Variables associated with cognitive function: an exposome-wide and mendelian randomization analysis
Abstract Background Evidence indicates that cognitive function is influenced by potential environmental factors. We aimed to determine the variables influencing cognitive function. Methods Our study included 164,463 non-demented adults (89,644 [54.51%] female; mean [SD] age, 56.69 [8.14] years) from...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | Alzheimer’s Research & Therapy |
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| Online Access: | https://doi.org/10.1186/s13195-025-01670-5 |
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| author | Yong-Li Zhao Yi-Ning Hao Yi-Jun Ge Yi Zhang Lang-Yu Huang Yan Fu Dan-Dan Zhang Ya-Nan Ou Xi-Peng Cao Jian-Feng Feng Wei Cheng Lan Tan Jin-Tai Yu |
| author_facet | Yong-Li Zhao Yi-Ning Hao Yi-Jun Ge Yi Zhang Lang-Yu Huang Yan Fu Dan-Dan Zhang Ya-Nan Ou Xi-Peng Cao Jian-Feng Feng Wei Cheng Lan Tan Jin-Tai Yu |
| author_sort | Yong-Li Zhao |
| collection | DOAJ |
| description | Abstract Background Evidence indicates that cognitive function is influenced by potential environmental factors. We aimed to determine the variables influencing cognitive function. Methods Our study included 164,463 non-demented adults (89,644 [54.51%] female; mean [SD] age, 56.69 [8.14] years) from the UK Biobank who completed four cognitive assessments at baseline. 364 variables were finally extracted for analysis through a rigorous screening process. We performed univariate analyses to identify variables significantly associated with each cognitive function in two equal-sized split discovery and replication datasets. Subsequently, the identified variables in univariate analyses were further assessed in a multivariable model. Additionally, for the variables identified in multivariable model, we explored the associations with longitudinal cognitive decline. Moreover, one- and two- sample Mendelian randomization (MR) analyses were conducted to confirm the genetic associations. Finally, the quality of the pooled evidence for the associations between variables and cognitive function was evaluated. Results 252 variables (69%) exhibited significant associations with at least one cognitive function in the discovery dataset. Of these, 231 (92%) were successfully replicated. Subsequently, our multivariable analyses identified 41 variables that were significantly associated with at least one cognitive function, spanning categories such as education, socioeconomic status, lifestyle factors, body measurements, mental health, medical conditions, early life factors, and household characteristics. Among these 41 variables, 12 were associated with more than one cognitive domain, and were further identified in all subgroup analyses. And LASSO, rigde, and principal component analysis indicated the robustness of the primary results. Moreover, among these 41 variables, 12 were significantly associated with a longitudinal cognitive decline. Furthermore, 22 were supported by one-sample MR analysis, and 5 were further confirmed by two-sample MR analysis. Additionally, the quality of the pooled evidence for the associations between 10 variables and cognitive function was rated as high. Based on these 10 identified variables, adopting a more favorable lifestyle was significantly associated with 38% and 34% decreased risks of dementia and Alzheimer’s disease (AD). Conclusion Overall, our study constructed an evidence database of variables associated with cognitive function, which could contribute to the prevention of cognitive impairment and dementia. |
| format | Article |
| id | doaj-art-5889892953df4e929579eda22d62f516 |
| institution | Kabale University |
| issn | 1758-9193 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Alzheimer’s Research & Therapy |
| spelling | doaj-art-5889892953df4e929579eda22d62f5162025-01-12T12:10:58ZengBMCAlzheimer’s Research & Therapy1758-91932025-01-0117111610.1186/s13195-025-01670-5Variables associated with cognitive function: an exposome-wide and mendelian randomization analysisYong-Li Zhao0Yi-Ning Hao1Yi-Jun Ge2Yi Zhang3Lang-Yu Huang4Yan Fu5Dan-Dan Zhang6Ya-Nan Ou7Xi-Peng Cao8Jian-Feng Feng9Wei Cheng10Lan Tan11Jin-Tai Yu12Department of Neurology, Institute of Neurology, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical College, Huashan Hospital, Fudan UniversityDepartment of Neurology, Institute of Neurology, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical College, Huashan Hospital, Fudan UniversityDepartment of Neurology, Institute of Neurology, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical College, Huashan Hospital, Fudan UniversityDepartment of Neurology, Institute of Neurology, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical College, Huashan Hospital, Fudan UniversityDepartment of Neurology, Qingdao Municipal Hospital, Qingdao UniversityDepartment of Neurology, Qingdao Municipal Hospital, Qingdao UniversityDepartment of Neurology, Qingdao Municipal Hospital, Qingdao UniversityDepartment of Neurology, The Affiliated Hospital of Qingdao UniversityClinical Research Centre, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital)Institute of Science and Technology for Brain-Inspired Intelligence, Fudan UniversityDepartment of Neurology, Institute of Neurology, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical College, Huashan Hospital, Fudan UniversityDepartment of Neurology, Qingdao Municipal Hospital, Qingdao UniversityDepartment of Neurology, Institute of Neurology, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical College, Huashan Hospital, Fudan UniversityAbstract Background Evidence indicates that cognitive function is influenced by potential environmental factors. We aimed to determine the variables influencing cognitive function. Methods Our study included 164,463 non-demented adults (89,644 [54.51%] female; mean [SD] age, 56.69 [8.14] years) from the UK Biobank who completed four cognitive assessments at baseline. 364 variables were finally extracted for analysis through a rigorous screening process. We performed univariate analyses to identify variables significantly associated with each cognitive function in two equal-sized split discovery and replication datasets. Subsequently, the identified variables in univariate analyses were further assessed in a multivariable model. Additionally, for the variables identified in multivariable model, we explored the associations with longitudinal cognitive decline. Moreover, one- and two- sample Mendelian randomization (MR) analyses were conducted to confirm the genetic associations. Finally, the quality of the pooled evidence for the associations between variables and cognitive function was evaluated. Results 252 variables (69%) exhibited significant associations with at least one cognitive function in the discovery dataset. Of these, 231 (92%) were successfully replicated. Subsequently, our multivariable analyses identified 41 variables that were significantly associated with at least one cognitive function, spanning categories such as education, socioeconomic status, lifestyle factors, body measurements, mental health, medical conditions, early life factors, and household characteristics. Among these 41 variables, 12 were associated with more than one cognitive domain, and were further identified in all subgroup analyses. And LASSO, rigde, and principal component analysis indicated the robustness of the primary results. Moreover, among these 41 variables, 12 were significantly associated with a longitudinal cognitive decline. Furthermore, 22 were supported by one-sample MR analysis, and 5 were further confirmed by two-sample MR analysis. Additionally, the quality of the pooled evidence for the associations between 10 variables and cognitive function was rated as high. Based on these 10 identified variables, adopting a more favorable lifestyle was significantly associated with 38% and 34% decreased risks of dementia and Alzheimer’s disease (AD). Conclusion Overall, our study constructed an evidence database of variables associated with cognitive function, which could contribute to the prevention of cognitive impairment and dementia.https://doi.org/10.1186/s13195-025-01670-5Risk factorsCognitive functionAssessmentDementiaEWASMR |
| spellingShingle | Yong-Li Zhao Yi-Ning Hao Yi-Jun Ge Yi Zhang Lang-Yu Huang Yan Fu Dan-Dan Zhang Ya-Nan Ou Xi-Peng Cao Jian-Feng Feng Wei Cheng Lan Tan Jin-Tai Yu Variables associated with cognitive function: an exposome-wide and mendelian randomization analysis Alzheimer’s Research & Therapy Risk factors Cognitive function Assessment Dementia EWAS MR |
| title | Variables associated with cognitive function: an exposome-wide and mendelian randomization analysis |
| title_full | Variables associated with cognitive function: an exposome-wide and mendelian randomization analysis |
| title_fullStr | Variables associated with cognitive function: an exposome-wide and mendelian randomization analysis |
| title_full_unstemmed | Variables associated with cognitive function: an exposome-wide and mendelian randomization analysis |
| title_short | Variables associated with cognitive function: an exposome-wide and mendelian randomization analysis |
| title_sort | variables associated with cognitive function an exposome wide and mendelian randomization analysis |
| topic | Risk factors Cognitive function Assessment Dementia EWAS MR |
| url | https://doi.org/10.1186/s13195-025-01670-5 |
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