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Shared genetic components between metabolic syndrome and schizophrenia: Genetic correlation using multipopulation data sets
AIM: The genetic relationship between schizophrenia (SCZ) and other nonpsychiatric disorders remains largely unknown. We examined the shared genetic components between these disorders based on multipopulation data sets. METHODS: We used two data sets for East Asian (EAS) and European (EUR) samples....
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons Australia, Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546074/ https://www.ncbi.nlm.nih.gov/pubmed/35536160 http://dx.doi.org/10.1111/pcn.13372 |
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author | Aoki, Rei Saito, Takeo Ninomiya, Kohei Shimasaki, Ayu Ashizawa, Takuma Ito, Kenta Ikeda, Masashi Iwata, Nakao |
author_facet | Aoki, Rei Saito, Takeo Ninomiya, Kohei Shimasaki, Ayu Ashizawa, Takuma Ito, Kenta Ikeda, Masashi Iwata, Nakao |
author_sort | Aoki, Rei |
collection | PubMed |
description | AIM: The genetic relationship between schizophrenia (SCZ) and other nonpsychiatric disorders remains largely unknown. We examined the shared genetic components between these disorders based on multipopulation data sets. METHODS: We used two data sets for East Asian (EAS) and European (EUR) samples. SCZ data was based on the Psychiatric Genomics Consortium Asia with our own genome‐wide association study for EAS and Psychiatric Genomics Consortium for EUR. Nonpsychiatric data (20 binary traits [mainly nonpsychiatric complex disorders] and 34 quantitative traits [mainly laboratory examinations and physical characteristics]) were obtained from Biobank Japan and UK Biobank for EAS and EUR samples, respectively. To evaluate genetic correlation, linkage disequilibrium score regression analysis was utilized with further meta‐analysis for each result from EAS and EUR samples to obtain robust evidence. Subsequent mendelian randomization analysis was also included to examine the causal effect. RESULTS: A significant genetic correlation between SCZ and several metabolic syndrome (MetS) traits was detected in the combined samples (meta‐analysis between EAS and EUR data) (body mass index [r(g) = −0.10, q‐value = 1.0 × 10(−9)], high‐density‐lipoprotein cholesterol [r(g) = 0.072, q‐value = 2.9 × 10(−3)], blood sugar [r(g) = −0.068, q‐value = 1.4 × 10(−2)], triglycerides [r(g) = −0.052, q‐value = 2.4 × 10(−2)], systolic blood pressure [r(g) = −0.054, q‐value = 3.5 × 10(−2)], and C‐reactive protein [r(g) = −0.076, q‐value = 7.8 × 10(−5)]. However, no causal relationship on SCZ susceptibility was detected for these traits based on the mendelian randomization analysis. CONCLUSION: Our results indicate shared genetic components between SCZ and MetS traits and C‐reactive protein. Specifically, we found it interesting that the correlation between MetS traits and SCZ was the opposite of that expected from clinical studies: this genetic study suggests that SCZ susceptibility was associated with reduced MetS. This implied that MetS in patients with SCZ was not associated with genetic components but with environmental factors, including antipsychotics, lifestyle changes, poor diet, lack of exercise, and living conditions. |
format | Online Article Text |
id | pubmed-9546074 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley & Sons Australia, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-95460742022-10-14 Shared genetic components between metabolic syndrome and schizophrenia: Genetic correlation using multipopulation data sets Aoki, Rei Saito, Takeo Ninomiya, Kohei Shimasaki, Ayu Ashizawa, Takuma Ito, Kenta Ikeda, Masashi Iwata, Nakao Psychiatry Clin Neurosci Regular Articles AIM: The genetic relationship between schizophrenia (SCZ) and other nonpsychiatric disorders remains largely unknown. We examined the shared genetic components between these disorders based on multipopulation data sets. METHODS: We used two data sets for East Asian (EAS) and European (EUR) samples. SCZ data was based on the Psychiatric Genomics Consortium Asia with our own genome‐wide association study for EAS and Psychiatric Genomics Consortium for EUR. Nonpsychiatric data (20 binary traits [mainly nonpsychiatric complex disorders] and 34 quantitative traits [mainly laboratory examinations and physical characteristics]) were obtained from Biobank Japan and UK Biobank for EAS and EUR samples, respectively. To evaluate genetic correlation, linkage disequilibrium score regression analysis was utilized with further meta‐analysis for each result from EAS and EUR samples to obtain robust evidence. Subsequent mendelian randomization analysis was also included to examine the causal effect. RESULTS: A significant genetic correlation between SCZ and several metabolic syndrome (MetS) traits was detected in the combined samples (meta‐analysis between EAS and EUR data) (body mass index [r(g) = −0.10, q‐value = 1.0 × 10(−9)], high‐density‐lipoprotein cholesterol [r(g) = 0.072, q‐value = 2.9 × 10(−3)], blood sugar [r(g) = −0.068, q‐value = 1.4 × 10(−2)], triglycerides [r(g) = −0.052, q‐value = 2.4 × 10(−2)], systolic blood pressure [r(g) = −0.054, q‐value = 3.5 × 10(−2)], and C‐reactive protein [r(g) = −0.076, q‐value = 7.8 × 10(−5)]. However, no causal relationship on SCZ susceptibility was detected for these traits based on the mendelian randomization analysis. CONCLUSION: Our results indicate shared genetic components between SCZ and MetS traits and C‐reactive protein. Specifically, we found it interesting that the correlation between MetS traits and SCZ was the opposite of that expected from clinical studies: this genetic study suggests that SCZ susceptibility was associated with reduced MetS. This implied that MetS in patients with SCZ was not associated with genetic components but with environmental factors, including antipsychotics, lifestyle changes, poor diet, lack of exercise, and living conditions. John Wiley & Sons Australia, Ltd 2022-05-30 2022-08 /pmc/articles/PMC9546074/ /pubmed/35536160 http://dx.doi.org/10.1111/pcn.13372 Text en © 2022 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Regular Articles Aoki, Rei Saito, Takeo Ninomiya, Kohei Shimasaki, Ayu Ashizawa, Takuma Ito, Kenta Ikeda, Masashi Iwata, Nakao Shared genetic components between metabolic syndrome and schizophrenia: Genetic correlation using multipopulation data sets |
title | Shared genetic components between metabolic syndrome and schizophrenia: Genetic correlation using multipopulation data sets |
title_full | Shared genetic components between metabolic syndrome and schizophrenia: Genetic correlation using multipopulation data sets |
title_fullStr | Shared genetic components between metabolic syndrome and schizophrenia: Genetic correlation using multipopulation data sets |
title_full_unstemmed | Shared genetic components between metabolic syndrome and schizophrenia: Genetic correlation using multipopulation data sets |
title_short | Shared genetic components between metabolic syndrome and schizophrenia: Genetic correlation using multipopulation data sets |
title_sort | shared genetic components between metabolic syndrome and schizophrenia: genetic correlation using multipopulation data sets |
topic | Regular Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546074/ https://www.ncbi.nlm.nih.gov/pubmed/35536160 http://dx.doi.org/10.1111/pcn.13372 |
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