Junhao Wen1, Cynthia H Y Fu2,3, Duygu Tosun4, Yogasudha Veturi5, Zhijian Yang1, Ahmed Abdulkadir1, Elizabeth Mamourian1, Dhivya Srinivasan1, Ioanna Skampardoni1, Ashish Singh1, Hema Nawani1, Jingxuan Bao6, Guray Erus1, Haochang Shou1,7, Mohamad Habes8, Jimit Doshi1, Erdem Varol9, R Scott Mackin10, Aristeidis Sotiras11, Yong Fan1, Andrew J Saykin12, Yvette I Sheline13, Li Shen6, Marylyn D Ritchie5, David A Wolk1,14, Marilyn Albert15, Susan M Resnick16, Christos Davatzikos1. 1. Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia. 2. University of East London, School of Psychology, London, United Kingdom. 3. Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom. 4. Department of Radiology and Biomedical Imaging, University of California, San Francisco. 5. Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia. 6. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia. 7. Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia. 8. Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio. 9. Department of Statistics, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, New York. 10. Department of Psychiatry and Behavioral Sciences, University of California, San Francisco. 11. Department of Radiology and Institute for Informatics, Washington University School of Medicine, St Louis, Missouri. 12. Radiology and Imaging Sciences, Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana Alzheimer's Disease Research Center and the Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis. 13. Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia. 14. Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia. 15. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland. 16. Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland.
Abstract
Importance: Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support precision and individualized medicine. Objective: To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associated with neuroanatomy, cognitive functioning, clinical symptoms, and genetic profiles. Design, Setting, and Participants: The Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with more than 35 000 participants, including a subset of individuals with major depressive disorder. Multimodal data from a multicenter sample (N = 996), including neuroimaging, neurocognitive assessments, and genetics, were analyzed in this study. A semisupervised clustering method (heterogeneity through discriminative analysis) was applied to regional gray matter (GM) brain volumes to derive dimensional representations. Data were collected from July 2017 to July 2020 and analyzed from July 2020 to December 2021. Main Outcomes and Measures: Two dimensions were identified to delineate LLD-associated heterogeneity in voxelwise GM maps, white matter (WM) fractional anisotropy, neurocognitive functioning, clinical phenotype, and genetics. Results: A total of 501 participants with LLD (mean [SD] age, 67.39 [5.56] years; 332 women) and 495 healthy control individuals (mean [SD] age, 66.53 [5.16] years; 333 women) were included. Patients in dimension 1 demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy control individuals. In contrast, patients in dimension 2 showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, 1 de novo independent genetic variant (rs13120336; chromosome: 4, 186387714; minor allele, G) was significantly associated with dimension 1 (odds ratio, 2.35; SE, 0.15; P = 3.14 ×108) but not with dimension 2. The 2 dimensions demonstrated significant single-nucleotide variant-based heritability of 18% to 27% within the general population (N = 12 518 in UK Biobank). In a subset of individuals having longitudinal measurements, those in dimension 2 experienced a more rapid longitudinal change in GM and brain age (Cohen f2 = 0.03; P = .02) and were more likely to progress to Alzheimer disease (Cohen f2 = 0.03; P = .03) compared with those in dimension 1 (N = 1431 participants and 7224 scans from the Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], and Biomarkers for Older Controls at Risk for Dementia [BIOCARD] data sets). Conclusions and Relevance: This study characterized heterogeneity in LLD into 2 dimensions with distinct neuroanatomical, cognitive, clinical, and genetic profiles. This dimensional approach provides a potential mechanism for investigating the heterogeneity of LLD and the relevance of the latent dimensions to possible disease mechanisms, clinical outcomes, and responses to interventions.
Importance: Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support precision and individualized medicine. Objective: To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associated with neuroanatomy, cognitive functioning, clinical symptoms, and genetic profiles. Design, Setting, and Participants: The Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with more than 35 000 participants, including a subset of individuals with major depressive disorder. Multimodal data from a multicenter sample (N = 996), including neuroimaging, neurocognitive assessments, and genetics, were analyzed in this study. A semisupervised clustering method (heterogeneity through discriminative analysis) was applied to regional gray matter (GM) brain volumes to derive dimensional representations. Data were collected from July 2017 to July 2020 and analyzed from July 2020 to December 2021. Main Outcomes and Measures: Two dimensions were identified to delineate LLD-associated heterogeneity in voxelwise GM maps, white matter (WM) fractional anisotropy, neurocognitive functioning, clinical phenotype, and genetics. Results: A total of 501 participants with LLD (mean [SD] age, 67.39 [5.56] years; 332 women) and 495 healthy control individuals (mean [SD] age, 66.53 [5.16] years; 333 women) were included. Patients in dimension 1 demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy control individuals. In contrast, patients in dimension 2 showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, 1 de novo independent genetic variant (rs13120336; chromosome: 4, 186387714; minor allele, G) was significantly associated with dimension 1 (odds ratio, 2.35; SE, 0.15; P = 3.14 ×108) but not with dimension 2. The 2 dimensions demonstrated significant single-nucleotide variant-based heritability of 18% to 27% within the general population (N = 12 518 in UK Biobank). In a subset of individuals having longitudinal measurements, those in dimension 2 experienced a more rapid longitudinal change in GM and brain age (Cohen f2 = 0.03; P = .02) and were more likely to progress to Alzheimer disease (Cohen f2 = 0.03; P = .03) compared with those in dimension 1 (N = 1431 participants and 7224 scans from the Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], and Biomarkers for Older Controls at Risk for Dementia [BIOCARD] data sets). Conclusions and Relevance: This study characterized heterogeneity in LLD into 2 dimensions with distinct neuroanatomical, cognitive, clinical, and genetic profiles. This dimensional approach provides a potential mechanism for investigating the heterogeneity of LLD and the relevance of the latent dimensions to possible disease mechanisms, clinical outcomes, and responses to interventions.
Authors: Astrid Lugtenburg; Marij Zuidersma; Klaas J Wardenaar; Ivan Aprahamian; Didi Rhebergen; Robert A Schoevers; Richard C Oude Voshaar Journal: J Gerontol A Biol Sci Med Sci Date: 2021-01-01 Impact factor: 6.053
Authors: M Luppa; C Sikorski; T Luck; L Ehreke; A Konnopka; B Wiese; S Weyerer; H-H König; S G Riedel-Heller Journal: J Affect Disord Date: 2010-12-30 Impact factor: 4.839
Authors: Claire E Sexton; Charlotte L Allan; Marisa Le Masurier; Lisa M McDermott; Ukwuori G Kalu; Lucie L Herrmann; Matthias Mäurer; Kevin M Bradley; Clare E Mackay; Klaus P Ebmeier Journal: Arch Gen Psychiatry Date: 2012-07
Authors: Xueyi Shen; Lianne M Reus; Simon R Cox; Mark J Adams; David C Liewald; Mark E Bastin; Daniel J Smith; Ian J Deary; Heather C Whalley; Andrew M McIntosh Journal: Sci Rep Date: 2017-07-17 Impact factor: 4.379
Authors: Emma Rhodes; Philip S Insel; Meryl A Butters; Ruth Morin; David Bickford; Duygu Tosun; Devon Gessert; Howie J Rosen; Paul Aisen; Rema Raman; Susan Landau; Andrew Saykin; Arthur Toga; Clifford R Jack; Michael W Weiner; Craig Nelson; Scott Mackin Journal: J Alzheimers Dis Date: 2021 Impact factor: 4.472
Authors: Mohamad Habes; Raymond Pomponio; Haochang Shou; Jimit Doshi; Elizabeth Mamourian; Guray Erus; Ilya Nasrallah; Lenore J Launer; Tanweer Rashid; Murat Bilgel; Yong Fan; Jon B Toledo; Kristine Yaffe; Aristeidis Sotiras; Dhivya Srinivasan; Mark Espeland; Colin Masters; Paul Maruff; Jurgen Fripp; Henry Völzk; Sterling C Johnson; John C Morris; Marilyn S Albert; Michael I Miller; R Nick Bryan; Hans J Grabe; Susan M Resnick; David A Wolk; Christos Davatzikos Journal: Alzheimers Dement Date: 2020-09-13 Impact factor: 16.655
Authors: Laura de Nooij; Mathew A Harris; Mark J Adams; Toni-Kim Clarke; Xueyi Shen; Simon R Cox; Andrew M McIntosh; Heather C Whalley Journal: Eur Psychiatry Date: 2020-02-21 Impact factor: 5.361
Authors: Joan Guàrdia-Olmos; Carles Soriano-Mas; Lara Tormo-Rodríguez; Cristina Cañete-Massé; Inés Del Cerro; Mikel Urretavizcaya; José M Menchón; Virgina Soria; Maribel Peró-Cebollero Journal: Int J Clin Health Psychol Date: 2022-05-27