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Genomic insights into the comorbidity between type 2 diabetes and schizophrenia
Multimorbidity represents an increasingly important public health challenge with far-reaching implications for health management and policy. Mental health and metabolic diseases have a well-established epidemiological association. In this study, we investigate the genetic intersection between type 2...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615007/ https://www.ncbi.nlm.nih.gov/pubmed/37905000 http://dx.doi.org/10.1101/2023.10.16.23297073 |
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author | Arruda, Ana Luiza Khandaker, Golam M. Morris, Andrew P. Smith, George Davey Huckins, Laura M. Zeggini, Eleftheria |
author_facet | Arruda, Ana Luiza Khandaker, Golam M. Morris, Andrew P. Smith, George Davey Huckins, Laura M. Zeggini, Eleftheria |
author_sort | Arruda, Ana Luiza |
collection | PubMed |
description | Multimorbidity represents an increasingly important public health challenge with far-reaching implications for health management and policy. Mental health and metabolic diseases have a well-established epidemiological association. In this study, we investigate the genetic intersection between type 2 diabetes and schizophrenia. We use Mendelian randomization to examine potential causal relationships between the two conditions and related endophenotypes. We report no compelling evidence that type 2 diabetes genetic liability potentially causally influences schizophrenia risk and vice versa. Our findings show that increased body mass index (BMI) has a protective effect against schizophrenia, in contrast to the well-known risk-increasing effect of BMI on type 2 diabetes risk. We identify evidence of colocalization of association signals for these two conditions at 11 genomic loci, six of which have opposing directions of effect for type 2 diabetes and schizophrenia. To elucidate these colocalizing signals, we integrate multi-omics data from bulk and single-cell gene expression studies, along with functional information. We identify high-confidence effector genes and find that they are enriched for homeostasis and lipid-related pathways. We also highlight drug repurposing opportunities including N-methyl-D-aspartate (NMDA) receptor antagonists. Our findings provide insights into shared biological mechanisms for type 2 diabetes and schizophrenia, highlighting common factors that influence the risk of the two conditions in opposite directions and shedding light on the complex nature of this comorbidity. |
format | Online Article Text |
id | pubmed-10615007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-106150072023-10-31 Genomic insights into the comorbidity between type 2 diabetes and schizophrenia Arruda, Ana Luiza Khandaker, Golam M. Morris, Andrew P. Smith, George Davey Huckins, Laura M. Zeggini, Eleftheria medRxiv Article Multimorbidity represents an increasingly important public health challenge with far-reaching implications for health management and policy. Mental health and metabolic diseases have a well-established epidemiological association. In this study, we investigate the genetic intersection between type 2 diabetes and schizophrenia. We use Mendelian randomization to examine potential causal relationships between the two conditions and related endophenotypes. We report no compelling evidence that type 2 diabetes genetic liability potentially causally influences schizophrenia risk and vice versa. Our findings show that increased body mass index (BMI) has a protective effect against schizophrenia, in contrast to the well-known risk-increasing effect of BMI on type 2 diabetes risk. We identify evidence of colocalization of association signals for these two conditions at 11 genomic loci, six of which have opposing directions of effect for type 2 diabetes and schizophrenia. To elucidate these colocalizing signals, we integrate multi-omics data from bulk and single-cell gene expression studies, along with functional information. We identify high-confidence effector genes and find that they are enriched for homeostasis and lipid-related pathways. We also highlight drug repurposing opportunities including N-methyl-D-aspartate (NMDA) receptor antagonists. Our findings provide insights into shared biological mechanisms for type 2 diabetes and schizophrenia, highlighting common factors that influence the risk of the two conditions in opposite directions and shedding light on the complex nature of this comorbidity. Cold Spring Harbor Laboratory 2023-10-16 /pmc/articles/PMC10615007/ /pubmed/37905000 http://dx.doi.org/10.1101/2023.10.16.23297073 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Arruda, Ana Luiza Khandaker, Golam M. Morris, Andrew P. Smith, George Davey Huckins, Laura M. Zeggini, Eleftheria Genomic insights into the comorbidity between type 2 diabetes and schizophrenia |
title | Genomic insights into the comorbidity between type 2 diabetes and schizophrenia |
title_full | Genomic insights into the comorbidity between type 2 diabetes and schizophrenia |
title_fullStr | Genomic insights into the comorbidity between type 2 diabetes and schizophrenia |
title_full_unstemmed | Genomic insights into the comorbidity between type 2 diabetes and schizophrenia |
title_short | Genomic insights into the comorbidity between type 2 diabetes and schizophrenia |
title_sort | genomic insights into the comorbidity between type 2 diabetes and schizophrenia |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615007/ https://www.ncbi.nlm.nih.gov/pubmed/37905000 http://dx.doi.org/10.1101/2023.10.16.23297073 |
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