Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.
Department of Ophthalmology, Shanghai General Hospital, Shanghai, China.
BMC Med. 2022 Aug 15;20(1):252. doi: 10.1186/s12916-022-02449-3.
BACKGROUND: Plasma metabolomic profile is disturbed in dementia patients, but previous studies have discordant conclusions. METHODS: Circulating metabolomic data of 110,655 people in the UK Biobank study were measured with nuclear magnetic resonance technique, and incident dementia records were obtained from national health registers. The associations between plasma metabolites and dementia were estimated using Cox proportional hazard models. The 10-fold cross-validation elastic net regression models selected metabolites that predicted incident dementia, and a 10-year prediction model for dementia was constructed by multivariable logistic regression. The predictive values of the conventional risk model, the metabolites model, and the combined model were discriminated by comparison of area under the receiver operating characteristic curves (AUCs). Net reclassification improvement (NRI) was used to estimate the change of reclassification ability when adding metabolites into the conventional prediction model. RESULTS: Amongst 110,655 participants, the mean (standard deviation) age was 56.5 (8.1) years, and 51 186 (46.3%) were male. A total of 1439 (13.0%) developed dementia during a median follow-up of 12.2 years (interquartile range: 11.5-12.9 years). A total of 38 metabolites, including lipids and lipoproteins, ketone bodies, glycolysis-related metabolites, and amino acids, were found to be significantly associated with incident dementia. Adding selected metabolites (n=24) to the conventional dementia risk prediction model significantly improved the prediction for incident dementia (AUC: 0.824 versus 0.817, p =0.042) and reclassification ability (NRI = 4.97%, P = 0.009) for identifying high risk groups. CONCLUSIONS: Our analysis identified various metabolomic biomarkers which were significantly associated with incident dementia. Metabolomic profiles also provided opportunities for dementia risk reclassification. These findings may help explain the biological mechanisms underlying dementia and improve dementia prediction.
背景：痴呆症患者的血浆代谢组谱发生紊乱，但先前的研究得出了不一致的结论。
方法：采用核磁共振技术对英国生物库研究中 110655 人的循环代谢组数据进行了测量，并从国家健康登记处获得了痴呆症的发病记录。使用 Cox 比例风险模型估计了血浆代谢物与痴呆症之间的关联。10 倍交叉验证弹性网回归模型选择了预测痴呆症发病的代谢物，并通过多变量逻辑回归构建了痴呆症 10 年预测模型。通过比较受试者工作特征曲线（ROC）下面积（AUC）来区分传统风险模型、代谢物模型和联合模型的预测价值。净重新分类改善（NRI）用于估计在将代谢物加入传统预测模型时重新分类能力的变化。
结果：在 110655 名参与者中，平均（标准差）年龄为 56.5（8.1）岁，51186 名（46.3%）为男性。在中位随访 12.2 年（四分位间距：11.5-12.9 年）期间，共有 1439 名（13.0%）发生痴呆症。共发现 38 种代谢物，包括脂质和脂蛋白、酮体、糖酵解相关代谢物和氨基酸，与痴呆症发病显著相关。将选定的代谢物（n=24）添加到传统的痴呆症风险预测模型中，显著提高了痴呆症发病的预测能力（AUC：0.824 比 0.817，p=0.042）和高危人群的重新分类能力（NRI=4.97%，P=0.009）。
结论：我们的分析确定了与痴呆症发病显著相关的各种代谢组学生物标志物。代谢组学图谱也为痴呆症风险的重新分类提供了机会。这些发现可能有助于解释痴呆症的生物学机制，并提高痴呆症的预测能力。
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