Lei Liu, Ming Zhou, Yuanyuan Zhang, Yang Chen, Huiru Wang, Yuan Cao, Chao Fang, Xiaoju Wan, Xiaochen Wang, Huilan Liu, Peng Wang
{"title":"阿尔茨海默病与脂肪肝相关代谢功能障碍之间的因果关系：来自双向网络孟德尔随机化分析的见解","authors":"Lei Liu, Ming Zhou, Yuanyuan Zhang, Yang Chen, Huiru Wang, Yuan Cao, Chao Fang, Xiaoju Wan, Xiaochen Wang, Huilan Liu, Peng Wang","doi":"10.1007/s11306-024-02193-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction/objectives: </strong>Several observational investigations have observed the possible links between Alzheimer's disease (AD) and metabolic dysfunction associated with fatty liver disease (MAFLD), yet the underlying causal relationships remain undetermined. This study aimed to systemically infer the causal associations between AD and MAFLD by employing a bidirectional network two-sample Mendelian randomization (MR) analysis.</p><p><strong>Methods: </strong>Genome-wide significant (P < 5 × 10<sup>- 8</sup>) genetic variants associated with AD and MAFLD were selected as instrumental variables (IVs) from the consortium of FinnGen, MRC-IEU, UK biobank, and genome-wide association studies (GWAS), respectively. The study sample sizes range from 55,134 to 423,738 for AD and from 218,792 to 778,614 for MAFLD. In the forward analysis, AD was set as the exposure factor, and MAFLD was employed as the disease outcome. Causal relationships between AD and MAFLD were evaluated using inverse-variance weighted (IVW), MR Egger regression, the weighted median, and weighted mode. Additionally, the reverse MR analysis was conducted to infer causality between MAFLD and AD. Sensitivity analyses were performed to assess the robustness of causal estimates.</p><p><strong>Results: </strong>In the forward MR analysis, the genetically determined family history of AD was associated with a lower risk of MAFLD (mother's history: OR<sub>discovery</sub>=0.08, 95%CI: 0.03, 0.22, P = 7.91 × 10<sup>- 7</sup>; OR<sub>replicate</sub>=0.83, 95%CI: 0.74, 0.94, P = 3.68 × 10<sup>- 3</sup>; father's history: OR<sub>discovery</sub>=0.01, 95%CI: 0.01, 0.08, P = 5.48 × 10<sup>- 5</sup>; OR<sub>replicate</sub>=0.79, 95%CI: 0.68, 0.93, P = 4.07 × 10<sup>- 3</sup>; family history: OR<sub>discovery</sub>=0.84, 95%CI: 0.77, 0.91, P = 6.30 × 10<sup>- 5</sup>; OR<sub>replicate</sub>=0.15, 95%CI: 0.05, 0.41, P = 2.51 × 10<sup>- 4</sup>) in the primary MAFLD cohort. Consistent findings were observed in an independent MAFLD cohort (all P < 0.05). However, the reverse MR analysis suggested that genetic susceptibility to MAFLD had no causal effects on developing AD.</p><p><strong>Conclusion: </strong>Our study demonstrates a causal association between a family history of AD and a lower risk of MAFLD. It suggests that individuals with a history of AD may benefit from tailored metabolic assessments to better understand their risk of MAFLD, and inform the development of preventive strategies targeting high-risk populations.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 1","pages":"4"},"PeriodicalIF":3.5000,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Causal relationships between Alzheimer's disease and metabolic dysfunction associated with fatty liver disease: insights from bidirectional network Mendelian Randomization analysis.\",\"authors\":\"Lei Liu, Ming Zhou, Yuanyuan Zhang, Yang Chen, Huiru Wang, Yuan Cao, Chao Fang, Xiaoju Wan, Xiaochen Wang, Huilan Liu, Peng Wang\",\"doi\":\"10.1007/s11306-024-02193-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction/objectives: </strong>Several observational investigations have observed the possible links between Alzheimer's disease (AD) and metabolic dysfunction associated with fatty liver disease (MAFLD), yet the underlying causal relationships remain undetermined. This study aimed to systemically infer the causal associations between AD and MAFLD by employing a bidirectional network two-sample Mendelian randomization (MR) analysis.</p><p><strong>Methods: </strong>Genome-wide significant (P < 5 × 10<sup>- 8</sup>) genetic variants associated with AD and MAFLD were selected as instrumental variables (IVs) from the consortium of FinnGen, MRC-IEU, UK biobank, and genome-wide association studies (GWAS), respectively. The study sample sizes range from 55,134 to 423,738 for AD and from 218,792 to 778,614 for MAFLD. In the forward analysis, AD was set as the exposure factor, and MAFLD was employed as the disease outcome. Causal relationships between AD and MAFLD were evaluated using inverse-variance weighted (IVW), MR Egger regression, the weighted median, and weighted mode. Additionally, the reverse MR analysis was conducted to infer causality between MAFLD and AD. Sensitivity analyses were performed to assess the robustness of causal estimates.</p><p><strong>Results: </strong>In the forward MR analysis, the genetically determined family history of AD was associated with a lower risk of MAFLD (mother's history: OR<sub>discovery</sub>=0.08, 95%CI: 0.03, 0.22, P = 7.91 × 10<sup>- 7</sup>; OR<sub>replicate</sub>=0.83, 95%CI: 0.74, 0.94, P = 3.68 × 10<sup>- 3</sup>; father's history: OR<sub>discovery</sub>=0.01, 95%CI: 0.01, 0.08, P = 5.48 × 10<sup>- 5</sup>; OR<sub>replicate</sub>=0.79, 95%CI: 0.68, 0.93, P = 4.07 × 10<sup>- 3</sup>; family history: OR<sub>discovery</sub>=0.84, 95%CI: 0.77, 0.91, P = 6.30 × 10<sup>- 5</sup>; OR<sub>replicate</sub>=0.15, 95%CI: 0.05, 0.41, P = 2.51 × 10<sup>- 4</sup>) in the primary MAFLD cohort. Consistent findings were observed in an independent MAFLD cohort (all P < 0.05). However, the reverse MR analysis suggested that genetic susceptibility to MAFLD had no causal effects on developing AD.</p><p><strong>Conclusion: </strong>Our study demonstrates a causal association between a family history of AD and a lower risk of MAFLD. It suggests that individuals with a history of AD may benefit from tailored metabolic assessments to better understand their risk of MAFLD, and inform the development of preventive strategies targeting high-risk populations.</p>\",\"PeriodicalId\":18506,\"journal\":{\"name\":\"Metabolomics\",\"volume\":\"21 1\",\"pages\":\"4\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Metabolomics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s11306-024-02193-0\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metabolomics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11306-024-02193-0","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Causal relationships between Alzheimer's disease and metabolic dysfunction associated with fatty liver disease: insights from bidirectional network Mendelian Randomization analysis.
Introduction/objectives: Several observational investigations have observed the possible links between Alzheimer's disease (AD) and metabolic dysfunction associated with fatty liver disease (MAFLD), yet the underlying causal relationships remain undetermined. This study aimed to systemically infer the causal associations between AD and MAFLD by employing a bidirectional network two-sample Mendelian randomization (MR) analysis.
Methods: Genome-wide significant (P < 5 × 10- 8) genetic variants associated with AD and MAFLD were selected as instrumental variables (IVs) from the consortium of FinnGen, MRC-IEU, UK biobank, and genome-wide association studies (GWAS), respectively. The study sample sizes range from 55,134 to 423,738 for AD and from 218,792 to 778,614 for MAFLD. In the forward analysis, AD was set as the exposure factor, and MAFLD was employed as the disease outcome. Causal relationships between AD and MAFLD were evaluated using inverse-variance weighted (IVW), MR Egger regression, the weighted median, and weighted mode. Additionally, the reverse MR analysis was conducted to infer causality between MAFLD and AD. Sensitivity analyses were performed to assess the robustness of causal estimates.
Results: In the forward MR analysis, the genetically determined family history of AD was associated with a lower risk of MAFLD (mother's history: ORdiscovery=0.08, 95%CI: 0.03, 0.22, P = 7.91 × 10- 7; ORreplicate=0.83, 95%CI: 0.74, 0.94, P = 3.68 × 10- 3; father's history: ORdiscovery=0.01, 95%CI: 0.01, 0.08, P = 5.48 × 10- 5; ORreplicate=0.79, 95%CI: 0.68, 0.93, P = 4.07 × 10- 3; family history: ORdiscovery=0.84, 95%CI: 0.77, 0.91, P = 6.30 × 10- 5; ORreplicate=0.15, 95%CI: 0.05, 0.41, P = 2.51 × 10- 4) in the primary MAFLD cohort. Consistent findings were observed in an independent MAFLD cohort (all P < 0.05). However, the reverse MR analysis suggested that genetic susceptibility to MAFLD had no causal effects on developing AD.
Conclusion: Our study demonstrates a causal association between a family history of AD and a lower risk of MAFLD. It suggests that individuals with a history of AD may benefit from tailored metabolic assessments to better understand their risk of MAFLD, and inform the development of preventive strategies targeting high-risk populations.
期刊介绍：
Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to:
metabolomic applications within man, including pre-clinical and clinical
pharmacometabolomics for precision medicine
metabolic profiling and fingerprinting
metabolite target analysis
metabolomic applications within animals, plants and microbes
transcriptomics and proteomics in systems biology
Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.
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