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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....

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Autores principales: Aoki, Rei, Saito, Takeo, Ninomiya, Kohei, Shimasaki, Ayu, Ashizawa, Takuma, Ito, Kenta, Ikeda, Masashi, Iwata, Nakao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons Australia, Ltd 2022
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.
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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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