切换至 "中华医学电子期刊资源库"
高级检索
中华脑科疾病与康复杂志(电子版)

中华脑科疾病与康复杂志(电子版) ›› 2026, Vol. 16 ›› Issue (03) : 135 -149. doi: 10.3877/cma.j.issn.2095-123X.2026.03.002

临床研究

肠道菌群与糖尿病神经病变间的因果推断：通过循环代谢物和蛋白质介导的两步孟德尔随机化研究
刘佳鑫1, 张泽2, 张黎3,†()   
  1. 1100000　北京，中日友好临床医学研究所
    2100191　北京，北京大学医学部
    3100029　北京，中日友好医院神经外科
  • 收稿日期:2025-12-09 出版日期:2026-06-15
  • 通信作者: 张黎

Causal inference between gut microbiota and diabetic neuropathy: a two-step Mendelian randomization study mediated by circulating metabolites and proteins

Jiaxin Liu1, Ze Zhang2, Li Zhang3,†()   

  1. 1Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing 100000, China
    2Peking University Health Science Center, Beijing 100191, China
    3Department of Neurosurgery, China-Japan Friendship Hospital, Beijing 100029, China
  • Received:2025-12-09 Published:2026-06-15
  • Corresponding author: Li Zhang
  • Supported by:
    Beijing Natural Science Foundation-Changping Innovation Joint Fund Project(L244029)
全文 ( ) 下载PDF ( ) 导出引用
引用本文：

刘佳鑫, 张泽, 张黎. 肠道菌群与糖尿病神经病变间的因果推断：通过循环代谢物和蛋白质介导的两步孟德尔随机化研究[J/OL]. 中华脑科疾病与康复杂志(电子版), 2026, 16(03): 135-149.

Jiaxin Liu, Ze Zhang, Li Zhang. Causal inference between gut microbiota and diabetic neuropathy: a two-step Mendelian randomization study mediated by circulating metabolites and proteins[J/OL]. Chinese Journal of Brain Diseases and Rehabilitation(Electronic Edition), 2026, 16(03): 135-149.

目的

采用孟德尔随机化（MR）方法评估肠道菌群（GM）与糖尿病神经病变（DN）间的因果关系，并筛选可能在其中起介导作用的循环代谢物与蛋白质。

方法

GM、DN的全基因组关联研究（GWAS）数据分别来源于MiBioGen联盟及FinnGen联盟R12版本数据集；529种循环代谢物、2994种蛋白质的GWAS数据分别来自2篇已发表文献，相关结果已被GWAS Catalog数据库收录。通过Steiger方向性检验排除GM与DN的反向因果关系，采用两样本MR分析评估GM与DN的因果关系。进一步采用两步法MR、MR中介分析及多变量MR分析筛选可能在上述因果路径中发挥介导作用的循环代谢物与蛋白质。

结果

共有6种GM与DN存在因果关系，其中嗜胆甾醇真杆菌属（P=0.031）和嗜木聚糖真杆菌属（P<0.001）与DN呈负相关，而厚壁菌门（P=0.026）、阿德勒克鲁茨菌属（P=0.022）、另枝菌属（P=0.046）及克里斯滕森菌科R-7群（P=0.009）与DN呈正相关。MR中介分析结果显示，嗜木聚糖真杆菌属可通过上调体内髓样细胞白血病-1蛋白（MCL-1）（P=0.003）及下调HIV-1 Tat交互蛋白2（HTATIP2）（P<0.001）和基质金属蛋白酶-16（MMP-16）（P=0.002）来减轻DN，而厚壁菌门通过降低体内酸性鞘磷脂酶样磷酸二酯酶3A水平（P=0.008）、另枝菌属通过升高循环中X-11423—O-磺基-L-酪氨酸水平（P=0.014）促进DN的进展。

结论

嗜木聚糖真杆菌属通过上调MCL-1、下调HTATIP2及MMP-16，对DN发挥保护作用；厚壁菌门通过降低SMPDL3A、另枝菌属通过升高X-11423—O-磺基-L-酪氨酸促进DN进展。上述发现为DN的微生物-代谢干预提供了新的候选靶点。

关键词: 肠道菌群, 糖尿病神经病变, 循环代谢物, 孟德尔随机化
Objective

To evaluate the causal relationships between gut microbiota (GM) and diabetic neuropathy (DN) using Mendelian randomization (MR), and to identify circulating metabolites and proteins that may mediate these associations.

Methods

Genome-wide association study (GWAS) data for GM and DN were derived from the MiBioGen consortium and the FinnGen R12 dataset, respectively. GWAS summary statistics for 529 circulating metabolites and 2994 proteins were obtained from two published articles, and their corresponding GWAS results have been deposited in the GWAS Catalog database. The Steiger directionality test was employed to exclude reverse causation between GM and DN. Two-sample MR analysis was conducted to assess the causal effect of GM on DN. Two-step MR, MR mediation analysis, and multivariable MR were further utilized to screen for circulating metabolites and proteins that may mediate the aforementioned causal pathways.

Results

A total of six GM taxa were causally associated with DN. Specifically, Eubacterium coprostanoligenes group (P=0.031) and Eubacterium xylanophilum group (P<0.001) were inversely associated with DN, whereas Firmicutes (P=0.026), Adlercreutzia (P=0.022), Alistipes (P=0.046), and Christensenellaceae R-7 group (P=0.009) were positively associated with DN. MR mediation analysis revealed that Eubacterium xylanophilum group alleviated DN by upregulating myeloid cell leukemia-1 protein (MCL-1) (P=0.003) and downregulating HIV-1 Tat interactive protein 2 (HTATIP2) (P<0.001) and matrix metalloproteinase-16 (MMP-16) (P=0.002). Conversely, Firmicutes promoted DN progression by reducing circulating levels of sphingomyelin phosphodiesterase acid-like 3A (SMPDL3A) (P=0.008), while Alistipes facilitated DN progression by elevating circulating levels of X-11423—O-sulfo-L-tyrosine (P=0.014).

Conclusions

Eubacterium xylanophilum group exerts a protective effect against DN by upregulating MCL-1 and downregulating HTATIP2 and MMP-16. In contrast, Firmicutes promotes DN progression by decreasing SMPDL3A levels, and Alistipes enhances DN progression by increasing X-11423—O-sulfo-L-tyrosine levels. These findings provide novel candidate targets for microbiota- and metabolite-based interventions in DN.

Key words: Gut microbiota, Diabetic neuropathy, Circulating metabolite, Mendelian randomization
图1 孟德尔随机化方法流程图SNP：单核苷酸多态性
Fig.1 Flowchart of Mendelian randomization analyses
表1 孟德尔随机化分析的数据来源与基本信息
Tab.1 Data sources and basic information used in the Mendelian randomization analyses
数据集 数据来源 第一作者 人群 样本量 网络链接
肠道菌群 MiBioGen联盟 - 欧洲人 18 340 https://mibiogen.gcc.rug.nl
糖尿病神经病变 FinnGen R12 - 芬兰人 96 474 https://r12.finngen.fi/region/DM_NEUROPATHY/10:129319100-129819100
循环代谢物 PMID: 24816252 Shin等[13] 欧洲人 7 824 https://www.ebi.ac.uk/gwas/publications/24816252
循环蛋白质 PMID: 29875488 Sun等[14] 欧洲人 3 301 https://www.ebi.ac.uk/gwas/publications/29875488
图2 基于逆方差加权法的肠道菌群与糖尿病神经病变因果关系的孟德尔随机化散点图A：阿德勒克鲁茨菌属；B：另枝菌属；C：克里斯滕森菌科R-7群；D：嗜胆甾醇真杆菌属；E：嗜木聚糖真杆菌属；F：厚壁菌门；SNP：单核苷酸多态性；DN：糖尿病神经病变；IVW：逆方差加权法；MR-Egger：MR-Egger回归法；WM：加权中位数法；WMode：加权众数法
Fig.2 Mendelian randomization scatter plot of the causal relationship between gut microbiota and diabetic neuropathy based on the inverse variance weighted method
表2 肠道菌群与糖尿病神经病变因果关系的两样本孟德尔随机化分析
Tab.2 Two-sample Mendelian randomization study on causal relationship of gut microbiota on diabetic peripheral neuropathy
肠道菌群 方法 nSNP P值 β Q值 Q检验P值 MR-Egger截距 截距P值 全局检验P值
厚壁菌门 IVW 16 0.026 0.302 24.525 0.057 -0.011 0.649 0.068
  MR-Egger 16 0.199 0.438 24.153 0.044      
  WM 16 0.296 0.167          
  WMode 16 0.819 0.056          
克里斯滕森菌科R-7群 IVW 9 0.009 0.417 3.672 0.886 -0.001 0.980 0.898
  MR-Egger 9 0.446 0.429 3.671 0.817      
  WM 9 0.115 0.326          
  WMode 9 0.486 0.226          
嗜胆甾醇真杆菌属 IVW 13 0.031 -0.334 16.071 0.188 -0.039 0.317 0.216
  MR-Egger 13 0.665 0.262 14.613 0.200      
  WM 13 0.135 -0.296          
  WMode 13 0.378 -0.332          
嗜木聚糖真杆菌属 IVW 9 0.001 -0.327 2.295 0.971 -0.019 0.445 0.707
  MR-Egger 9 0.467 -0.167 1.639 0.977      
  WM 9 0.007 -0.309          
  WMode 9 0.055 -0.301          
阿德勒克鲁茨菌属 IVW 8 0.023 0.353 12.434 0.087 -0.091 0.143 0.119
  MR-Egger 8 0.068 1.357 8.437 0.208      
  WM 8 0.027 0.383          
  WMode 8 0.229 0.387          
另枝菌属 IVW 13 0.047 0.320 15.767 0.202 -0.035 0.469 0.221
  MR-Egger 13 0.278 0.899 14.998 0.187      
  WM 13 0.038 0.418          
  WMode 13 0.264 0.404          
图3 肠道菌群与糖尿病神经病变因果关系的留一法敏感性分析图A：阿德勒克鲁茨菌属；B：另枝菌属；C：克里斯滕森菌科R-7群；D：嗜胆甾醇真杆菌属；E：嗜木聚糖真杆菌属；F：厚壁菌门
Fig.3 Leave-one-out plot of the causal relationship between gut microbiota and diabetic neuropathy
图4 肠道菌群与糖尿病神经病变因果关系敏感性分析的森林图A：阿德勒克鲁茨菌属；B：另枝菌属；C：克里斯滕森菌科R-7群；D：嗜胆甾醇真杆菌属；E：嗜木聚糖真杆菌属；F：厚壁菌门；IVW：逆方差加权法；MR-Egger：MR-Egger回归法
Fig.4 Forest plot of sensitivity analysis for the causal relationship between gut microbiota and diabetic neuropathy
图5 基于逆方差加权法的肠道菌群与糖尿病神经病变因果关系漏斗图A：阿德勒克鲁茨菌属；B：另枝菌属；C：克里斯滕森菌科R-7群；D：嗜胆甾醇真杆菌属；E：嗜木聚糖真杆菌属；F：厚壁菌门；IVW：逆方差加权法；MR-Egger：MR-Egger回归法；IV：工具变量
Fig.5 Funnel plot of the causal relationship between gut microbiota and diabetic neuropathy based on the inverse variance weighted method
表3 中介变量与糖尿病神经病变因果关系的两步法孟德尔随机化分析
Tab.3 Mendelian randomization causal relationship results between mediators and diabetic neuropathy
暴露 方法 β P值 OR值 95%CI
循环代谢物          
脯氨酸 WR 1.853 0.012 6.381 1.516~26.865
吲哚-3-乳酸 WR -3.002 0.041 0.049 0.003~0.880
3-羧基-4-甲基-5-丙基-2-呋喃丙酸酯 WR -1.355 0.007 0.259 0.096~0.695
乙酰左旋肉碱 IVW -1.787 0.039 0.167 0.031~0.919
丝氨酸 IVW -2.778 0.001 0.062 0.012~0.329
X-11423—O-磺基-L-酪氨酸 IVW 3.654 0.009 38.632 2.444~610.550
X-11452 WR 1.814 0.011 6.137 1.519~24.783
X-11786—甲基半胱氨酸 WR 1.213 0.039 3.364 1.061~10.662
ADpSGEGDFXAEGGGVR IVW -0.859 0.013 0.424 0.215~0.834
X-12441—12-羟基二十碳四烯酸 WR 0.507 0.016 1.659 1.097~2.510
辛酰肉碱 IVW 0.618 0.040 1.856 1.028~3.352
X-12855 IVW -2.096 0.007 0.123 0.027~0.561
十八二酸盐 IVW -1.970 0.003 0.139 0.037~0.522
循环蛋白质          
蛋白FAM171B WR 0.439 0.027 1.552 1.051~2.291
蛋白FAM213A WR -0.253 0.044 0.777 0.607~0.993
四关节盒蛋白1 IVW -0.209 0.019 0.812 0.682~0.966
半乳糖苷3(4)-L-岩藻糖基转移酶 WR 0.062 0.034 1.064 1.005~1.126
N-乙酰乳糖胺苷β-1,6-N-乙酰氨基葡萄糖基转移酶 WR 0.417 0.008 1.518 1.115~2.066
天冬氨酸氨基转移酶 IVW -0.203 0.008 0.816 0.702~0.948
谷胱甘肽S转移酶ω-1 IVW -0.068 0.027 0.935 0.880~0.992
细胞间黏附分子2 IVW 0.235 0.025 1.344 1.342~1.223
干扰素α/β受体1 WR 0.309 0.021 1.363 1.047~1.773
白细胞介素-13受体亚基α-1 IVW -0.243 0.010 0.784 0.653~0.943
白细胞介素-17受体A IVW -0.233 0.038 0.793 0.636~0.988
白细胞介素-27 WR 0.294 0.026 1.342 1.036~1.738
富含AT相互作用结构域蛋白3A WR 0.367 0.030 1.443 1.036~2.010
激肽释放酶-11 WR 0.085 0.010 1.089 1.020~1.162
激肽释放酶-14 IVW 0.097 0.032 1.102 1.008~1.204
激肽释放酶-7 WR -0.379 0.020 0.685 0.497~0.943
白细胞特异性转录本1蛋白 WR 0.479 0.007 1.615 1.143~2.281
黏膜定向细胞黏附分子1 WR -0.382 0.003 0.68 0.529~0.879
髓样细胞白血病-1蛋白 WR -0.923 0.001 0.397 0.244~0.647
微纤维蛋白相关蛋白2 IVW 0.190 0.029 1.209 1.019~1.436
基质金属蛋白酶-16 WR 0.496 0.003 1.642 1.189~2.265
巨噬细胞甘露聚糖受体1 WR 0.101 0.024 1.107 1.013~1.209
C型甘露糖受体2 IVW -0.173 0.046 0.841 0.710~0.997
神经连接蛋白-3-β IVW -0.278 0.007 0.758 0.618~0.928
脯氨酰4-羟化酶亚基α-2 WR -0.352 0.048 0.703 0.496~0.997
肽基甘氨酸α-酰胺化单加氧酶 IVW 0.073 0.047 1.076 1.000~1.157
原胶原C端肽酶增强因子1 WR -0.229 0.041 0.796 0.639~0.991
蛋白精氨酸N-甲基转移酶3 WR -0.318 0.011 0.728 0.571~0.929
妊娠特异性β-1-糖蛋白2 WR 0.498 0.047 1.646 1.006~2.692
B细胞淋巴瘤6蛋白 WR -0.198 0.036 0.821 0.683~0.987
Ras相关蛋白26 IVW 0.172 0.041 1.187 1.007~1.400
RNF149 WR -0.289 0.021 0.749 0.585~0.958
多聚糖二磷酸二糖-蛋白糖基转移酶亚基1 WR 0.148 0.033 1.159 1.012~1.329
分泌素蛋白 WR 0.584 0.034 1.793 1.046~3.073
14-3-3蛋白σ WR 0.326 0.043 1.385 1.010~1.899
单免疫球蛋白IL-1相关受体 WR -0.063 0.034 0.939 0.885~0.995
SLIT样蛋白和NTRK样蛋白5 WR -0.522 0.019 0.594 0.383~0.920
酸性鞘磷脂酶样磷酸二酯酶3A WR 0.067 0.018 1.069 1.012~1.131
信号转导适配蛋白1 IVW 0.231 0.049 1.260 1.000~1.587
C17orf78 WR 0.226 0.043 1.254 1.007~1.561
含SUN结构域蛋白3 WR -0.238 0.038 0.788 0.629~0.987
TAP结合蛋白 IVW 0.196 0.027 1.217 1.023~1.448
转铁蛋白受体蛋白1 IVW -0.240 0.001 0.787 0.681~0.909
胆汁盐激活脂肪酶 WR 0.350 0.031 1.432 1.033~1.984
补体因子H相关蛋白5 IVW -0.072 0.029 0.931 0.873~0.993
碳水化合物硫转移酶15 WR -0.452 0.017 0.637 0.439~0.922
碳水化合物硫转移酶1 WR -0.357 0.017 0.700 0.523~0.938
肌酸激酶M型 IVW -0.263 0.011 0.769 0.628~0.942
C型凝集素结构域家族2成员 WR -0.307 0.034 0.736 0.554~0.977
CXADR样膜蛋白 WR -0.306 0.001 0.736 0.612~0.886
UMP-CMP激酶 IVW -0.406 0.044 0.666 0.449~0.989
硫酸软骨素N-乙酰半乳糖胺转移酶2 WR -0.307 0.019 0.735 0.569~0.951
分泌型外胚层发育蛋白A WR 0.404 0.040 1.498 1.019~2.203
红细胞生成素 WR -0.408 0.014 0.665 0.480~0.920
受体酪氨酸蛋白激酶ErbB-3 WR -0.345 0.033 0.708 0.516~0.973
结节漏斗肽 WR 0.431 0.001 1.539 1.240~1.910
精子相关抗原11B WR -0.624 0.001 0.536 0.395~0.729
表4 基于逆方差加权法的肠道菌群与循环代谢物/蛋白质因果关联分析
Tab.4 Causal associations between gut microbiota and circulating metabolites/proteins identified using the inverse variance weighted method
肠道菌群 循环代谢物/蛋白质 方法 P值 β Q值 Q检验P值 MR-Egger截距 截距P值 全局检验P值
厚壁菌门 吲哚-3-乳酸 IVW 0.048 0.056 6.209 0.286 -0.004 0.713 0.292
  肽聚糖识别蛋白 IVW 0.044 -0.223 9.896 0.826 0.008 0.721 0.824
  B细胞淋巴瘤6蛋白 IVW 0.014 -0.272 8.459 0.904 -0.001 0.986 0.907
  Ras相关蛋白26 IVW 0.039 -0.228 5.331 0.989 0.009 0.619 0.992
  多萜醇磷酸寡糖蛋白寡糖转移酶 IVW 0.043 -0.225 15.145 0.441 -0.002 0.939 0.506
  14-3-3蛋白σ IVW 0.045 0.259 20.377 0.158 0.049 0.019 0.153
  酸性鞘磷脂酶样磷酸二酯酶3A IVW 0.008 -0.295 9.078 0.873 0.001 0.984 0.912
嗜木聚糖真杆菌属 天冬氨酸氨基转移酶 IVW 0.019 0.338 7.157 0.519 0.013 0.736 0.565
  HIV-1 Tat相互作用蛋白2 IVW <0.001 -0.615 5.019 0.756 -0.069 0.095 0.787
  髓样细胞白血病-1蛋白 IVW 0.003 0.421 6.357 0.607 -0.023 0.545 0.663
  基质金属蛋白酶-16 IVW 0.002 -0.439 2.896 0.941 0.018 0.622 0.951
  脯氨酰4-羟化酶亚基α‐2 IVW 0.011 0.368 4.268 0.832 0.001 0.971 0.564
嗜胆甾醇真杆菌属 多萜醇磷酸寡糖蛋白寡糖转移酶 IVW 0.022 -0.313 3.574 0.990 0.036 0.312 0.985
克里斯滕森菌科R-7群 细胞间黏附分子2 IVW 0.031 0.353 7.398 0.494 0.047 0.243 0.527
  C17orf78 IVW 0.035 0.347 7.805 0.453 -0.021 0.598 0.444
另枝菌属 髓样细胞白血病-1蛋白 IVW 0.039 0.295 11.99 0.446 0.020 0.631 0.469
  X-11423—O-磺基-L-酪氨酸 IVW 0.014 0.035 9.981 0.273 0.005 0.305 0.308
阿德勒克鲁茨菌属 辛酰基肉碱 IVW 0.023 0.083 1.618 0.997      
  白细胞介素17受体A IVW 0.033 0.361 12.86 0.076 -0.076 0.291 0.098
  激肽释放酶-11 IVW 0.034 0.317 9.992 0.189 -0.104 0.082 0.230
  激肽释放酶-14 IVW 0.031 0.294 8.306 0.306 -0.111 0.067 0.339
  髓样细胞白血病-1蛋白 IVW 0.017 0.299 5.159 0.641 -0.052 0.337 0.681
表5 循环代谢物/蛋白质在肠道菌群与糖尿病神经病变因果关系中的中介分析
Tab.5 Mediation analysis of circulating metabolites/proteins in the causal pathway between gut microbiota and diabetic neuropathy
暴露 循环代谢物/蛋白质 总效应（β） 直接效应（β1） 直接效应（β2） 间接效应（β1×β2） P值
嗜木聚糖真杆菌属 髓细胞白血病1蛋白 -0.327 0.421 -0.923 -0.389 0.022
  HIV-1 Tat相互作用蛋白2 -0.327 -0.615 0.107 -0.066 0.031
  基质金属蛋白酶-16 -0.327 -0.440 0.496 -0.218 0.032
  天冬氨酸氨基转移酶 -0.327 0.338 -0.203 -0.069 0.080
  脯氨酰4-羟化酶亚基α-2 -0.327 0.368 -0.352 -0.130 0.118
克里斯滕森菌科R-7群 C17orf78 0.417 0.347 0.226 0.078 0.144
  细胞间黏附分子2 0.417 0.353 0.283 0.100 0.087
嗜胆甾醇真杆菌属 多萜醇磷酸寡糖蛋白寡糖转移酶 -0.334 -0.310 0.148 -0.046 0.119
阿德勒克鲁茨菌属 髓样细胞白血病-1蛋白 0.353 0.300 -0.570 -0.171 0.098
  激肽释放酶-11 0.353 0.317 0.085 0.027 0.102
  辛酰基肉碱 0.353 0.083 0.618 0.051 0.128
  激肽释放酶-14 0.353 0.294 0.097 0.029 0.128
  白细胞介素17受体A 0.353 0.361 -0.233 -0.084 0.138
厚壁菌门 Rab相关蛋白26 0.302 -0.228 0.172 -0.039 0.147
  14-3-3蛋白σ 0.302 0.259 0.326 0.084 0.154
  吲哚-3-乳酸 0.302 0.056 -3.002 -0.169 0.155
  肽基甘氨酸α-酰胺化单加氧酶 0.302 -0.223 0.073 -0.016 0.157
  多萜醇磷酸寡糖蛋白寡糖转移酶 0.302 -0.225 0.148 -0.033 0.142
  酸性鞘磷脂酶样磷酸二酯酶3A 0.302 -0.295 0.067 -0.020 0.047
  B细胞淋巴瘤6蛋白 0.302 -0.272 -0.198 0.054 0.111
另枝菌属 X-11423—O-磺基-L-酪氨酸 0.320 0.035 3.654 0.128 0.045
  髓细胞白血病1蛋白 0.320 0.296 -0.570 -0.169 0.125
表6 肠道菌群与糖尿病神经病变因果通路的多变量孟德尔随机化分析
Tab.6 Multivariable Mendelian randomization analysis of causal pathways between gut microbiota and diabetic neuropathy
菌群 中介变量 暴露 β P值
完全介导        
阿德勒克鲁茨菌属 激肽释放酶-11 肠道菌群 0.016 0.467
    循环代谢物/蛋白质 0.414 <0.001
  白细胞介素-17受体A 肠道菌群 0.044 0.076
    循环代谢物/蛋白质 0.216 0.001
嗜木聚糖真杆菌属 HIV-1 Tat相互作用蛋白2 肠道菌群 -0.001 0.977
    循环代谢物/蛋白质 0.250 <0.001
  基质金属蛋白酶-16 肠道菌群 -0.016 0.534
    循环代谢物/蛋白质 0.283 <0.001
  髓细胞白血病1蛋白 肠道菌群 -0.002 0.927
    循环代谢物/蛋白质 -0.292 <0.001
  脯氨酰4-羟化酶亚基α-2 肠道菌群 0.006 0.812
    循环代谢物/蛋白质 0.251 <0.001
厚壁菌门 吲哚-3-乳酸 肠道菌群 -0.030 0.419
    循环代谢物/蛋白质 -3.125 <0.001
部分介导        
阿德勒克鲁茨菌属 激肽释放酶-14 肠道菌群 0.076 <0.001
    循环代谢物/蛋白质 0.479 <0.001
  辛酰基肉碱 肠道菌群 0.121 <0.001
    循环代谢物/蛋白质 -0.936 0.030
另枝菌属 X-11423—O-磺基-L-酪氨酸 肠道菌群 0.216 <0.001
    循环代谢物/蛋白质 3.852 <0.001
  髓细胞白血病1蛋白 肠道菌群 0.247 <0.001
    循环代谢物/蛋白质 0.115 0.003
嗜胆甾醇真杆菌属 多萜醇磷酸寡糖蛋白寡糖转移酶 肠道菌群 0.074 0.001
    循环代谢物/蛋白质 0.708 <0.001
厚壁菌门 酸性鞘磷脂酶样磷酸二酯酶3A 肠道菌群 0.209 <0.001
    循环代谢物/蛋白质 0.254 <0.001
  多萜醇磷酸寡糖蛋白寡糖转移酶 肠道菌群 -0.135 <0.001
    循环代谢物/蛋白质 0.553 <0.001
  Ras相关蛋白26 肠道菌群 -0.226 <0.001
    循环代谢物/蛋白质 0.180 0.002
  14-3-3蛋白σ 肠道菌群 -0.397 <0.001
    循环代谢物/蛋白质 0.575 <0.001
克里斯滕森菌科R-7群 细胞间黏附分子2 肠道菌群 0.107 <0.001
    循环代谢物/蛋白质 0.128 0.004
不介导        
阿德勒克鲁茨菌属 髓细胞白血病1蛋白 肠道菌群 0.062 0.010
    循环代谢物/蛋白质 0.185 0.110
嗜木聚糖真杆菌属 天冬氨酸氨基转移酶 肠道菌群 0.066 0.009
    循环代谢物/蛋白质 -0.060 0.224
厚壁菌门 B细胞淋巴瘤6蛋白 肠道菌群 -0.273 <0.001
    循环代谢物/蛋白质 -0.020 0.730
  肽基甘氨酸α-酰胺化单加氧酶 肠道菌群 -0.285 <0.001
    循环代谢物/蛋白质 -0.646 0.548
克里斯滕森菌科R-7群 C17orf78 肠道菌群 0.152 0.079
    循环代谢物/蛋白质 0.071 0.089
[1]
GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the global burden of disease study 2021[J]. Lancet, 2023, 402(10397): 203-234. DOI: 10.1016/S0140-6736(23)01301-6.
[2]
Feldman EL, Callaghan BC, Pop-Busui R, et al. Diabetic neuropathy[J]. Nat Rev Dis Primers, 2019, 5(1): 42. DOI: 10.1038/s41572-019-0097-9.
[3]
Callaghan BC, Cheng HT, Stables CL, et al. Diabetic neuropathy: clinical manifestations and current treatments[J]. Lancet Neurol, 2012, 11(6): 521-534. DOI: 10.1016/s1474-4422(12)70065-0.
[4]
Gomaa EZ. Human gut microbiota/microbiome in health and diseases: a review[J]. Antonie Van Leeuwenhoek, 2020, 113(12): 2019-2040. DOI: 10.1007/s10482-020-01474-7.
[5]
Wang Y, Ye X, Ding D, et al. Characteristics of the intestinal flora in patients with peripheral neuropathy associated with type 2 diabetes[J]. J Int Med Res, 2020, 48(9): 300060520936806. DOI: 10.1177/0300060520936806.
[6]
Guo K, Figueroa-Romero C, Noureldein M, et al. Gut microbiota in a mouse model of obesity and peripheral neuropathy associated with plasma and nerve lipidomics and nerve transcriptomics[J]. Microbiome, 2023, 11(1): 52. DOI: 10.1186/s40168-022-01436-3.
[7]
Sabico S, Al-Mashharawi A, Al-Daghri NM, et al. Effects of a 6-month multi-strain probiotics supplementation in endotoxemic, inflammatory and cardiometabolic status of T2DM patients: a randomized, double-blind, placebo-controlled trial[J]. Clin Nutr, 2019, 38(4): 1561-1569. DOI: 10.1016/j.clnu.2018.08.009.
[8]
Yang J, Yang X, Wu G, et al. Gut microbiota modulate distal symmetric polyneuropathy in patients with diabetes[J]. Cell Metab, 2023, 35(9): 1548-1562.e7. DOI: 10.1016/j.cmet.2023.06.010.
[9]
Gupta V, Walia GK, Sachdeva MP. 'Mendelian randomization': an approach for exploring causal relations in epidemiology[J]. Public Health, 2017, 145: 113-119. DOI: 10.1016/j.puhe.2016.12.033.
[10]
Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians[J]. BMJ, 2018, 362: k601. DOI: 10.1136/bmj.k601.
[11]
Sanna S, van Zuydam NR, Mahajan A, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases[J]. Nat Genet, 2019, 51(4): 600-605. DOI: 10.1038/s41588-019-0350-x.
[12]
Glymour MM, Tchetgen Tchetgen EJ, Robins JM. Credible Mendelian randomization studies: approaches for evaluating the instrumental variable assumptions[J]. Am J Epidemiol, 2012, 175(4): 332-339. DOI: 10.1093/aje/kwr323.
[13]
Shin SY, Fauman EB, Petersen AK, et al. An atlas of genetic influences on human blood metabolites[J]. Nat Genet, 2014, 46(6): 543-550. DOI: 10.1038/ng.2982.
[14]
Sun BB, Maranville JC, Peters JE, et al. Genomic atlas of the human plasma proteome[J]. Nature, 2018, 558(7708): 73-79. DOI: 10.1038/s41586-018-0175-2.
[15]
Yang J, Yan B, Zhao B, et al. Assessing the causal effects of human serum metabolites on 5 major psychiatric disorders[J]. Schizophr Bull, 2020, 46(4): 804-813. DOI: 10.1093/schbul/sbz138.
[16]
Baird SJ. Exploring linkage disequilibrium[J]. Mol Ecol Resour, 2015, 15(5): 1017-1019. DOI: 10.1111/1755-0998.12424.
[17]
Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants[J]. Int J Epidemiol, 2011, 40(3): 740-752. DOI: 10.1093/ije/dyq151.
[18]
Verbanck M, Chen CY, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases[J]. Nat Genet, 2018, 50(5): 693-698. DOI: 10.1038/s41588-018-0099-7.
[19]
Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data[J]. Genet Epidemiol, 2013, 37(7): 658-665. DOI: 10.1002/gepi.21758.
[20]
Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method[J]. Eur J Epidemiol, 2017, 32(5): 377-389. DOI: 10.1007/s10654-017-0255-x.
[21]
Bowden J, Davey Smith G, Haycock PC, et al. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator[J]. Genet Epidemiol, 2016, 40(4): 304-314. DOI: 10.1002/gepi.21965.
[22]
Lutz SM, Voorhies K, Wu AC, et al. The influence of unmeasured confounding on the MR Steiger approach[J]. Genet Epidemiol, 2022, 46(2): 139-141. DOI: 10.1002/gepi.22442.
[23]
Jie J, Gong Y, Hu H, et al. The role of cerebrospinal fluid metabolites in mediating the impact of lipids on Late-Onset Alzheimer's disease: a two-step Mendelian randomization analysis[J]. J Transl Med, 2024, 22(1): 1077. DOI: 10.1186/s12967-024-05796-2.
[24]
Hoaglin DC. Misunderstandings about Q and "Cochran's Q test" in meta-analysis[J]. Stat Med, 2016, 35(4): 485-495. DOI: 10.1002/sim.6632.
[25]
Burgess S, Thompson SG. Bias in causal estimates from Mendelian randomization studies with weak instruments[J]. Stat Med, 2011, 30(11): 1312-1323. DOI: 10.1002/sim.4197.
[26]
Burgess S, Thompson SG. Erratum to: Interpreting findings from Mendelian randomization using the Mr-Egger method[J]. Eur J Epidemiol, 2017, 32(5): 391-392. DOI: 10.1007/s10654-017-0276-5.
[27]
Mukherjee A, Lordan C, Ross RP, et al. Gut microbes from the phylogenetically diverse genus eubacterium and their various contributions to gut health[J]. Gut Microbes, 2020, 12(1): 1802866. DOI: 10.1080/19490976.2020.1802866.
[28]
Coppola S, Avagliano C, Calignano A, et al. The protective role of butyrate against obesity and obesity-related diseases[J]. Molecules, 2021, 26(3): 682. DOI: 10.3390/molecules26030682.
[29]
den Besten G, van Eunen K, Groen AK, et al. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism[J]. J Lipid Res, 2013, 54(9): 2325-2340. DOI: 10.1194/jlr.R036012.
[30]
Peters SG, Pomare EW, Fisher CA. Portal and peripheral blood short chain fatty acid concentrations after caecal lactulose instillation at surgery[J]. Gut, 1992, 33(9): 1249-1252. DOI: 10.1136/gut.33.9.1249.
[31]
Kien CL, Peltier CP, Mandal S, et al. Effects of the in vivo supply of butyrate on histone acetylation of cecum in piglets[J]. JPEN J Parenter Enteral Nutr, 2008, 32(1): 51-56. DOI: 10.1177/014860710803200151.
[32]
Stilling RM, van de Wouw M, Clarke G, et al. The neuropharmacology of butyrate: the bread and butter of the microbiota-gut-brain axis?[J]. Neurochem Int, 2016, 99: 110-132. DOI: 10.1016/j.neuint.2016.06.011.
[33]
Lal S, Kirkup AJ, Brunsden AM, et al. Vagal afferent responses to fatty acids of different chain length in the rat[J]. Am J Physiol Gastrointest Liver Physiol, 2001, 281(4): G907-G915. DOI: 10.1152/ajpgi.2001.281.4.G907.
[34]
Vieira EL, Leonel AJ, Sad AP, et al. Oral administration of sodium butyrate attenuates inflammation and mucosal lesion in experimental acute ulcerative colitis[J]. J Nutr Biochem, 2012, 23(5): 430-436. DOI: 10.1016/j.jnutbio.2011.01.007.
[35]
Chen PS, Wang CC, Bortner CD, et al. Valproic acid and other histone deacetylase inhibitors induce microglial apoptosis and attenuate lipopolysaccharide-induced dopaminergic neurotoxicity[J]. Neuroscience, 2007, 149(1): 203-212. DOI: 10.1016/j.neuroscience.2007.06.053.
[36]
Furusawa Y, Obata Y, Fukuda S, et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells[J]. Nature, 2013, 504(7480): 446-450. DOI: 10.1038/nature12721.
[37]
Cheng H, Xu M, Zou J, et al. Mechanisms of matrix metalloproteinase 16 in development and cancer[J]. Gene, 2025, 965: 149657. DOI: 10.1016/j.gene.2025.149657.
[38]
Xia H, Qi Y, Ng SS, et al. microRNA-146b inhibits glioma cell migration and invasion by targeting MMPs[J]. Brain Res, 2009, 1269: 158-165. DOI: 10.1016/j.brainres.2009.02.037.
[39]
Bar-Or A, Nuttall RK, Duddy M, et al. Analyses of all matrix metalloproteinase members in leukocytes emphasize monocytes as major inflammatory mediators in multiple sclerosis[J]. Brain, 2003, 126(Pt 12): 2738-2749. DOI: 10.1093/brain/awg285.
[40]
Kawamura T, Andoh A, Nishida A, et al. Inhibitory effects of short-chain fatty acids on matrix metalloproteinase secretion from human colonic subepithelial myofibroblasts[J]. Dig Dis Sci, 2009, 54(2): 238-245. DOI: 10.1007/s10620-008-0348-1.
[41]
Cardinale CJ, Chang X, Wei Z, et al. Genome-wide association study of the age of onset of type 1 diabetes reveals HTATIP2 as a novel T cell regulator[J]. Front Immunol, 2023, 14: 1101488. DOI: 10.3389/fimmu.2023.1101488.
[42]
Liao BM, Raddatz K, Zhong L, et al. Proteomic analysis of livers from fat-fed mice deficient in either PKCδ or PKCε identifies Htatip2 as a regulator of lipid metabolism[J]. Proteomics, 2014, 14(21-22): 2578-2587. DOI: 10.1002/pmic.201400202.
[43]
Nakahara J, Aiso S, Suzuki N. Factors that retard remyelination in multiple sclerosis with a focus on TIP30: a novel therapeutic target[J]. Expert Opin Ther Targets, 2009, 13(12): 1375-1386. DOI: 10.1517/14728220903307491.
[44]
Zhao J, Chen J, Lu B, et al. TIP30 induces apoptosis under oxidative stress through stabilization of p53 messenger RNA in human hepatocellular carcinoma[J]. Cancer Res, 2008, 68(11): 4133-4141. DOI: 10.1158/0008-5472.Can-08-0432.
[45]
Kong G, Huang Z, Ji W, et al. The ketone metabolite β-hydroxybutyrate attenuates oxidative stress in spinal cord injury by suppression of class I histone deacetylases[J]. J Neurotrauma, 2017, 34(18): 2645-2655. DOI: 10.1089/neu.2017.5192.
[46]
Sun Y, Zhang S, Nie Q, et al. Gut firmicutes: Relationship with dietary fiber and role in host homeostasis[J]. Crit Rev Food Sci Nutr, 2023, 63(33): 12073-12088. DOI: 10.1080/10408398.2022.2098249.
[47]
Traini M, Quinn CM, Sandoval C, et al. Sphingomyelin phosphodiesterase acid-like 3A (SMPDL3A) is a novel nucleotide phosphodiesterase regulated by cholesterol in human macrophages[J]. J Biol Chem, 2014, 289(47): 32895-32913. DOI: 10.1074/jbc.M114.612341.
[48]
Pirscoveanu DFV, Olaru DG, Hermann DM, et al. Immune genes involved in synaptic plasticity during early postnatal brain development contribute to post-stroke damage in the aging male rat brain[J]. Biogerontology, 2025, 26(2): 60. DOI: 10.1007/s10522-025-10203-4.
[49]
Wu X, Ma C, Han L, et al. Molecular characterisation of the faecal microbiota in patients with type II diabetes[J]. Curr Microbiol, 2010, 61(1): 69-78. DOI: 10.1007/s00284-010-9582-9.
[50]
Qin J, Li Y, Cai Z, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes[J]. Nature, 2012, 490(7418): 55-60. DOI: 10.1038/nature11450.
[51]
Wei J, Zhao Y, Zhou C, et al. Dietary polysaccharide from enteromorpha clathrata attenuates obesity and increases the intestinal abundance of butyrate-producing bacterium, eubacterium xylanophilum, in mice fed a high-fat diet[J]. Polymers (Basel), 2021, 13(19): 3286. DOI: 10.3390/polym13193286.
[1] 高宇, 郑薇, 李丽丽, 钟威, 魏乾伟, 李一辰. 基于CiteSpace的我国儿童肠道菌群领域研究热点及趋势的文献计量学分析[J/OL]. 中华妇幼临床医学杂志(电子版), 2026, 22(03): 239-249.
[2] 冉金玲, 冯卓慧, 赵智. 孕妇低剂量抗菌药物应用对新生儿肠道菌群影响的研究现状[J/OL]. 中华妇幼临床医学杂志(电子版), 2026, 22(02): 103-108.
[3] 王加中, 曹罡, 刘阳, 陈硕, 李昊煜, 朱绵军, 郑博. 自身免疫性疾病与甲状旁腺功能亢进症的因果关联：孟德尔随机化研究[J/OL]. 中华普通外科学文献(电子版), 2026, 20(01): 35-40.
[4] 汤乘, 瞿根义, 阳光, 徐勇, 江伟民. 基于GWAS数据库的2型糖尿病与勃起功能障碍两样本孟德尔随机化分析[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2026, 20(01): 71-76.
[5] 薛伟伟, 杜凯豪, 汪占金, 东小鸽, 何洁洁, 罗兰明慧, 蒋威, 王展. 遗传学视角孟德尔随机化分析东亚人群肠道菌群与原发性肝癌因果关系[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(01): 79-88.
[6] 张瑞豪, 李文星. 肠道微生物组在结直肠癌化疗中的双向调控作用[J/OL]. 中华结直肠疾病电子杂志, 2026, 15(03): 269-273.
[7] 王珍. 基于"肠-肾"轴探讨"内热致癥"病机在糖尿病肾病中的应用[J/OL]. 中华肾病研究电子杂志, 2026, 15(01): 60-60.
[8] 张玉坤, 田磊, 接英, 保佳玉, 白芸, 王娟, 邵琦妍, 刘晶, 吴彬阁. 中重度干眼患者肠道菌群特征及其与眼表稳态相关性的临床研究[J/OL]. 中华眼科医学杂志(电子版), 2026, 16(03): 153-159.
[9] 李冠奇, 汤嘉俊, 刘杰灵, 王方敏, 丁质钰, 彭毅, 王卫国, 苗惊雷, 陈世杰, 李劲松. 通过蛋白质性状位点分析揭示弥漫性特发性骨肥厚的潜在治疗靶点[J/OL]. 中华老年骨科与康复电子杂志, 2025, 11(06): 345-350.
[10] 谭金梅, 吴珊. 偏头痛与癫痫：一项两样本孟德尔随机化研究[J/OL]. 中华脑科疾病与康复杂志(电子版), 2026, 16(02): 71-76.
[11] 孙薇, 李昱儒, 郭永真, 赵楠. 糖尿病肾病患者微炎症状态和肠道菌群特征与胃肠功能障碍的关系[J/OL]. 中华消化病与影像杂志(电子版), 2025, 15(06): 594-598.
[12] 张峥祥, 高龙, 李文. 柴胡疏肝散对胃食管反流病患者的效果及细胞因子、肠道菌群水平的影响[J/OL]. 中华消化病与影像杂志(电子版), 2025, 15(06): 590-593.
[13] 程华中, 吴沭城, 岳苏苏, 管丽, 戴俊, 袁芳, 宗原, 李硕, 怀德, 王海旭. 血细胞与扁桃体和腺样体相关慢性疾病因果关系的孟德尔随机化分析[J/OL]. 中华临床医师杂志(电子版), 2026, 20(01): 49-57.
[14] 杨彪, 魏新锁, 张雅淳, 王瑶. 组织蛋白酶与类风湿关节炎因果关系的双向孟德尔随机化研究[J/OL]. 中华临床医师杂志(电子版), 2025, 19(09): 689-695.
[15] 万孟夏, 杨毅, 申珅, 甘景环, 张拥波. 空气污染与脑卒中亚型的因果关联：孟德尔随机化分析[J/OL]. 中华脑血管病杂志(电子版), 2026, 20(03): 266-273.
阅读次数
全文


摘要

版权所有 中华医学会 中华医学电子音像出版社有限责任公司  京ICP备14006079号-1  网络出版服务许可证：(署)网出证(京)字第075号

AI
小
编
AI小编
你好！我是《中华医学电子期刊资源库》AI小编，有什么可以帮您的吗？