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Binge eating disorder (BED) is the most common eating disorder, yet its genetic architecture remains largely unknown. Studying BED is challenging because it is often comorbid with obesity, a common and highly polygenic trait, and it is underdiagnosed in biobank data sets. To address this limitation, we apply a supervised machine-learning approach (using 822 cases of individuals diagnosed with BED) to estimate the probability of each individual having BED based on electronic medical records from the Million Veteran Program. We perform a genome-wide association study of individuals of African (n = 77,574) and European (n = 285,138) ancestry while controlling for body mass index to identify three independent loci near the HFE, MCHR2 and LRP11 genes and suggest APOE as a risk gene for BED. We identify shared heritability between BED and several neuropsychiatric traits, and implicate iron metabolism in the pathophysiology of BED. Overall, our findings provide insights into the genetics underlying BED and suggest directions for future translational research.
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BED GWAS summary statistics from the MVP data are available on dbGaP (accession no. phs001672). For the external validation sets for the partitioned heritability analysis, we used open chromatin regions from a murine erythroid cell beta-estradiol stimulation model (GEO accession no. GSE114996), open chromatin atlas of adult human brains (GEO accession no. GSE147672) and open chromatin atlas of developing human organs (https://descartes.brotmanbaty.org/bbi/human-chromatin-during-development). For GWAS summary statistics for genetic correlation analyses, see the IEU Open GWAS Project (https://gwas.mrcieu.ac.uk).
Software used in this study included the following programs: EIGENSOFT v.6 (https://github.com/dreichlab/eig); FUMA v.1.3.7 (https://fuma.ctglab.nl); GCTA v.1.93.2 (https://yanglab.westlake.edu.cn/software/gcta/#Overview); KING v.2.0 (https://www.chen.kingrelatedness.com); LD score regression v.1.0.1 (https://github.com/bulik/ldsc); liftOver v.1.2.0 (https://genome.ucsc.edu/cgi-bin/hgLiftOver); Minimac v.3 (https://genome.sph.umich.edu/wiki/Minimac3); Multi-ancestry meta-analysis (https://github.com/JonJala/mama); PheMED (https://github.com/DiseaseNeuroGenomics/PheMED); PRS-CS (https://github.com/getian107/PRScs); SuSiE as implemented in echolocatoR72 (https://github.com/RajLabMSSM/echolocatoR).
Mitchell, K. S. et al. Binge eating disorder: a symptom-level investigation of genetic and environmental influences on liability. Psychol. Med. 40, 1899–1906 (2010).
Article CAS PubMed PubMed Central Google Scholar
Reichborn-Kjennerud, T., Bulik, C. M., Tambs, K. & Harris, J. R. Genetic and environmental influences on binge eating in the absence of compensatory behaviors: a population-based twin study. Int. J. Eat. Disord. 36, 307–314 (2004).
Article PubMed Google Scholar
Udo, T. & Grilo, C. M. Prevalence and correlates of DSM-5-defined eating disorders in a nationally representative sample of U.S. adults. Biol. Psychiatry 84, 345–354 (2018).
Article PubMed PubMed Central Google Scholar
Brownley, K. A. et al. Binge-eating disorder in adults: a systematic review and meta-analysis. Ann. Intern. Med. 165, 409–420 (2016).
Article PubMed PubMed Central Google Scholar
Wonderlich, S. A., Gordon, K. H., Mitchell, J. E., Crosby, R. D. & Engel, S. G. The validity and clinical utility of binge eating disorder. Int. J. Eat. Disord. 42, 687–705 (2009).
Article PubMed Google Scholar
Bulik, C. M. et al. The binge eating genetics initiative (BEGIN): study protocol. BMC Psychiatry 20, 307 (2020).
Article PubMed PubMed Central Google Scholar
Javaras, K. N. et al. Co-occurrence of binge eating disorder with psychiatric and medical disorders. J. Clin. Psychiatry 69, 266–273 (2008).
Article PubMed Google Scholar
Javaras, K. N. et al. Familiality and heritability of binge eating disorder: results of a case-control family study and a twin study. Int. J. Eat. Disord. 41, 174–179 (2008).
Article PubMed Google Scholar
Hübel, C. et al. One size does not fit all. Genomics differentiates among anorexia nervosa, bulimia nervosa, and binge-eating disorder. Int. J. Eat. Disord. 54, 785–793 (2021).
Article PubMed PubMed Central Google Scholar
Guss, J. L., Kissileff, H. R., Devlin, M. J., Zimmerli, E. & Walsh, B. T. Binge size increases with body mass index in women with binge-eating disorder. Obes. Res. 10, 1021–1029 (2002).
Article PubMed Google Scholar
Anderson, D. A., Williamson, D. A., Johnson, W. G. & Grieve, C. O. Validity of test meals for determining binge eating. Eat. Behav. 2, 105–112 (2001).
Article CAS PubMed Google Scholar
Kenardy, J. et al. Disordered eating behaviours in women with type 2 diabetes mellitus. Eat. Behav. 2, 183–192 (2001).
Article CAS PubMed Google Scholar
Hudson, J. I. et al. Longitudinal study of the diagnosis of components of the metabolic syndrome in individuals with binge-eating disorder. Am. J. Clin. Nutr. 91, 1568–1573 (2010).
Article CAS PubMed PubMed Central Google Scholar
Hilbert, A. et al. Meta-analysis on the long-term effectiveness of psychological and medical treatments for binge-eating disorder. Int. J. Eat. Disord. 53, 1353–1376 (2020).
Article PubMed Google Scholar
Peat, C. M. et al. Comparative effectiveness of treatments for binge-eating disorder: systematic review and network meta-analysis. Eur. Eat. Disord. Rev. 25, 317–328 (2017).
Article PubMed Google Scholar
Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).
Article PubMed Google Scholar
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
Article PubMed PubMed Central Google Scholar
Volkow, N. D. et al. The conception of the ABCD study: from substance use to a broad NIH collaboration. Dev. Cogn. Neurosci. 32, 4–7 (2018).
Article PubMed Google Scholar
Satterthwaite, T. D. et al. The Philadelphia Neurodevelopmental Cohort: a publicly available resource for the study of normal and abnormal brain development in youth. Neuroimage 124, 1115–1119 (2016).
Article PubMed Google Scholar
Ollier, W., Sprosen, T. & Peakman, T. UK Biobank: from concept to reality. Pharmacogenomics 6, 639–646 (2005).
Article PubMed Google Scholar
Diagnostic and Statistical Manual of Mental Disorders (DSM-5) 5th edn (American Psychiatric Association Publishing, 2013).
Kessler, R. C. et al. The prevalence and correlates of binge eating disorder in the World Health Organization World Mental Health Surveys. Biol. Psychiatry 73, 904–914 (2013).
Article PubMed PubMed Central Google Scholar
Sonneville, K. R. & Lipson, S. K. Disparities in eating disorder diagnosis and treatment according to weight status, race/ethnicity, socioeconomic background, and sex among college students. Int. J. Eat. Disord. 51, 518–526 (2018).
Article CAS PubMed Google Scholar
Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).
Article CAS PubMed PubMed Central Google Scholar
Polimanti, R. et al. Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium. Mol. Psychiatry 25, 1673–1687 (2020).
Article PubMed PubMed Central Google Scholar
Bulik-Sullivan, B. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Article CAS PubMed PubMed Central Google Scholar
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Article CAS PubMed PubMed Central Google Scholar
Turley, P. et al. Multi-ancestry meta-analysis yields novel genetic discoveries and ancestry-specific associations. Preprint at bioRxiv https://doi.org/10.1101/2021.04.23.441003 (2021).
Zou, Y., Carbonetto, P., Wang, G. & Stephens, M. Fine-mapping from summary data with the ‘Sum of Single Effects’ model. PLoS Genet. 18, e1010299 (2022).
Article CAS PubMed PubMed Central Google Scholar
Burstein, D. et al. Detecting and adjusting for hidden biases due to phenotype misclassification in genome-wide association studies. Preprint at medRxiv https://doi.org/10.1101/2023.01.17.23284670 (2023).
Genovese, C. R., Roeder, K. & Wasserman, L. False discovery control with p-value weighting. Biometrika 93, 509–524 (2006).
Article Google Scholar
Karlsson Linnér, R. et al. Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. Nat. Neurosci. 24, 1367–1376 (2021).
Article PubMed Google Scholar
Williams, C. et al. Guidelines for evaluating the comparability of down-sampled GWAS summary statistics. Preprint at bioRxiv https://doi.org/10.1101/2023.03.21.533641 (2023).
Elsworth, B. et al. The MRC IEU OpenGWAS data infrastructure. Preprint at bioRxiv https://doi.org/10.1101/2020.08.10.244293 (2020).
Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
Article PubMed PubMed Central Google Scholar
Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).
Article PubMed PubMed Central Google Scholar
Bell, S. et al. A genome-wide meta-analysis yields 46 new loci associating with biomarkers of iron homeostasis. Commun. Biol. 4, 156 (2021).
Article CAS PubMed PubMed Central Google Scholar
Tanimura, N. et al. GATA/heme multi-omics reveals a trace metal-dependent cellular differentiation mechanism. Dev. Cell 46, 581–594.e4 (2018).
Article CAS PubMed PubMed Central Google Scholar
Domcke, S. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020).
Article CAS PubMed PubMed Central Google Scholar
Corces, M. R. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat. Genet. 52, 1158–1168 (2020).
Article CAS PubMed PubMed Central Google Scholar
An, S. J., Kim, T. J. & Yoon, B.-W. Epidemiology, risk factors, and clinical features of intracerebral hemorrhage: an update. J. Stroke 19, 3–10 (2017).
Article PubMed PubMed Central Google Scholar
Stunkard, A. J. & Allison, K. C. Binge eating disorder: disorder or marker? Int. J. Eat. Disord. 34 (Suppl.), S107–S116 (2003).
Article PubMed Google Scholar
Hinckley, J. D. et al. Quantitative trait locus linkage analysis in a large Amish pedigree identifies novel candidate loci for erythrocyte traits. Mol. Genet. Genom. Med. 1, 131–141 (2013).
Article Google Scholar
Galmozzi, A. et al. PGRMC2 is an intracellular haem chaperone critical for adipocyte function. Nature 576, 138–142 (2019).
Article CAS PubMed PubMed Central Google Scholar
Borgna-Pignatti, C. & Zanella, S. Pica as a manifestation of iron deficiency. Expert Rev. Hematol. 9, 1075–1080 (2016).
Article CAS PubMed Google Scholar
Ersche, K. D. et al. Disrupted iron regulation in the brain and periphery in cocaine addiction. Transl. Psychiatry 7, e1040 (2017).
Article CAS PubMed PubMed Central Google Scholar
Barnea, R. et al. Trait and state binge eating predispose towards cocaine craving. Addict. Biol. 22, 163–171 (2017).
Article CAS PubMed Google Scholar
Succurro, E. et al. Obese patients with a binge eating disorder have an unfavorable metabolic and inflammatory profile. Medicine 94, e2098 (2015).
Article CAS PubMed PubMed Central Google Scholar
Al-Massadi, O. et al. Multifaceted actions of melanin-concentrating hormone on mammalian energy homeostasis. Nat. Rev. Endocrinol. 17, 745–755 (2021).
Article CAS PubMed Google Scholar
Noble, E. E. et al. Hypothalamus–hippocampus circuitry regulates impulsivity via melanin-concentrating hormone. Nat. Commun. 10, 4923 (2019).
Article PubMed PubMed Central Google Scholar
Harrington, K. M. et al. Gender differences in demographic and health characteristics of the Million Veteran Program cohort. Women’s Health Issues 29 (Suppl. 1), S56–S66 (2019).
Article PubMed PubMed Central Google Scholar
Gelernter, J. et al. Genome-wide association study of post-traumatic stress disorder reexperiencing symptoms in >165,000 US veterans. Nat. Neurosci. 22, 1394–1401 (2019).
Article CAS PubMed PubMed Central Google Scholar
Fang, H. et al. Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. Am. J. Hum. Genet. 105, 763–772 (2019).
Article CAS PubMed PubMed Central Google Scholar
1000 Genomes Project Consortium An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
Article Google Scholar
Karcher, N. R. & Barch, D. M. The ABCD study: understanding the development of risk for mental and physical health outcomes. Neuropsychopharmacology 46, 131–142 (2021).
Article PubMed Google Scholar
Wu, P. et al. Mapping ICD-10 and ICD-10-CM codes to phecodes: workflow development and initial evaluation. JMIR Med. Inform. 7, e14325 (2019).
Article PubMed PubMed Central Google Scholar
Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).
Article CAS PubMed PubMed Central Google Scholar
1000 Genomes Project Consortium A global reference for human genetic variation. Nature 526, 68–74 (2015).
Article Google Scholar
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).
Article CAS PubMed PubMed Central Google Scholar
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
Article CAS PubMed Google Scholar
Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).
Article PubMed PubMed Central Google Scholar
Bigdeli, T. B. et al. A simple yet accurate correction for winner’s curse can predict signals discovered in much larger genome scans. Bioinformatics 32, 2598–2603 (2016).
Article CAS PubMed PubMed Central Google Scholar
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
Article PubMed PubMed Central Google Scholar
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
Article CAS PubMed PubMed Central Google Scholar
Wang, G., Sarkar, A., Carbonetto, P. & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. B 82, 1273–1300 (2020).
Article Google Scholar
Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1110 (2013).
Article CAS PubMed PubMed Central Google Scholar
Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).
Article PubMed PubMed Central Google Scholar
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Article CAS PubMed PubMed Central Google Scholar
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
Article CAS PubMed PubMed Central Google Scholar
Churchhouse, C. & Neale, B. Rapid GWAS of Thousands of Phenotypes for 337,000 Samples in the UK Biobank http://www.nealelab.is/blog/2017/7/19/rapid-gwas-of-thousands-of-phenotypes-for-337000-samples-in-the-uk-biobank (Biobank, 2017).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Article CAS PubMed PubMed Central Google Scholar
Schilder, B. M., Humphrey, J. & Raj, T. echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline. Bioinformatics 38, 536–539 (2021).
Article PubMed Central Google Scholar
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This research is based on data from the MVP, Office of Research and Development, Veterans Health Administration, and was supported by award no. MVP006. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. This study was also supported by the National Institutes of Health (NIH), Bethesda, Maryland, USA, under award numbers T32MH087004 (K.T.), T32MH096679 (T.C.G.), T32MH122394 (A.M.), K08MH122911 (G.V.), R01MH125246, R01AG067025, U01MH116442 and R01MH109677 (P.R.), and by the Veterans Affairs Merit grants BX002395 and BX004189 (P.R.). This study was also funded in part by the Brain & Behavior Research Foundation through the 2020 NARSAD Young Investigator Grant no. 29350 (G.V.). We thank S. W. Choi and P. F. O’Reilly for their guidance and expertise in using data from the UKBB. We thank the participants in the UKBB and the scientists involved in the construction of this resource. This research was conducted using the UKBB resource under application 18177 (P. F. O’Reilly). This work was supported in part by the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai, New York, New York, USA. Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the National Institute of Mental Health Data Archive (NDA), Bethesda, Maryland, USA. This is a multi-site, longitudinal study designed to recruit more than 10,000 children aged 9–10 years and follow them over 10 years into early adulthood. The ABCD Study is supported by the NIH and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093 and U01DA041025. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/scientists/workgroups. ABCD consortium investigators designed and implemented the study and/or provided data but did not participate in the analysis or writing of this report. The manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from https://doi.org/10.15154/1527728. DOIs can be found at https://nda.nih.gov/study.html?id=1661. Support for data collection for the PNC, acquired through dbGaP (accession no. phs000607.v3.p2), was provided by grant RC2MH089983 awarded to R. Gur and RC2MH089924 awarded to H. Hakonarson. Participants were recruited and genotyped through the Center for Applied Genomics (CAG) at the Children’s Hospital in Philadelphia (CHOP), Pennsylvania, USA. Phenotypic data collection occurred at the CAG and CHOP and at the Brain Behavior Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Trevor C. Griffen
Present address: Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
These authors contributed equally: David Burstein, Trevor C. Griffen.
These authors jointly supervised this work: Georgios Voloudakis, Panos Roussos.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
David Burstein, Trevor C. Griffen, Karen Therrien, Jaroslav Bendl, Sanan Venkatesh, Pengfei Dong, Amirhossein Modabbernia, Biao Zeng, Deepika Mathur, Gabriel Hoffman, Robyn Sysko, Tom Hildebrandt, Georgios Voloudakis & Panos Roussos
Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
David Burstein, Karen Therrien, Jaroslav Bendl, Sanan Venkatesh, Pengfei Dong, Biao Zeng, Deepika Mathur, Gabriel Hoffman, Georgios Voloudakis & Panos Roussos
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
David Burstein, Karen Therrien, Jaroslav Bendl, Sanan Venkatesh, Pengfei Dong, Biao Zeng, Deepika Mathur, Gabriel Hoffman, Georgios Voloudakis & Panos Roussos
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
David Burstein, Karen Therrien, Jaroslav Bendl, Sanan Venkatesh, Pengfei Dong, Biao Zeng, Deepika Mathur, Gabriel Hoffman, Georgios Voloudakis & Panos Roussos
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
David Burstein, Karen Therrien, Jaroslav Bendl, Sanan Venkatesh, Pengfei Dong, Biao Zeng, Deepika Mathur, Gabriel Hoffman, Georgios Voloudakis & Panos Roussos
Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
David Burstein, Karen Therrien, Jaroslav Bendl, Sanan Venkatesh, Pengfei Dong, Biao Zeng, Deepika Mathur, Gabriel Hoffman, Georgios Voloudakis & Panos Roussos
Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, New York, NY, USA
David Burstein, Karen Therrien, Sanan Venkatesh & Georgios Voloudakis
Center of Excellence in Eating and Weight Disorders, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Trevor C. Griffen, Robyn Sysko & Tom Hildebrandt
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D.B., K.T., J.B., S.V., P.D., B.Z. and D.M. performed the analysis. D.B., K.T., J.B. and S.V. performed sample and/or data provision and processing. D.B. and T.C.G. wrote the manuscript. D.B., T.C.G., K.T., J.B., S.V., A.M., G.H., R.S., T.H., G.V. and P.R. performed core revision of the manuscript. T.C.G., G.H., R.S., T.H., G.V. and P.R. provided study direction. G.H., R.S., T.H., G.V. and P.R. supervised the study. All authors contributed to critical revision of the manuscript.
Correspondence to Georgios Voloudakis or Panos Roussos.
T.H. is a scientific advisory board member of Noom. T.H. and R.S. receive funding from and have equity in Noom (a non-publicly traded company). R.S. receives royalties from Wolters Kluwer Health. The remaining authors declare no competing interests.
Nature Genetics thanks Eske Derks, Adam Locke and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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Burstein, D., Griffen, T.C., Therrien, K. et al. Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism. Nat Genet (2023). https://doi.org/10.1038/s41588-023-01464-1
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Received: 06 May 2022
Accepted: 29 June 2023
Published: 07 August 2023
DOI: https://doi.org/10.1038/s41588-023-01464-1
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