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Assessing a multivariate model of brain-mediated genetic influences on disordered eating in the ABCD cohort

Nature Mental Health volume 1, pages 573–585 (2023)Cite this article

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Abstract

Eating disorders often emerge during adolescence, and affected individuals frequently demonstrate high rates of psychiatric comorbidity, particularly with depressive and anxiety disorders. Although risk for eating disorders reflects both genetic and neurobiological factors, knowledge of how genetic risk for eating disorders relates to neurobiology and psychiatric symptoms during critical developmental periods remains limited. Here we simultaneously estimated associations between genetic risk, brain structure, and eating-disorder-related psychopathology symptoms in over 4,900 adolescents of European ancestry from the ABCD study (mean age (s.d.) = 9.94 (0.62) years). Polygenic scores for anorexia nervosa (AN PGS) and body mass index (BMI PGS) were related to three morphometric brain features—cortical thickness, surface area, and subcortical gray matter volume—and to latent psychopathology factors using structural equation modeling. We identified a three-factor structure of eating-disorder-related psychopathology symptoms: eating, distress, and fear factors. Increased BMI PGS were uniquely associated with greater eating factor scores. Moreover, greater BMI PGS predicted widespread increases in cortical thickness and reductions in surface area while AN PGS were related to reduced caudate volume. Altered default mode and visual network thickness was associated with greater eating factor scores, whereas distress and fear factor scores reflected a shared reduction in somatomotor network thickness. Our novel findings indicate that greater genetic risk for high BMI and altered cortical thickness of canonical brain networks underpin eating disorder symptomatology in early adolescence. As neurobiological factors appear to shape disordered eating earlier in development than previously thought, these results underscore the need for early detection and intervention efforts for eating disorders.

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Fig. 1: Simplified path diagram of multivariate model of gene–brain–behavior associations.
Fig. 2: Genetic risk for high BMI is uniquely related to ED psychopathology scores.
Fig. 3: Associations between BMI PGS, CT and psychopathology.
Fig. 4: Associations between BMI PGS, SA, and psychopathology.
Fig. 5: Genetic risk for AN is related to reduced caudate size.

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Data availability

Data from the 1000 Genomes Project Phase 3 may be accessed from https://www.internationalgenome.org/data. All other data used for these analyses were obtained from the ABCD curated annual release 2.0 (https://doi.org/10.15154/1503209) and release 2.0.1 (https://nda.nih.gov/study.html?id=721). Access to ABCD study data is restricted to protect participants’ privacy. Users must create an account through the National Institute of Mental Health Data Archive and they may then complete the necessary steps to gain access.

Code availability

Scripts for the genetics analyses are publicly available and may be retrieved from https://github.com/vwarrier/ABCD_geneticQC. Scripts for MRI processing may be downloaded from https://zenodo.org/record/8051799. Structural equation model analysis scripts are publicly available on the Open Science Framework: https://osf.io/hru7e/?view_only=e5e81549659d4ef68e0abb0db12dbe5c.

References

  1. Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 2013).

  2. 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 

  3. Stice, E., Marti, C. N. & Rohde, P. Prevalence, incidence, impairment, and course of the proposed DSM-5 eating disorder diagnoses in an 8-year prospective community study of young women. J. Abnorm. Psychol. 122, 445–457 (2013).

    Article  PubMed  Google Scholar 

  4. Mustelin, L. et al. The DSM-5 diagnostic criteria for anorexia nervosa may change its population prevalence and prognostic value. J. Psychiatr. Res. 77, 85–91 (2016).

    Article  PubMed  Google Scholar 

  5. Favaro, A., Caregaro, L., Tenconi, E., Bosello, R. & Santonastaso, P. Time trends in age at onset of anorexia nervosa and bulimia nervosa. J. Clin. Psychiatry 70, 1715–1721 (2009).

    Article  PubMed  Google Scholar 

  6. 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 

  7. Eddy, K. T. et al. Recovery from anorexia nervosa and bulimia nervosa at 22-year follow-up. J. Clin. Psychiatry 78, 184–189 (2016).

    Article  Google Scholar 

  8. Yilmaz, Z., Hardaway, A. & Bulik, C. Genetics and epigenetics of eating disorders. Adv. Genomics Genet. 5, 131–150 (2015).

    PubMed  PubMed Central  Google Scholar 

  9. Watson, H. J. et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat. Genet. 51, 1207–1214 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Grasby, K. L. et al. The genetic architecture of the human cerebral cortex. Science 367, eaay6690 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Makowski, C. et al. Discovery of genomic loci of the human cerebral cortex using genetically informed brain atlases. Science 375, 522–528 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Warrier, V. et al. The genetics of cortical organisation and development: a study of 2,347 neuroimaging phenotypes. Preprint at bioRxiv https://doi.org/10.1101/2022.09.08.507084 (2022).

  14. Leehr, E. J. et al. Evidence for a sex-specific contribution of polygenic load for anorexia nervosa to body weight and prefrontal brain structure in nonclinical individuals. Neuropsychopharmacology 44, 2212–2219 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Opel, N. et al. Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders: evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group. Mol. Psychiatry 26, 4839–4852 (2021).

    Article  PubMed  Google Scholar 

  16. Marín, O. Developmental timing and critical windows for the treatment of psychiatric disorders. Nat. Med. 22, 1229–1238 (2016).

    Article  PubMed  Google Scholar 

  17. Kessler, R. C., Chiu, W. T., Demler, O. & Walters, E. E. Prevalence, severity, and comorbidity of 12-Month DSM-IV disorders in the national comorbidity survey replication. Arch. Gen. Psychiatry 62, 617 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Paus, T., Keshavan, M. & Giedd, J. N. Why do many psychiatric disorders emerge during adolescence? Nat. Rev. Neurosci. 9, 947–957 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Mills, K. L., Goddings, A.-L., Clasen, L. S., Giedd, J. N. & Blakemore, S.-J. The developmental mismatch in structural brain maturation during adolescence. Dev. Neurosci. 36, 147–160 (2014).

    Article  PubMed  Google Scholar 

  20. Shaw, P. et al. Neurodevelopmental trajectories of the human cerebral cortex. J. Neurosci. 28, 3586–3594 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Bethlehem, R. A. I. et al. Brain charts for the human lifespan. Nature 604, 525–533 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hensch, T. K. Critical period regulation. Annu. Rev. Neurosci. 27, 549–579 (2004).

    Article  PubMed  Google Scholar 

  23. Knudsen, E. I. Sensitive periods in the development of the brain and behavior. J. Cogn. Neurosci. 16, 1412–1425 (2004).

    Article  PubMed  Google Scholar 

  24. Shaw, P. et al. Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proc. Natl Acad. Sci. USA 104, 19649–19654 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Whittle, S. et al. Structural brain development and depression onset during adolescence: a prospective longitudinal study. Am. J. Psychiatry 171, 564–571 (2014).

    Article  PubMed  Google Scholar 

  26. Bernardoni, F. et al. Weight restoration therapy rapidly reverses cortical thinning in anorexia nervosa: a longitudinal study. Neuroimage 130, 214–222 (2016).

    Article  PubMed  Google Scholar 

  27. Bernardoni, F. et al. Nutritional status affects cortical folding: lessons learned from anorexia nervosa. Biol. Psychiatry 84, 692–701 (2018).

    Article  PubMed  Google Scholar 

  28. Cyr, M. et al. Reduced inferior and orbital frontal thickness in adolescent bulimia nervosa persists over two-year follow-up. J. Am. Acad. Child Adolesc. Psychiatry 56, 866–874.e7 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Kendler, K. S., Gardner, C. O. & Lichtenstein, P. A developmental twin study of symptoms of anxiety and depression: evidence for genetic innovation and attenuation. Psychol. Med. 38, 1567–1575 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Kendler, K. S. et al. A longitudinal twin study of fears from middle childhood to early adulthood: evidence for a developmentally dynamic genome. Arch. Gen. Psychiatry 65, 421–429 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Waszczuk, M. A., Zavos, H. M. S., Gregory, A. M. & Eley, T. C. The stability and change of etiological influences on depression, anxiety symptoms and their co-occurrence across adolescence and young adulthood. Psychol. Med. 46, 161–175 (2016).

    Article  PubMed  Google Scholar 

  32. Udo, T. & Grilo, C. M. Psychiatric and medical correlates of DSM-5 eating disorders in a nationally representative sample of adults in the United States. Int. J. Eat. Disord. 52, 42–50 (2019).

    Article  PubMed  Google Scholar 

  33. Ulfvebrand, S., Birgegård, A., Norring, C., Högdahl, L. & von Hausswolff-Juhlin, Y. Psychiatric comorbidity in women and men with eating disorders results from a large clinical database. Psychiatry Res. 230, 294–299 (2015).

    Article  PubMed  Google Scholar 

  34. Tozzi, F. et al. Symptom fluctuation in eating disorders: correlates of diagnostic crossover. Am. J. Psychiatry 162, 732–740 (2005).

    Article  PubMed  Google Scholar 

  35. Van Son, G. E., Van Hoeken, D., Van Furth, E. F., Donker, G. A. & Hoek, H. W. Course and outcome of eating disorders in a primary care-based cohort. Int. J. Eat. Disord. 43, 130–138 (2010).

    PubMed  Google Scholar 

  36. Stice, E. Interactive and mediational etiologic models of eating disorder onset: evidence from prospective studies. Annu. Rev. Clin. Psychol. 12, 359–381 (2016).

    Article  PubMed  Google Scholar 

  37. Schaumberg, K. et al. Anxiety disorder symptoms at age 10 predict eating disorder symptoms and diagnoses in adolescence. J. Child Psychol. Psychiatry Allied Discip. 60, 686–696 (2019).

    Article  Google Scholar 

  38. Cederlöf, M. et al. Etiological overlap between obsessive-compulsive disorder and anorexia nervosa: a longitudinal cohort, multigenerational family and twin study. World Psychiatry 14, 333–338 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Yilmaz, Z. et al. Predicting eating disorder and anxiety symptoms using disorder-specific and transdiagnostic polygenic scores for anorexia nervosa and obsessive-compulsive disorder. Psychol. Med. https://doi.org/10.1017/S0033291721005079 (2022).

  40. Abdulkadir, M. et al. Polygenic score for body mass index is associated with disordered eating in a general population cohort. J. Clin. Med. 9, 1187 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Robinson, L. et al. Association of genetic and phenotypic assessments with onset of disordered eating behaviors and comorbid mental health problems among adolescents. JAMA Netw. Open 3, e2026874 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ~700 000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Kaufman, J., Townsend, L. D. & Kobak, K. The computerized kiddie schedule for affective disorders and schizophrenia (KSADS): development and administration guidelines. J. Am. Acad. Child Adolesc. Psychiatry 56, S357 (2017).

    Article  Google Scholar 

  44. Yilmaz, Z., Gottfredson, N. C., Zerwas, S. C., Bulik, C. M. & Micali, N. Developmental premorbid body mass index trajectories of adolescents with eating disorders in a longitudinal population cohort. J. Am. Acad. Child Adolesc. Psychiatry 58, 191–199 (2019).

    Article  PubMed  Google Scholar 

  45. Waszczuk, M. A. et al. General v. specific vulnerabilities: polygenic risk scores and higher-order psychopathology dimensions in the Adolescent Brain Cognitive Development (ABCD) Study. Psychol. Med. https://doi.org/10.1017/S0033291721003639 (2021).

  46. Martins-Silva, T. et al. Obesity and ADHD: exploring the role of body composition, BMI polygenic risk score, and reward system genes. J. Psychiatr. Res. 136, 529–536 (2021).

    Article  PubMed  Google Scholar 

  47. Kappelmann, N. et al. Polygenic risk for immuno-metabolic markers and specific depressive symptoms: a multi-sample network analysis study. Brain. Behav. Immun. 95, 256–268 (2021).

    Article  PubMed  Google Scholar 

  48. Akingbuwa, W. A. et al. Genetic associations between childhood psychopathology and adult depression and associated traits in 42 998 individuals. JAMA Psychiatry 77, 715 (2020).

    Article  PubMed  Google Scholar 

  49. Wainberg, M., Jacobs, G. R., Voineskos, A. N. & Tripathy, S. J. Neurobiological, familial and genetic risk factors for dimensional psychopathology in the Adolescent Brain Cognitive Development study. Mol. Psychiatry 27, 2731–2741 (2022).

    Article  PubMed  Google Scholar 

  50. Westwater, M. L., Vilar-López, R., Ziauddeen, H., Verdejo-García, A. & Fletcher, P. C. Combined effects of age and BMI are related to altered cortical thickness in adolescence and adulthood. Dev. Cogn. Neurosci. https://doi.org/10.1016/J.DCN.2019.100728 (2019).

  51. Saute, R. L., Soder, R. B., Alves Filho, J. O., Baldisserotto, M. & Franco, A. R. Increased brain cortical thickness associated with visceral fat in adolescents. Pediatr. Obes. 13, 74–77 (2016).

    Article  PubMed  Google Scholar 

  52. Hill, J. et al. Similar patterns of cortical expansion during human development and evolution. Proc. Natl Acad. Sci. USA 107, 13135–13140 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Hogstrom, L. J., Westlye, L. T., Walhovd, K. B. & Fjell, A. M. The structure of the cerebral cortex across adult life: age-related patterns of surface area, thickness, and gyrification. Cereb. Cortex 23, 2521–2530 (2013).

    Article  PubMed  Google Scholar 

  54. Seldon, H. L. Does brain white matter growth expand the cortex like a balloon? Hypothesis and consequences. Laterality 10, 81–95 (2005).

    Article  PubMed  Google Scholar 

  55. Ronan, L., Alexander-Bloch, A. & Fletcher, P. C. Childhood obesity, cortical structure, and executive function in healthy children. Cereb. Cortex https://doi.org/10.1093/cercor/bhz257 (2019).

    Article  PubMed Central  Google Scholar 

  56. Ronan, L. et al. Obesity associated with increased brain age from midlife. Neurobiol. Aging 47, 63–70 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Foerde, K., Steinglass, J. E., Shohamy, D. & Walsh, B. T. Neural mechanisms supporting maladaptive food choices in anorexia nervosa. Nat. Neurosci. 18, 1571–1573 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  58. DeGuzman, M., Shott, M. E., Yang, T. T., Riederer, J. & Frank, G. K. W. Association of elevated reward prediction error response with weight gain in adolescent anorexia nervosa. Am. J. Psychiatry 174, 557–565 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Walton, E. et al. Brain structure in acutely underweight and partially weight-restored individuals with anorexia nervosa—a coordinated analysis by the ENIGMA eating disorders working group. Biol. Psychiatry https://doi.org/10.1016/j.biopsych.2022.04.022 (2022).

  60. Bos, M. G. N., Peters, S., Van De Kamp, F. C., Crone, E. A. & Tamnes, C. K. Emerging depression in adolescence coincides with accelerated frontal cortical thinning. J. Child Psychol. Psychiatry Allied Discip. 59, 994–1002 (2018).

    Article  Google Scholar 

  61. Sylvester, C. M. et al. Functional network dysfunction in anxiety and anxiety disorders. Trends Neurosci. 35, 527–535 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Monk, C. S. et al. Ventrolateral prefrontal cortex activation and attentional bias in response to angry faces in adolescents with generalized anxiety disorder. Am. J. Psychiatry 163, 1091–1097 (2006).

    Article  PubMed  Google Scholar 

  63. Balderston, N. L. et al. Anxiety patients show reduced working memory related dlPFC activation during safety and threat. Depress. Anxiety 34, 25–36 (2017).

    Article  PubMed  Google Scholar 

  64. Raznahan, A. et al. Patterns of coordinated anatomical change in human cortical development: a longitudinal neuroimaging study of maturational coupling. Neuron 72, 873–884 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Favaro, A. et al. Disruption of visuospatial and somatosensory functional connectivity in anorexia nervosa. Biol. Psychiatry 72, 864–870 (2012).

    Article  PubMed  Google Scholar 

  66. Stopyra, M. A. et al. Altered functional connectivity in binge eating disorder and bulimia nervosa: a resting-state fMRI study. Brain Behav. 9, e01207 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Steward, T., Menchon, J. M., Jiménez-Murcia, S., Soriano-Mas, C. & Fernandez-Aranda, F. Neural network alterations across eating disorders: a narrative review of fMRI studies. Curr. Neuropharmacol. 16, 1150–1163 (2017).

    Article  Google Scholar 

  68. Westwater, M. L., Seidlitz, J., Diederen, K. M. J., Fischer, S. & Thompson, J. C. Associations between cortical thickness, structural connectivity and severity of dimensional bulimia nervosa symptomatology. Psychiatry Res. Neuroimaging 271, 118–125 (2018).

    Article  PubMed  Google Scholar 

  69. Berner, L. A. et al. Altered cortical thickness and attentional deficits in adolescent girls and women with bulimia nervosa. J. Psychiatry Neurosci. 43, 151–160 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Marsh, R. et al. Anatomical characteristics of the cerebral surface in bulimia nervosa. Biol. Psychiatry 77, 616–623 (2015).

    Article  PubMed  Google Scholar 

  71. Baum, G. L. et al. Development of structure–function coupling in human brain networks during youth. Proc. Natl Acad. Sci. USA. 117, 771–778 (2020).

    Article  PubMed  Google Scholar 

  72. Nunn, K., Frampton, I. J., Gordon, I. & Lask, B. The fault is not in her parents but in her insula—a neurobiological hypothesis of anorexia nervosa. Eur. Eat. Disord. Rev. 16, 355–360 (2008).

    Article  PubMed  Google Scholar 

  73. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Privé, F. et al. Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. Am. J. Hum. Genet. 109, 12–23 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Cascino, G. et al. Childhood maltreatment is associated with cortical thinning in people with eating disorders. Eur. Arch. Psychiatry Clin. Neurosci. 273, 459–466 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Buimer, E. E. L. et al. Adverse childhood experiences and fronto-subcortical structures in the developing brain. Front. Psychiatry 13, 955871 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Grotzinger, A. D. et al. Multivariate genomic architecture of cortical thickness and surface area at multiple levels of analysis. Nat. Commun. 14, 946 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Noble, S., Mejia, A. F., Zalesky, A. & Scheinost, D. Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference. Proc. Natl Acad. Sci. USA 119, e2203020119 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Smink, F. R. E., van Hoeken, D. & Hoek, H. W. Epidemiology of eating disorders: incidence, prevalence and mortality rates. Curr. Psychiatry Rep. 14, 406–414 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Garavan, H. et al. Recruiting the ABCD sample: design considerations and procedures. Dev. Cogn. Neurosci. 32, 16–22 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Michelini, G. et al. Delineating and validating higher-order dimensions of psychopathology in the Adolescent Brain Cognitive Development (ABCD) study. Transl. Psychiatry 9, 261 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Auchter, A. M. et al. A description of the ABCD organizational structure and communication framework. Dev. Cogn. Neurosci. 32, 8–15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43–54 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).

    Article  PubMed  Google Scholar 

  85. Fischl, B., Sereno, M. I. & Dale, A. M. Cortical surface-based analysis: II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195–207 (1999).

    Article  PubMed  Google Scholar 

  86. Fischl, B., Sereno, M. I., Tootell, R. B. H. & Dale, A. M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8, 272–284 (1999).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).

    Article  PubMed  Google Scholar 

  88. Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).

    Article  PubMed  Google Scholar 

  89. Rosen, A. F. G. et al. Quantitative assessment of structural image quality. Neuroimage 169, 407–418 (2018).

    Article  PubMed  Google Scholar 

  90. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  92. 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 

  93. Fortin, J.-P. et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 167, 104–120 (2018).

    Article  PubMed  Google Scholar 

  94. Raznahan, A. et al. Longitudinally mapping the influence of sex and androgen signaling on the dynamics of human cortical maturation in adolescence. Proc. Natl Acad. Sci. USA 107, 16988–16993 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Giedd, J. N. et al. Review: magnetic resonance imaging of male/female differences in human adolescent brain anatomy. Biol. Sex Differ. 3, 19 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Shaw, P. et al. Intellectual ability and cortical development in children and adolescents. Nature 440, 676–679 (2006).

    Article  PubMed  Google Scholar 

  97. Goddings, A.-L. et al. The influence of puberty on subcortical brain development. Neuroimage 88, 242–251 (2014).

    Article  PubMed  Google Scholar 

  98. Wechsler, D. Wechsler Intelligence Scale for Children–Fifth Edition (WISC-V): Technical and Interpretive Manual (Pearson, 2014).

  99. Petersen, A. C., Crockett, L., Richards, M. & Boxer, A. A self-report measure of pubertal status: reliability, validity, and initial norms. J. Youth Adolesc. 17, 117–133 (1988).

    Article  PubMed  Google Scholar 

  100. Cheng, T. W. et al. A researcher’s guide to the measurement and modeling of puberty in the ABCD Study® at baseline. Front. Endocrinol. 12, 608575 (2021).

    Article  Google Scholar 

  101. Yeo, B. T. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).

    Article  PubMed  Google Scholar 

  102. Barch, D. M. et al. Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: Rationale and description. Dev. Cogn. Neurosci. 32, 55–66 (2018).

    Article  PubMed  Google Scholar 

  103. Muthen, L. K. & Muthen, B. O. Mplus User’s Guide (Muthén and Muthén, 2017).

  104. Kotov, R. et al. The hierarchical taxonomy of psychopathology (HiTOP): a dimensional alternative to traditional nosologies. J. Abnorm. Psychol. 126, 454–477 (2017).

    Article  PubMed  Google Scholar 

  105. Forbush, K. T. et al. Locating eating pathology within an empirical diagnostic taxonomy: evidence from a community-based sample. J. Abnorm. Psychol. 119, 282–292 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Eaton, N. R. et al. The structure and predictive validity of the internalizing disorders. J. Abnorm. Psychol. 122, 86–92 (2013).

    Article  PubMed  Google Scholar 

  107. Lahey, B. B. et al. Testing structural models of DSM-IV symptoms of common forms of child and adolescent psychopathology. J. Abnorm. Child Psychol. 36, 187–206 (2008).

    Article  PubMed  Google Scholar 

  108. Hu, L. T. & Bentler, P. M. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model. 6, 1–55 (1999).

    Article  Google Scholar 

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Acknowledgements

We thank A. Nadig (Harvard Medical School) for helpful discussions. We also wish to thank the ABCD participants, their families, and the researchers who collected these data. M.L.W. was supported by the National Institutes of Health (NIH) Oxford–Cambridge Scholars Program and NIH T32DA022975. T.T.M. receives support from NIH T32HG010464. V.W. is supported by St. Catharine’s College Cambridge. R.A.I.B. received support from the Autism Research Trust. P.C.F. receives support from the Bernard Wolfe Health Neuroscience Fund. J.S. was supported by NIH T32MH019112. Finally, C.G., M.E. and M.L.W. received funding from the Intramural Research Program at the National Institute of Mental Health (NIMH; ZIAMH002798). Data were curated and analyzed using a computational facility funded by a Medical Research Council research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NIH, National Health Service, NIHR or the Department of Health and Social Care of the UK government. This work utilized computational resources of the NIH high-performance computing Biowulf cluster (http://hpc.nih.gov). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data used in the preparation of this article were obtained from the ABCD study (https://abcdstudy.org), held in the NIMH Data Archive. This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 and follow them for 10 years into early adulthood. The ABCD study is supported by the NIH and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123 and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A list of participating sites and a complete list of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This 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. Digital object identifiers can be found at https://nda.nih.gov/abcd/abcd-annual-releases.html.

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Author notes

  1. These authors jointly supervised this work: Jakob Seidlitz, Monique Ernst.

Authors and Affiliations

  1. Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA

    Margaret L. Westwater & Dustin Scheinost

  2. National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA

    Margaret L. Westwater, Christian Grillon & Monique Ernst

  3. Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Travis T. Mallard

  4. Department of Psychiatry, Harvard Medical School, Boston, MA, USA

    Travis T. Mallard

  5. Department of Psychiatry, University of Cambridge, Cambridge, UK

    Varun Warrier & Paul C. Fletcher

  6. Department of Psychology, University of Cambridge, Cambridge, UK

    Varun Warrier & Richard A. I. Bethlehem

  7. Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK

    Paul C. Fletcher

  8. Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Jakob Seidlitz

  9. Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA

    Jakob Seidlitz

  10. Lifespan Brain Institute of Children’s Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, USA

    Jakob Seidlitz

Authors
  1. Margaret L. Westwater
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Contributions

M.L.W., T.T.M., J.S., and M.E. designed the study. V.W. computed polygenic scores and performed quality assurance checks of genetic data. R.A.I.B. and J.S. processed neuroimaging data. T.T.M. estimated genetic relatedness of ABCD participants. M.L.W. prepared neuroimaging and behavioral data and performed all SEM analysis, with assistance from T.T.M.; D.S., C.G., and P.C.F. provided computing resources and advice during preparation of the paper. M.L.W. prepared figures and tables. M.L.W. drafted the paper. All authors revised the paper and provided critical intellectual contributions.

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Correspondence to Margaret L. Westwater.

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Nature Mental Health thanks Samantha Jane Brooks, Fernando Fernandez-Aranda, and Trevor Steward for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 KSADS-5 item correlations.

Items indexing lifetime internalizing and eating disorder psychopathology demonstrated moderate to strong postive correlations with one another; however, lifetime emaciation demonstrated weak, negative associations with most items (see Supplementary Table 2 for corresponding p-values). Lifetime self-induced vomiting similarly was nonsignificantly correlated with other items. Correlation values were estimated in the maximum analytic sample for visualization. EMAC = emaciation, OBE = objective binge eating, VOM = self-induced vomiting, OB_FEAR = fear of becoming obese, L_MOOD = low mood, ANHED = anhedonia, WRRY = worry most days, SOC_FEAR = fear of social situations, PHOB = avoidance of phobic object, OBSES = obsessions, COMP = compulsions.

Extended Data Fig. 2 Frequency of lifetime KSADS-5 item endorsement by factor.

To determine the number of eating disorder and internalizing psychopathology symptoms reported for each participant, item endorsement was summed for each latent factor. As would be expected, the distribution of sum scores for each factor was positively skewed, meaning that most parents reported one or fewer lifetime symptoms for their child. Histograms were generated in the full analytic sample for visualization. Frequency counts are presented above each bin.

Supplementary information

Supplementary Information

Supplementary Results, Discussion and References.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–12.

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Westwater, M.L., Mallard, T.T., Warrier, V. et al. Assessing a multivariate model of brain-mediated genetic influences on disordered eating in the ABCD cohort. Nat. Mental Health 1, 573–585 (2023). https://doi.org/10.1038/s44220-023-00101-4

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