Ma Ya-Hui, Shen Xue-Ning, Xu Wei, Huang Yu-Yuan, Li Hong-Qi, Tan Lin, Tan Chen-Chen, Dong Qiang, Tan Lan, Yu Jin-Tai
Department of Neurology Qingdao Municipal Hospital Qingdao University Qingdao China.
Department of Neurology and Institute of Neurology Huashan Hospital Shanghai Medical College Fudan University Shanghai China.
Alzheimers Dement (Amst). 2020 Sep 15;12(1):e12041. doi: 10.1002/dad2.12041. eCollection 2020.
We sought lipid-metabolic biomarkers involved in the processes underlying cognitive decline and detected them in association with Alzheimer's disease (AD) phenotypes.
A least absolute shrinkage and selection operator logistic regression model was used to select lipids that best classified cognitive decline defined by a fast-annual rate of cognition. Lipid summary scores were constructed as predictors of cognitive decline by using this model. Multivariable-adjusted models tested the associations of risk score with AD phenotypes.
A model incorporating 17 selected lipids showed good discrimination and calibration. The lipid risk score was positively associated with the baseline Alzheimer Disease Assessment Scale-13-item cognitive subscale (ADAS-Cog13) score and cerebrospinal tau protein level, and predicted cognitive diagnoses. Additional results showing that individuals with increased lipid risk scores had rapid change rates of ADAS-Cog13 and brain atrophy further corroborated the predictive role of lipids.
A panel of blood lipids instead of individual lipid molecules could better diagnose and predict cognitive decline.
我们寻找参与认知衰退潜在过程的脂质代谢生物标志物，并检测它们与阿尔茨海默病（AD）表型的关联。
使用最小绝对收缩和选择算子逻辑回归模型来选择能最佳区分由快速年认知率定义的认知衰退的脂质。通过该模型构建脂质汇总分数作为认知衰退的预测指标。多变量调整模型检验风险评分与AD表型的关联。
包含17种选定脂质的模型显示出良好的区分度和校准度。脂质风险评分与基线阿尔茨海默病评估量表13项认知子量表（ADAS-Cog13）评分及脑脊液tau蛋白水平呈正相关，并能预测认知诊断。其他结果表明，脂质风险评分升高的个体ADAS-Cog13和脑萎缩的变化率较快，这进一步证实了脂质的预测作用。
一组血脂而非单个脂质分子能更好地诊断和预测认知衰退。
