Trares Kira, Wiesenfarth Manuel, Stocker Hannah, Perna Laura, Petrera Agnese, Hauck Stefanie M, Beyreuther Konrad, Brenner Hermann, Schöttker Ben
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 581, Heidelberg, 69120, Germany.
Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany.
Immun Ageing. 2024 Apr 3;21(1):23. doi: 10.1186/s12979-024-00427-2.
It is of interest whether inflammatory biomarkers can improve dementia prediction models, such as the widely used Cardiovascular Risk Factors, Aging and Dementia (CAIDE) model.
The Olink Target 96 Inflammation panel was assessed in a nested case-cohort design within a large, population-based German cohort study (n = 9940; age-range: 50-75 years). All study participants who developed dementia over 20 years of follow-up and had complete CAIDE variable data (n = 562, including 173 Alzheimer's disease (AD) and 199 vascular dementia (VD) cases) as well as n = 1,356 controls were selected for measurements. 69 inflammation-related biomarkers were eligible for use. LASSO logistic regression and bootstrapping were utilized to select relevant biomarkers and determine areas under the curve (AUCs).
The CAIDE model 2 (including Apolipoprotein E (APOE) ε4 carrier status) predicted all-cause dementia, AD, and VD better than CAIDE model 1 (without APOE ε4) with AUCs of 0.725, 0.752 and 0.707, respectively. Although 20, 7, and 4 inflammation-related biomarkers were selected by LASSO regression to improve CAIDE model 2, the AUCs did not increase markedly. CAIDE models 1 and 2 generally performed better in mid-life (50-64 years) than in late-life (65-75 years) sub-samples of our cohort, but again, inflammation-related biomarkers did not improve their predictive abilities.
Despite a lack of improvement in dementia risk prediction, the selected inflammation-related biomarkers were significantly associated with dementia outcomes and may serve as a starting point to further elucidate the pathogenesis of dementia.
炎症生物标志物能否改善痴呆预测模型，如广泛使用的心血管危险因素、衰老与痴呆（CAIDE）模型，这一问题备受关注。
在一项基于德国大型队列研究（n = 9940；年龄范围：50 - 75岁）的巢式病例对照设计中，对Olink Target 96炎症检测板进行了评估。选取了在20年随访期间发生痴呆且拥有完整CAIDE变量数据的所有研究参与者（n = 562，包括173例阿尔茨海默病（AD）和199例血管性痴呆（VD）病例）以及n = 1356名对照进行检测。69种与炎症相关的生物标志物符合使用条件。采用套索逻辑回归和自抽样法来选择相关生物标志物并确定曲线下面积（AUC）。
CAIDE模型2（包括载脂蛋白E（APOE）ε4携带者状态）在预测全因性痴呆、AD和VD方面优于CAIDE模型1（不包括APOE ε4），其AUC分别为0.725、0.752和0.707。尽管通过套索回归选择了20种（全因性痴呆）、7种（AD）和4种（VD）与炎症相关的生物标志物来改进CAIDE模型2，但AUC并未显著增加。在我们队列的中年（50 - 64岁）子样本中，CAIDE模型1和2总体上比老年（65 - 75岁）子样本表现更好，但同样，与炎症相关的生物标志物并未提高它们的预测能力。
尽管痴呆风险预测没有得到改善，但所选择的与炎症相关的生物标志物与痴呆结局显著相关，可能作为进一步阐明痴呆发病机制的起点。
