Abstract
Type 1 diabetes, particularly with childhood onset, is associated with altered neurocognitive traits, yet the underlying biological mechanisms are unclear. Here, we integrate genome-wide association results with single-cell epigenomic profiles and show that type 1 diabetes heritability is enriched in accessible chromatin of human brain-resident cells, most notably microglia, across neurodevelopment into adulthood. Bonferroni-corrected cross-trait genetic correlation analyses reveal negative correlations of type 1 diabetes with intelligence, executive function, and bipolar disorder, and a positive correlation with myasthenia gravis. Conjunctional false discovery rate analysis identifies pleiotropic loci jointly influencing type 1 diabetes and neurocognitive traits, including the 17q21.31 neurogenomic hub. Mendelian randomization further demonstrates protective effects of educational attainment, intelligence, Alzheimer’s disease, and bipolar disorder on type 1 diabetes risk, whereas liability to multiple sclerosis and myasthenia gravis increases type 1 diabetes risk. In the reverse direction, liability to type 1 diabetes is associated with increased risk of myasthenia gravis. We identify several gene expression regulatory variants in brain and immune cells that jointly influence type 1 diabetes and neurocognitive traits, some of which show concordant differential expression in disease-affected versus control tissue. Together, these findings highlight pleiotropic genetic and neuroimmune mechanisms that link type 1 diabetes with cognition and neuropsychiatric disease risk.
Similar content being viewed by others
Data availability
Genome-wide association study (GWAS) summary statistics used in this study are publicly available from the original consortia and repositories listed in Supplementary Table 1, with accession links and/or PMIDs provided. Single-cell ATAC-seq and RNA-seq data are available from the referenced studies as indicated in the Methods. Processed data generated in this work, including results from LDSC, MiXeR, conjunctional false discovery rate, SMR/HEIDI, and Mendelian randomization analyses, are provided in the Supplementary Tables. Source data are provided with this paper.
Code availability
Code for this study is available at GitHub (https://github.com/AlagsLabTeam/T1D-NEURO) and archived at Zenodo82.
References
Gregory, G.A. et al. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. Lancet Diabetes Endocrinol. https://doi.org/10.1016/s2213-8587(22)00218-2 (2022)
Arffman, M. et al. Long-term and recent trends in survival and life expectancy for people with type 1 diabetes in Finland. Diabetes Res. Clin. Pract. 198, 110580 (2023).
Hex, N. et al. Estimating the current and future costs of Type 1 and Type 2 diabetes in the UK, including direct health costs and indirect societal and productivity costs. Diabet. Med. 29, 855–862 (2012).
Shalimova, A. et al. Cognitive dysfunction in type 1 diabetes mellitus. J. Clin. Endocrinol. Metab. 104, 2239–2249 (2019).
Naguib, J. M. et al. Neuro-cognitive performance in children with type 1 diabetes–a meta-analysis. J. Pediatr. Psychol. 34, 271–282 (2009).
Gaudieri, P. A. et al. Cognitive function in children with type 1 diabetes: a meta-analysis. Diab. Care 31, 1892–1897 (2008).
Cato, A. & Hershey, T. Cognition and type 1 diabetes in children and adolescents. Diab. Spectr. 29, 197–202 (2016).
Mauras, N. et al. Impact of type 1 diabetes in the developing brain in children: a longitudinal study. Diab. Care 44, 983–992 (2021).
Liu, S. et al. Educational outcomes in children and adolescents with type 1 diabetes and psychiatric disorders. JAMA Netw. Open 6, e238135 (2023).
Butwicka, A. et al. Risks of psychiatric disorders and suicide attempts in children and adolescents with type 1 diabetes: a population-based cohort study. Diab. Care 38, 453–459 (2015).
Dybdal, D. et al. Increasing risk of psychiatric morbidity after childhood onset type 1 diabetes: a population-based cohort study. Diabetologia 61, 831–838 (2018).
Dolatshahi, M. et al. Central nervous system microstructural alterations in Type 1 diabetes mellitus: a systematic review of diffusion Tensor imaging studies. Diab. Res. Clin. Pract. 205, 110645 (2023).
Hansen, T. M. et al. Neuropathic phenotypes of type 1 diabetes are related to different signatures of magnetic resonance spectroscopy-assessed brain metabolites. Clin. Neurophysiol. 166, 11–19 (2024).
Stanisławska-Kubiak, M. et al. Brain functional and structural changes in diabetic children. How can intellectual development be optimized in type 1 diabetes? Ther. Adv. Chronic Dis. 15, 20406223241229855 (2024).
Habes, M. et al. Patterns of regional brain atrophy and brain aging in middle- and older-aged adults with type 1 diabetes. JAMA Netw. Open 6, e2316182 (2023).
Jacobson, A. M. et al. Brain structure among middle-aged and older adults with long-standing type 1 diabetes in the DCCT/EDIC study. Diab. Care 45, 1779–1787 (2022).
Benton, M. et al. Prevalence of mental disorders in people living with type 1 diabetes: A systematic literature review and meta-analysis. Gen. Hosp. Psychiatry 80, 1–16 (2023).
Chen, M. H. et al. Type 1 diabetes mellitus and risks of major psychiatric disorders: a nationwide population-based cohort study. Diab. Metab. 48, 101319 (2022).
Formánek, T. et al. Childhood-onset type 1 diabetes and subsequent adult psychiatric disorders: a nationwide cohort and genome-wide Mendelian randomization study. Nat. Mental Health https://doi.org/10.1038/s44220-024-00280-8 (2024)
Trief, P. M. et al. Longitudinal changes in depression symptoms and glycemia in adults with type 1 diabetes. Diab. Care 42, 1194–1201 (2019).
Jaser, S. S. & Jordan, L. C. Brain health in children with type 1 diabetes: risk and protective factors. Curr. Diab Rep. 21, 12 (2021).
Sildorf, S. M. et al. Poor metabolic control in children and adolescents with type 1 diabetes and psychiatric comorbidity. Diab. Care 41, 2289–2296 (2018).
Liu, S. et al. Neurodevelopmental disorders, glycemic control, and diabetic complications in type 1 diabetes: a nationwide cohort study. J. Clin. Endocrinol. Metab. 106, e4459–e4470 (2021).
Giedd, J. N. et al. Puberty-related influences on brain development. Mol. Cell. Endocrinol. 254-255, 154–162 (2006).
Luna, B. & Sweeney, J. A. Studies of brain and cognitive maturation through childhood and adolescence: a strategy for testing neurodevelopmental hypotheses. Schizophr. Bull. 27, 443–455 (2001).
Goyal, M. S. & Raichle, M. E. Glucose requirements of the developing human brain. J. Pediatr. Gastroenterol. Nutr. 66, S46–s49 (2018). Suppl 3.
Vannucci, R. C. & Vannucci, S. J. Glucose metabolism in the developing brain. Semin. Perinatol. 24, 107–115 (2000).
Cerolsaletti, K. et al. Genetics coming of age in type 1 diabetes. Diab. Care 42, 189–191 (2019).
Bray, N.J. & O’Donovan, M.C. The genetics of neuropsychiatric disorders. Brain Neurosci. Adv. 2 https://doi.org/10.1177/2398212818799271 (2019)
Świątek, Ł et al. The effect of insulin on the central nervous system: insights into neurological complications and management of type 1 diabetes in the pediatric population. Pediatr. Endocrinol. Diab. Metab. 31, 68–74 (2025).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Chiou, J. et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature 594, 398–402 (2021).
Velmeshev, D. et al. Single-cell analysis of prenatal and postnatal human cortical development. Science 382, eadf0834 (2023).
Hujoel, M. L. A. et al. Disease heritability enrichment of regulatory elements is concentrated in elements with ancient sequence age and conserved function across species. Am. J. Hum. Genet. 104, 611–624 (2019).
Wainschtein, P. et al. Estimation and mapping of the missing heritability of human phenotypes. Nature https://doi.org/10.1038/s41586-025-09720-6 (2025).
Yu, F. et al. Variant to function mapping at single-cell resolution through network propagation. Nat. Biotechnol. 40, 1644–1653 (2022).
Wang, L. et al. Molecular and cellular dynamics of the developing human neocortex. Nature https://doi.org/10.1038/s41586-024-08351-7 (2025)
Andreassen, O. A. et al. Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate. PLoS Genet. 9, e1003455 (2013).
Cheng, W. et al. Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood. Mol. Psychiatry 27, 5167–5176 (2022).
Koolen, D. A. et al. Mutations in the chromatin modifier gene KANSL1 cause the 17q21.31 microdeletion syndrome. Nat. Genet. 44, 639–641 (2012).
de Jong, S. et al. Common inversion polymorphism at 17q21.31 affects expression of multiple genes in tissue-specific manner. BMC Genomics 13, 458 (2012).
Harerimana, N. V. et al. The influence of 17q21.31 and APOE genetic ancestry on neurodegenerative disease risk. Front. Aging Neurosci. 14, 1021918 (2022).
Koolen, D. A. et al. The Koolen-de Vries syndrome: a phenotypic comparison of patients with a 17q21.31 microdeletion versus a KANSL1 sequence variant. Eur. J. Hum. Genet. 24, 652–659 (2016).
Yang, J. et al. Investigation of the causal association between Parkinson’s disease and autoimmune disorders: a bidirectional Mendelian randomization study. Front. Immunol. 15, 1370831 (2024).
Mai, A.S. et al. Association between Type 1 diabetes mellitus and Parkinson’s disease: a Mendelian randomization study. J. Clin. Med. 13, https://doi.org/10.3390/jcm13020561 (2024)
Zhu, B. et al. Single-cell transcriptomic and proteomic analysis of Parkinson’s disease brains. Sci. Transl. Med. 16, eabo1997 (2024).
Tansey, M. G. et al. Inflammation and immune dysfunction in Parkinson disease. Nat. Rev. Immunol. 22, 657–673 (2022).
Charles, A. The pathophysiology of migraine: implications for clinical management. Lancet Neurol. 17, 174–182 (2018).
Gilhus, N. E. Myasthenia gravis. N. Engl. J. Med. 375, 2570–2581 (2016).
Beers, D. R. & Appel, S. H. Immune dysregulation in amyotrophic lateral sclerosis: mechanisms and emerging therapies. Lancet Neurol. 18, 211–220 (2019).
Duan, J. et al. From schizophrenia genetics to disease biology: harnessing new concepts and technologies. J. Psychiatry Brain Sci. 4, e190014 (2019).
Alloza, I. et al. ANKRD55 and DHCR7 are novel multiple sclerosis risk loci. Genes Immun. 13, 253–257 (2012).
Ugidos, N. et al. Interactome of the autoimmune risk protein ANKRD55. Front. Immunol. 10, 2067 (2019).
Ni, G. et al. Estimation of genetic correlation via linkage disequilibrium score regression and genomic restricted maximum likelihood. Am. J. Hum. Genet. 102, 1185–1194 (2018).
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
Lorincz-Comi, N. et al. MRBEE: a bias-corrected multivariable Mendelian randomization method. HGG Adv. 5, 100290 (2024).
Lindbohm, J. V. et al. Immune system-wide Mendelian randomization and triangulation analyses support autoimmunity as a modifiable component in dementia-causing diseases. Nat. Aging 2, 956–972 (2022).
Bellenguez, C. et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 54, 412–436 (2022).
Mullins, N. et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat. Genet. 53, 817–829 (2021).
Shi, S. et al. A Genomics England haplotype reference panel and imputation of UK Biobank. Nat. Genet. 56, 1800–1803 (2024).
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Frei, O. et al. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nat. Commun. 10, 2417 (2019).
Bahrami, S. et al. Genetic loci shared between major depression and intelligence with mixed directions of effect. Nat. Hum. Behav. 5, 795–801 (2021).
Watanabe, K. et al. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
Andreassen, O. A. et al. Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors. Am. J. Hum. Genet. 92, 197–209 (2013).
Smeland, O. B. et al. Discovery of shared genomic loci using the conditional false discovery rate approach. Hum. Genet. 139, 85–94 (2020).
Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Ghoussaini, M. et al. Open targets genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Res. 49, D1311–d1320 (2021).
Bowden, J. & Holmes, M. V. Meta-analysis and Mendelian randomization: a review. Res. Synth. Methods 10, 486–496 (2019).
Bowden, J. et al. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).
Bowden, J. et al. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).
Verbanck, M. et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).
Burgess, S. et al. A review of instrumental variable estimators for Mendelian randomization. Stat. Methods Med. Res. 26, 2333–2355 (2017).
Burgess, S. & Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. 32, 377–389 (2017).
Skrivankova, V. W. et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ 375, n2233 (2021).
Battle, A. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
Bryois, J. et al. Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders. Nat. Neurosci. 25, 1104–1112 (2022).
Yazar, S. et al. Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease. Science 376, eabf3041 (2022).
Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414.e1324 (2016).
Wu, Y. et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 9, 918 (2018).
Genomes Project, C. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Alagpulinsa, D. A. et al. Code for: Shared genetic and neuroimmune architecture links type 1 diabetes with neurocognitive traits. Zenodo https://doi.org/10.5281/zenodo.18793533 (2026).
Acknowledgements
This study was supported by Breakthrough T1D grants to DAA (Grant Keys: 5-CDA-2025-1682-S-B and SRA-2024-1472-S-B).
Author information
Authors and Affiliations
Contributions
P.S. and Z.A.S. contributed to the conception and initiation of the project. P.S., Z.A.S., Z.X., Y.D., and B.Z. performed data analyses. D.A.A. conceived, designed, and supervised the study and drafted the manuscript. All authors (P.S., Z.A.S., Z.X., Y.D., A.J., M.S., S.R., B.Z., L.Z., A.T.D., S.A., and D.A.A.) contributed to data interpretation, manuscript review, and approval of the final draft.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks Andrew Grotzinger and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Saarah, P., Syeda, Z.A., Xu, Z. et al. Shared genetic and neuroimmune architecture links type 1 diabetes with neurocognitive traits. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70694-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-026-70694-8


