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目的:探讨通过脑肠轴血清代谢物评估老年人认知功能的可行性。方法:采用方便抽样从2019年7月至10月间参加体检的健康人群队列和广西长寿人群微生态平衡队列中分别选取100和60名研究对象，构成健康人群子集和长寿人群子集。问卷调查收集人口学特征，采集空腹静脉血2~5 ml，采用液相色谱串联质谱技术进行血清非靶向代谢组学测定，通过文献检索汇总脑肠轴相关生物标志物，与非靶向代谢物测定结果取交集并进行注释。采用Spearman相关分析筛选与增龄相关的脑肠轴血清代谢物，采用多重线性回归法构建生物学年龄模型；采用简易精神状态检查表评估长寿人群子集认知功能，比较长寿人群子集不同认知功能老年人生物学年龄和时序年龄的差异。结果:健康人群子集和长寿人群子集年龄的n M（n Q1，n Q3）分别为64（38，72）和97（95，99）岁，女性分别为50（50.0%）和44名（73.3%）。初筛得到9种脑肠轴血清代谢物，分别为丙酸、谷氨酸、γ-氨基丁酸（GABA）、乳酸、5-羟色胺（5-HT）、色氨酸、氧化三甲胺、多巴胺和犬尿素。Spearman相关分析显示：谷氨酸和多巴胺与增龄呈正相关（n r值分别为0.208和0.524，均n P<0.05），色氨酸、5-HT和GABA与增龄呈负相关（n r值分别为-0.308、-0.533和-0.213，均n P<0.05）。构建生物学年龄模型为：n y=49.81-1.18×10n -5×GABA-1.82×10n -4×5-HT+1.99×10n -3×多巴胺+1.65×10n -6×谷氨酸-2.04×10n -6×色氨酸+2.36×性别，式中：n y为生物学年龄（岁），右侧各项依次为截距项、各代谢物相对水平和性别（男性=1，女性=2），模型的决定系数为0.50（n P0.05），认知功能较好的研究对象生物学年龄的n M（n Q1，n Q3）为51（38，54）岁，低于认知功能较差者［57（47，61）］（n P=0.040）。n 结论:可基于脑肠轴血清代谢物构建生物学年龄模型，对老年人认知功能进行评估。“,”Objective:To investigate the feasibility of assessing cognitive function of the elderly by serum metabolites of brain-gut axis.Methods:Convenience sampling was used to select 100 and 60 participants from the healthy population cohort and microecological balance cohort of the longevity population in Guangxi, to constitute subset of healthy population and longevity population, respectively. A questionnaire was used to investigate the demographic characteristics of the subjects, 2-5 ml of fasting venous blood was collected from the subjects, and the serum untargeted metabolomics was determined by liquid chromatography tandem mass spectrometry. The biomarkers related to the brain-gut axis were collected through literature retrieval, and the results were intersected with the untargeted metabolites and annotated. Spearman correlation analysis was used to screen serum metabolites of brain-gut axis associated with aging, and multiple linear regression method was used to construct biological age model. The mini mental status examination was used to evaluate the cognitive function of longevity population subsets. The differences of biological age and chronological age of longevity population subsets with different cognitive function were compared.Results:The n M (n Q1, n Q3) of subset of healthy population and longevity population were 64 (38, 72) and 97 (95, 99) years old, respectively, and there were 50 (50.0%) and 44 (73.3%) females, respectively. Nine serum metabolites of brain-gut axis were obtained by initial screening, which were propionic acid, glutamic acid, γ-aminobutyric acid (GABA), lactic acid, 5-hydroxytryptamine (5-HT), tryptophan, trimethylamine oxide, dopamine and canine urea. Spearman correlation analysis showed that glutamic acid and dopamine were positively correlated with aging (n r values were 0.208 and 0.524, respectively, all n P values<0.05), and tryptophan, 5-HT and GABA were negatively correlated with aging (n r values were -0.308, -0.533 and -0.213, respectively, all n P values<0.05). The biological age model was constructed as:n y=49.81-1.18×10n -5× GABA-1.82×10n -4×5-HT+1.99×10n -3×dopamine+1.65×10n -6×glutamic acid -2.04×10n -6×tryptophan+2.36×gender, where n y was the biological age (years), the items on the right were the intercept item, the relative concentration of each metabolite, and gender (male=1, female=2). The coefficient of determination of model was 0.50 (n P0.05). Then M (n Q1, n Q3) of the biological age of the subjects with better cognitive function was 51 (38, 54) years old, which was lower than that of the subjects with poor cognitive function [57 (47, 61)] (n P=0.040).n Conclusion:The biological age model can be constructed based on serum metabolites of brain-gut axis and used to evaluate the cognitive function of the elderly.