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[Preprint]. 2024 Jun 26:2024.01.09.24301030.
doi: 10.1101/2024.01.09.24301030.

Population clustering of structural brain aging and its association with brain development

Haojing Duan  1   2 , Runye Shi  3 , Jujiao Kang  1   2 , Tobias Banaschewski  4 , Arun L W Bokde  5 , Christian Büchel  6 , Sylvane Desrivières  7 , Herta Flor  8   9 , Antoine Grigis  10 , Hugh Garavan  11 , Penny A Gowland  12 , Andreas Heinz  13 , Rüdiger Brühl  14 , Jean-Luc Martinot  15   16 , Marie-Laure Paillère Martinot  15   17 , Eric Artiges  15   16 , Frauke Nees  4   8   18 , Dimitri Papadopoulos Orfanos  10 , Luise Poustka  19 , Sarah Hohmann  4 , Nathalie Holz  4 , Juliane H Fröhner  20 , Michael N Smolka  20 , Nilakshi Vaidya  21 , Henrik Walter  13 , Robert Whelan  22 , Gunter Schumann  1   21   23   24 , Xiaolei Lin  3   25 , Jianfeng Feng  1   2   3   26   27   28 ; IMAGEN consortium
Affiliations

Population clustering of structural brain aging and its association with brain development

Haojing Duan et al. medRxiv. .

Update in

  • Population clustering of structural brain aging and its association with brain development.
    Duan H, Shi R, Kang J, Banaschewski T, Bokde ALW, Büchel C, Desrivières S, Flor H, Grigis A, Garavan H, Gowland PA, Heinz A, Brühl R, Martinot JL, Martinot MP, Artiges E, Nees F, Papadopoulos Orfanos D, Poustka L, Hohmann S, Nathalie Holz N, Fröhner J, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Lin X, Feng J. Duan H, et al. Elife. 2024 Oct 18;13:RP94970. doi: 10.7554/eLife.94970. Elife. 2024. PMID: 39422662 Free PMC article.

Abstract

Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the "last in, first out" mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.

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Conflict of interest statement

Dr Banaschewski served in an advisory or consultancy role for eye level, Infectopharm, Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Roche, and Takeda. He received conference support or speaker’s fee by Janssen, Medice and Takeda. He received royalities from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press; the present work is unrelated to these relationships. Dr Poustka served in an advisory or consultancy role for Roche and Viforpharm and received speaker’s fee by Shire. She received royalties from Hogrefe, Kohlhammer and Schattauer. The present work is unrelated to the above grants and relationships. The other authors report no biomedical financial interests or potential conflicts of interest.

Figures

Figure 1.
Figure 1.. Overview of the study workflow.
a, Population cohorts (UK Biobank and IMAGEN) and data sources (brain imaging, biological aging biomarkers, cognitive functions, genomic data) involved in this study. b, Brain aging patterns were identified using longitudinal trajectories of the whole brain GMV, which enabled the capturing of long-term and individualized variations compared to only use cross-sectional data, and associations between brain aging patterns and other measurements (biological aging, cognitive functions and PRS of major neuropsychiatric disorders) were investigated. c, Mirroring patterns between brain aging and brain development was investigated using z-transformed brain volumetric change map and gene expression analysis.
Figure 2.
Figure 2.. Global (a) and selected regional (b, c) cortical gray matter volume rate of change among participants with brain aging patterns 1 (red) and 2 (blue).
Rates of volumetric change for total gray matter and each ROI were estimated using GAMM, which incorporates both cross-sectional between-subject variation and longitudinal within-subject variation from 40,921 observations and 37,013 participants. Covariates include sex, assessment center, handedness, ethnic, and ICV. Shaded areas around the fit line denotes 95% CI.
Figure 3.
Figure 3.. Distributions of biological aging biomarkers (leucocyte telomere length (LTL) and PhenoAge) among participants with brain aging patterns 1 and 2.
Boxes represent the interquartile range (IQR), lines within the boxes indicate the median. Two-sided P values were obtained by comparing LTL or PhenoAge between brain aging patterns using unadjusted multivariate linear regression models. Results remained significant when adjusting for sex, age, ethnic, BMI, smoking status and alcohol intake frequency in the LTL model and sex, age, ethnic, BMI, smoking status, alcohol frequency and education years in PhenoAge model. Stars indicate statistical significance after Bonferroni correction. ****: p <= 0.0001, *: p <= 0.05.
Figure 4.
Figure 4.. Effect size (Cohen’s D or odds ratio) for comparing the cognitive functions between participants with brain aging patterns 1 and 2.
Results were adjusted such that negative Cohen’s D and Odds Ratio less than 1 indicate worse cognitive performances in brain aging pattern 2 compared to pattern 1. Width of the lines extending from the center point represent 95% confidence interval. Two-sided P values were obtained using both unadjusted and adjusted (for sex, age, and TDI, education and income) multivariate regression models. Stars indicate statistical significance after FDR correction for 11 comparisons. ****: p <= 0.0001, ***: p <= 0.001, **: p <= 0.01, ns: p > 0.05.
Figure 5.
Figure 5.. Participants with accelerated brain aging (brain aging pattern 2) had significantly increased genetic liability to ADHD and delayed brain development.
Polygenic risk score (PRS) for ADHD, ASD, AD, PD, BIP, MDD, SCZ and delayed brain development (unpublished GWAS) were calculated at different p-value thresholds from 0.005 to 0.5 at an interval of 0.005. Vertical axis represents negative logarithm of P values comparing PRS in brain aging pattern 2 relative to pattern 1. Red horizontal dashed line indicates FDR corrected P value of 0.05. Colors represent traits and dots within the same color represent different p value thresholds. The trigonometric symbol indicates the average PRS across all p-value thresholds for the same trait.
Figure 6.
Figure 6.. Genome-wide association study (GWAS) identified 6 independent SNPs associated with accelerated brain aging.
Total GMV at 60 years old was estimated for each participant using mixed effect models allowing for individualized baseline GMV and GMV change rate, and was used as the phenotype in the GWAS. a, At genome-wide significance level (P = 5 × 10−8, red dashed line), rs10835187 and rs7776725 loci were identified to be associated with accelerated brain aging. b, Quantile–quantile plot showed that the most significant P values deviate from the null, suggesting that results are not unduly inflated.
Figure 7.
Figure 7.. The “last in, first out” mirroring patterns between brain development and brain aging.
a, The annual percentage volume change (APC) was calculated for each ROI and standardized across the whole brain in adolescents (IMAGEN, left) and mid-to-late aged adults (UK Biobank, right), respectively. For adolescents, ROIs of in red indicate delayed structural brain development, while for mid-to-late aged adults, ROIs in blue indicate accelerated structural brain aging. b, Estimated APC in brain development versus early aging (55 years old, left), and versus late aging (75 years old, right). ROIs in red indicate faster GMV decrease during brain aging and slower GMV decrease during brain development, i.e., stronger mirroring effects between brain development and brain aging. c, Mirroring patterns between brain development and brain aging were more manifested in participants with accelerated aging (brain aging pattern 2). The arrows point to ROIs with more pronounced mirroring patterns in each subfigure.
Figure 8.
Figure 8.. Functional enrichment of gene transcripts significantly associated with delayed brain development and accelerated brain aging.
a, 990 genes were spatially correlated with the first PLS component of delayed structural brain development, and were enriched for trans-synaptic signal regulation, forebrain development, signal release and cAMP signaling pathway. b, 2,293 genes were spatially correlated the first PLS component of accelerated structural brain aging, and were enriched for macroautophagy, pathways of neurodegeneration, establishment of protein localization to organelle and histone modification. Size of the circle represents number of genes in each term and P values were corrected using FDR for multiple comparisons.
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References

    1. Fjell A. M. & Walhovd K. B. Structural brain changes in aging: courses, causes and cognitive consequences. Reviews in the Neurosciences 21, 187–222 (2010). - PubMed
    1. Shaw P. et al. Neurodevelopmental trajectories of the human cerebral cortex. Journal of neuroscience 28, 3586–3594 (2008). - PMC - PubMed
    1. Elliott M. L. et al. Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort. Molecular psychiatry 26, 3829–3838 (2021). - PMC - PubMed
    1. Mattson M. P. & Arumugam T. V. Hallmarks of brain aging: adaptive and pathological modification by metabolic states. Cell metabolism 27, 1176–1199 (2018). - PMC - PubMed
    1. Park D. C. & Reuter-Lorenz P. The adaptive brain: aging and neurocognitive scaffolding. Annual review of psychology 60, 173–196 (2009). - PMC - PubMed

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