Browsing by Author "The Alzheimer’s Disease Neuroimaging Initiative"

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    A genetically informed brain atlas for enhancing brain imaging genomics
    (Springer Nature, 2025-04-14) Bao, Jingxuan; Wen, Junhao; Chang, Changgee; Mu, Shizhuo; Chen, Jiong; Shivakumar, Manu; Cui, Yuhan; Erus, Guray; Yang, Zhijian; Yang, Shu; Wen, Zixuan; The Alzheimer’s Disease Neuroimaging Initiative; Zhao, Yize; Kim, Dokyoon; Duong-Tran, Duy; Saykin, Andrew J.; Zhao, Bingxin; Davatzikos, Christos; Long, Qi; Shen, Li; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Brain imaging genomics has manifested considerable potential in illuminating the genetic determinants of human brain structure and function. This has propelled us to develop the GIANT (Genetically Informed brAiN aTlas) that accounts for genetic and neuroanatomical variations simultaneously. Integrating voxel-wise heritability and spatial proximity, GIANT clusters brain voxels into genetically informed regions, while retaining fundamental anatomical knowledge. Compared to conventional (non-genetics) brain atlases, GIANT exhibits smaller intra-region variations and larger inter-region variations in terms of voxel-wise heritability. As a result, GIANT yields increased regional SNP heritability, enhanced polygenicity, and its polygenic risk score explains more brain volumetric variation than traditional neuroanatomical brain atlases. We provide extensive validation to GIANT and demonstrate its neuroanatomical validity, confirming its generalizability across populations with diverse genetic ancestries and various brain conditions. Furthermore, we present a comprehensive genetic architecture of the GIANT regions, covering their functional annotation at the molecular levels, their associations with other complex traits/diseases, and the genetic and phenotypic correlations among GIANT-defined imaging endophenotypes. In summary, GIANT constitutes a brain atlas that captures the complexity of genetic and neuroanatomical heterogeneity, thereby enhancing the discovery power and applicability of imaging genomics investigations in biomedical science.
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    Individual bioenergetic capacity as a potential source of resilience to Alzheimer's disease
    (medRxiv, 2024-01-24) Arnold, Matthias; Buyukozkan, Mustafa; Doraiswamy, P. Murali; Nho, Kwangsik; Wu, Tong; Gudnason, Vilmundur; Launer, Lenore J.; Wang-Sattler, Rui; Adamski, Jerzy; The Alzheimer’s Disease Neuroimaging Initiative; Alzheimer’s Disease Metabolomics Consortium; De Jager, Philip L.; Ertekin-Taner, Nilüfer; Bennett, David A.; Saykin, Andrew J.; Peters, Annette; Suhre, Karsten; Kaddurah-Daouk, Rima; Kastenmüller, Gabi; Krumsiek, Jan; Radiology and Imaging Sciences, School of Medicine
    Impaired glucose uptake in the brain is one of the earliest presymptomatic manifestations of Alzheimer's disease (AD). The absence of symptoms for extended periods of time suggests that compensatory metabolic mechanisms can provide resilience. Here, we introduce the concept of a systemic 'bioenergetic capacity' as the innate ability to maintain energy homeostasis under pathological conditions, potentially serving as such a compensatory mechanism. We argue that fasting blood acylcarnitine profiles provide an approximate peripheral measure for this capacity that mirrors bioenergetic dysregulation in the brain. Using unsupervised subgroup identification, we show that fasting serum acylcarnitine profiles of participants from the AD Neuroimaging Initiative yields bioenergetically distinct subgroups with significant differences in AD biomarker profiles and cognitive function. To assess the potential clinical relevance of this finding, we examined factors that may offer diagnostic and therapeutic opportunities. First, we identified a genotype affecting the bioenergetic capacity which was linked to succinylcarnitine metabolism and significantly modulated the rate of future cognitive decline. Second, a potentially modifiable influence of beta-oxidation efficiency seemed to decelerate bioenergetic aging and disease progression. Our findings, which are supported by data from more than 9,000 individuals, suggest that interventions tailored to enhance energetic health and to slow bioenergetic aging could mitigate the risk of symptomatic AD, especially in individuals with specific mitochondrial genotypes.
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    Individual bioenergetic capacity as a potential source of resilience to Alzheimer's disease
    (Springer Nature, 2025-02-24) Arnold, Matthias; Buyukozkan, Mustafa; Doraiswamy, P. Murali; Nho, Kwangsik; Wu, Tong; Gudnason, Vilmundur; Launer, Lenore J.; Wang-Sattler, Rui; Adamski, Jerzy; The Alzheimer’s Disease Neuroimaging Initiative; Alzheimer’s Disease Metabolomics Consortium; De Jager, Philip L.; Ertekin-Taner, Nilüfer; Bennett, David A.; Saykin, Andrew J.; Peters, Annette; Suhre, Karsten; Kaddurah-Daouk, Rima; Kastenmüller, Gabi; Krumsiek, Jan; Radiology and Imaging Sciences, School of Medicine
    Impaired glucose uptake in the brain is an early presymptomatic manifestation of Alzheimer's disease (AD), with symptom-free periods of varying duration that likely reflect individual differences in metabolic resilience. We propose a systemic "bioenergetic capacity", the individual ability to maintain energy homeostasis under pathological conditions. Using fasting serum acylcarnitine profiles from the AD Neuroimaging Initiative as a blood-based readout for this capacity, we identified subgroups with distinct clinical and biomarker presentations of AD. Our data suggests that improving beta-oxidation efficiency can decelerate bioenergetic aging and disease progression. The estimated treatment effects of targeting the bioenergetic capacity were comparable to those of recently approved anti-amyloid therapies, particularly in individuals with specific mitochondrial genotypes linked to succinylcarnitine metabolism. Taken together, our findings provide evidence that therapeutically enhancing bioenergetic health may reduce the risk of symptomatic AD. Furthermore, monitoring the bioenergetic capacity via blood acylcarnitine measurements can be achieved using existing clinical assays.