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[Preprint]. 2024 Mar 8:2024.03.07.24303921.
doi: 10.1101/2024.03.07.24303921.

Local patterns of genetic sharing challenge the boundaries between neuropsychiatric and insulin resistance-related conditions

Giuseppe Fanelli  1   2   3 , Barbara Franke  1   2   4 , Chiara Fabbri  3 , Josefin Werme  4 , Izel Erdogan  2 , Ward De Witte  2 , Geert Poelmans  5 , I Hyun Ruisch  2 , Lianne Maria Reus  6   7   8 , Veerle van Gils  9 , Willemijn J Jansen  9 , Stephanie J B Vos  9 , Kazi Asraful Alam  10 , Aurora Martinez  10   11 , Jan Haavik  10   12 , Theresa Wimberley  13   14 , Søren Dalsgaard  13   15   16 , Ábel Fóthi  17 , Csaba Barta  17 , Fernando Fernandez-Aranda  18   19   20   21 , Susana Jimenez-Murcia  18   19   20   21   22 , Simone Berkel  23 , Silke Matura  24 , Jordi Salas-Salvadó  25   26   27 , Martina Arenella  2   28 , Alessandro Serretti  29 , Nina Roth Mota  1   2 , Janita Bralten  1   2
Affiliations

Local patterns of genetic sharing challenge the boundaries between neuropsychiatric and insulin resistance-related conditions

Giuseppe Fanelli et al. medRxiv. .

Update in

  • Local patterns of genetic sharing between neuropsychiatric and insulin resistance-related conditions.
    Fanelli G, Franke B, Fabbri C, Werme J, Erdogan I, De Witte W, Poelmans G, Ruisch IH, Reus LM, van Gils V, Jansen WJ, Vos SJB, Alam KA, Martinez A, Haavik J, Wimberley T, Dalsgaard S, Fóthi Á, Barta C, Fernandez-Aranda F, Jimenez-Murcia S, Berkel S, Matura S, Salas-Salvadó J, Arenella M, Serretti A, Mota NR, Bralten J. Fanelli G, et al. Transl Psychiatry. 2025 Apr 12;15(1):145. doi: 10.1038/s41398-025-03349-9. Transl Psychiatry. 2025. PMID: 40221434 Free PMC article.

Abstract

The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg|=0.21-1, pFDR<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer's disease, bipolar disorder, and Tourette's syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.

Keywords: Genetic overlap; cardio-metabolic diseases; comorbidity; diabetes mellitus; functional annotations; local genetic correlation; metabolism; pleiotropy.

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Conflict of interest statement

Declarations of interest AS is or has been a consultant/speaker for Abbott, Abbvie, Angelini, AstraZeneca, Clinical Data, Boehringer, Bristol-Myers Squibb, Eli Lilly, GlaxoSmithKline, Innovapharma, Italfarmaco, Janssen, Lundbeck, Naurex, Pfizer, Polifarma, Sanofi, Servier and Taliaz. BF discloses having received educational speaking fees and travel support from Medice. CF was a speaker for Janssen. GP is director/chief scientific officer and WDW as well as IHR are employees of Drug Target ID, Ltd., but their activities at this company do not constitute competing interests with regard to this paper. JH has received lecture honoraria as part of continuing medical education programs sponsored by Shire, Takeda, Medice, and Biocodex. JSS reported receiving research support through his institution from the Patrimonio Comunal Olivarero, Almond Board of California and Pistachio Growers of California; he is serving on the board of and receiving grant support through his institution from the International Nut and Dried Foundation, Instituto Danone Spain and Institute Danone International. FFA and SJM have been consultants/speakers for NovoNordisk. All other authors report no biomedical financial interests or potential conflicts of interest.

Figures

Figure 1.
Figure 1.. Local genetic correlations between insulin resistance-related conditions and neuropsychiatric disorders.
a. Chord diagram representing the network of local genetic correlations between insulin resistance-related conditions and neuropsychiatric disorders. A higher width of a ribbon reflects a higher number of shared genetically correlated loci between two phenotypes, highlighting a substantial polygenic overlap and suggesting potential shared pathophysiological mechanisms between them. The colours of the ribbons are used purely for visual distinction and do not imply any additional significance or categorisation. b. Bar plot presenting the number of local genetic correlations identified between neuropsychiatric disorders and insulin resistance-related conditions. Each bar corresponds to a different neuropsychiatric disorder, segmented by the direction of effect of local genetic correlations, with blue indicating negative and red indicating positive local genetic correlations between neuropsychiatric disorders and insulin resistance-related conditions. The height of each bar reflects the quantity of local genetic correlations detected for each disorder. c. Network visualisation of local genetic correlations between a spectrum of neuropsychiatric disorders and insulin resistance-related conditions. Nodes represent distinct phenotypes for which local bivariate genetic correlations were evaluated. Edges connecting the nodes vary in width proportionally to the number of local genetic correlations identified between phenotype pairs. Edge colour denotes the direction of the genetic correlation estimate, with red indicating a positive correlation and blue indicating a negative correlation. Abbreviations: AD, Alzheimer’s disease; ADHD, attention-deficit/hyperactivity disorder; AN, anorexia nervosa; ASD, autism spectrum disorder; BD, bipolar disorder; MDD, major depressive disorder; MetS, metabolic syndrome, OCD, obsessive-compulsive disorder; T2DM, type 2 diabetes mellitus; SCZ, schizophrenia; TS, Tourette’s syndrome.
Figure 1.
Figure 1.. Local genetic correlations between insulin resistance-related conditions and neuropsychiatric disorders.
a. Chord diagram representing the network of local genetic correlations between insulin resistance-related conditions and neuropsychiatric disorders. A higher width of a ribbon reflects a higher number of shared genetically correlated loci between two phenotypes, highlighting a substantial polygenic overlap and suggesting potential shared pathophysiological mechanisms between them. The colours of the ribbons are used purely for visual distinction and do not imply any additional significance or categorisation. b. Bar plot presenting the number of local genetic correlations identified between neuropsychiatric disorders and insulin resistance-related conditions. Each bar corresponds to a different neuropsychiatric disorder, segmented by the direction of effect of local genetic correlations, with blue indicating negative and red indicating positive local genetic correlations between neuropsychiatric disorders and insulin resistance-related conditions. The height of each bar reflects the quantity of local genetic correlations detected for each disorder. c. Network visualisation of local genetic correlations between a spectrum of neuropsychiatric disorders and insulin resistance-related conditions. Nodes represent distinct phenotypes for which local bivariate genetic correlations were evaluated. Edges connecting the nodes vary in width proportionally to the number of local genetic correlations identified between phenotype pairs. Edge colour denotes the direction of the genetic correlation estimate, with red indicating a positive correlation and blue indicating a negative correlation. Abbreviations: AD, Alzheimer’s disease; ADHD, attention-deficit/hyperactivity disorder; AN, anorexia nervosa; ASD, autism spectrum disorder; BD, bipolar disorder; MDD, major depressive disorder; MetS, metabolic syndrome, OCD, obsessive-compulsive disorder; T2DM, type 2 diabetes mellitus; SCZ, schizophrenia; TS, Tourette’s syndrome.
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References

    1. Agarwal S.M., Panda R., Costa-Dookhan K.A., MacKenzie N.E., Treen Q.C., Caravaggio F., Hashim E., Leung G., Kirpalani A., Matheson K., Chintoh A.F., Kramer C.K., Voineskos A.N., Graff-Guerrero A., Remington G.J., Hahn M.K., 2021. Metformin for early comorbid glucose dysregulation and schizophrenia spectrum disorders: a pilot double-blind randomized clinical trial. Transl Psychiatry 11, 219. - PMC - PubMed
    1. Almgren P., Lehtovirta M., Isomaa B., Sarelin L., Taskinen M.R., Lyssenko V., Tuomi T., Groop L., Botnia Study G., 2011. Heritability and familiality of type 2 diabetes and related quantitative traits in the Botnia Study. Diabetologia 54, 2811–2819. - PubMed
    1. Arenella M., Fanelli G., Kiemeney L.A., McAlonan G., Murphy D.G., Bralten J., 2023. Genetic relationship between the immune system and autism. Brain Behav Immun Health 34, 100698. - PMC - PubMed
    1. Bagge A., Dahmcke C.M., Dalgaard L.T., 2013. Syntaxin-1a is a direct target of miR-29a in insulin-producing beta-cells. Horm Metab Res 45, 463–466. - PubMed
    1. Balsevich G., Abizaid A., Chen A., Karatsoreos I.N., Schmidt M.V., 2019. Stress and glucocorticoid modulation of feeding and metabolism. Neurobiol Stress 11, 100171. - PMC - PubMed

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