sex-specific associations were observed, warranting further investigation into underlying mechanisms.

通过横断面和孟德尔随机化分析调查HDL-C对认知功能的因果影响:浓度-反应模式和阿尔茨海默病预防的线索
背景:胆固醇稳态的破坏可能加速认知老化。本研究利用横断面数据和孟德尔随机化(MR)分析调查了血清HDL-C水平与认知功能之间的关系。方法:采用2011-2014年国家健康与营养检查调查(NHANES)的数据进行横断面研究,共纳入19931名参与者。其中包括2777名年龄在60岁及以上的人,他们有完整的HDL-C水平和认知功能数据。认知功能通过诸如阿尔茨海默病即时和延迟回忆注册协会、动物流畅性测试和数字符号替代测试等测试进行评估。此外,磁共振分析被用来评估基因预测HDL-C和痴呆之间的因果关系。结果:性别分层分析揭示了HDL-C与认知功能之间关系的性别特异性模式。在完全调整的线性模型中,男性在所有认知领域都表现出一致的正相关,包括延迟回忆(β = 0.10, 95% CI 0.04-0.17, p)。结论:我们的研究结果表明,维持最佳的血清HDL-C水平可能有助于保持老年人的认知功能。值得注意的是,观察到性别特异性关联,需要进一步研究潜在机制。
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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