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Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality
- Ye Ella Tian ORCID:orcid.org/0000-0003-3107-55501,
- Vanessa Cropley1,
- Andrea B. Maier2,3,4,
- Nicola T. Lautenschlager5,6,
- Michael Breakspear7,8 &
- …
- Andrew Zalesky ORCID:orcid.org/0000-0003-2298-99081,9
Nature Medicinevolume 29, pages1221–1231 (2023)Cite this article
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Abstract
Biological aging of human organ systems reflects the interplay of age, chronic disease, lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes from the UK Biobank, we establish normative models of biological age for three brain and seven body systems. Here we find that an organ’s biological age selectively influences the aging of other organ systems, revealing a multiorgan aging network. We report organ age profiles for 16 chronic diseases, where advanced biological aging extends from the organ of primary disease to multiple systems. Advanced body age associates with several lifestyle and environmental factors, leukocyte telomere lengths and mortality risk, and predicts survival time (area under the curve of 0.77) and premature death (area under the curve of 0.86). Our work reveals the multisystem nature of human aging in health and chronic disease. It may enable early identification of individuals at increased risk of aging-related morbidity and inform new strategies to potentially limit organ-specific aging in such individuals.
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Data availability
Data were obtained from the UK Biobank, the ADNI and the AIBL Flagship Study of Ageing. Participant age gaps for all body and brain systems estimated in this study will be returned to the UK Biobank to strengthen the resource and facilitate access to other researchers for future research. Researchers can register to access all data used in this study via the UK Biobank Access Management System (https://bbams.ndph.ox.ac.uk/ams/) and the ADNI database (https://adni.loni.usc.edu/).
Code availability
MATLAB (R2021a, MathWorks) code for conducting the core analyses is available on GitHub (https://github.com/yetianmed/BioAge). SEM was performed using the Tetrad software package v.6.8.1 (https://github.com/cmu-phil/tetrad). The organ images shown in Fig.1 were created with BioRender.com. Other figures were created using visualization routines in MATLAB.
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Acknowledgements
This research has been conducted using data from UK Biobank (https://www.ukbiobank.ac.uk/), a major biomedical database. We are grateful to UK Biobank for making the data available and to all study participants, who generously donated their time to make this resource possible. Some of the data used in the preparation of this article were obtained from the AIBL Flagship Study of Ageing, funded by the Commonwealth Scientific and Industrial Research Organisation, which was made available at the ADNI database (https://adni.loni.usc.edu/). The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at:https://aibl.csiro.au/. Some of the data used in preparation of this article were obtained from the ADNI database (https://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. Some of the data collection and sharing for this project was funded by ADNI (National Institutes of Health grant U01 AG024904) and Department of Defense ADNI (award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica; Biogen; Bristol-Myers Squibb Company; CereSpir; Cogstate; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche and its affiliated company Genentech; Fujirebio; GE Healthcare; IXICO; Janssen Alzheimer Immunotherapy Research & Development; Johnson & Johnson Pharmaceutical Research & Development; Lumosity; Lundbeck; Merck & Co.; Meso Scale Diagnostics; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (https://www.fnih.org/). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. We thank S.M. Smith (University of Oxford) and D. Vidal-Piñeiro (University of Oslo) for their feedback and discussion on data analyses. Y.E.T. was supported by the Mary Lugton Postdoctoral Fellowship. A.Z. was supported by a National Health and Medical Research Council (NHMRC) grant (APP1142801 and APP118153), V.C. was supported by an NHMRC grant (APP1177370) and M.B. was supported by an NHMRC grant (APP1152623 and APP2008612).
Author information
Authors and Affiliations
Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
Ye Ella Tian, Vanessa Cropley & Andrew Zalesky
Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
Andrea B. Maier
Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore
Andrea B. Maier
Department of Human Movement Sciences, @AgeAmsterdam, Amsterdam Movement Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Andrea B. Maier
Academic Unit for Psychiatry of Old Age, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
Nicola T. Lautenschlager
NorthWestern Mental Health, Royal Melbourne Hospital, Melbourne, Victoria, Australia
Nicola T. Lautenschlager
Discipline of Psychiatry, College of Health, Medicine and Wellbeing, The University of Newcastle, Newcastle, New South Wales, Australia
Michael Breakspear
School of Psychological Sciences, College of Engineering, Science and Environment, The University of Newcastle, Newcastle, New South Wales, Australia
Michael Breakspear
Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Victoria, Australia
Andrew Zalesky
- Ye Ella Tian
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- Vanessa Cropley
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- Andrea B. Maier
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- Nicola T. Lautenschlager
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- Michael Breakspear
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- Andrew Zalesky
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Contributions
Conceptualization was the responsibility of Y.E.T., A.Z., V.C. and M.B. Methodology was the responsibility of Y.E.T., A.Z. and M.B. Investigation was carried out by Y.E.T. and A.Z. Visualization was carried out by Y.E.T. and A.Z. Project administration was performed by Y.E.T. Supervision was the responsibility of A.Z. and V.C. Writing of the original draft was carried out by Y.E.T. and A.Z. and review and editing was conducted by Y.E.T., A.Z., V.C., M.B., A.B.M. and N.T.L.
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Correspondence toYe Ella Tian orAndrew Zalesky.
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Extended data
Extended Data Fig. 1 Age prediction accuracy.
Scatter plots show associations between chronological and predicted age for prediction models based on body and brain phenotypes as well as phenotypes pertaining to each individual organ system. Lines of best fit indicated with solid black lines. r: Pearson correlation coefficients; MAE: mean absolute error.
Extended Data Fig. 2 Replication of predictive models for brain gray matter age.
(a) Scatter plots show associations between chronological age and predicted age for prediction model based on brain gray matter phenotypes in a combined group of healthy individuals from the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing (AIBL, n = 396, 154 males), the Alzheimer’s Disease NeuroImaging Initiative (ADNI, n = 467, 192 males) and the UK Biobank (n = 7,922, 3,624 males). Lines of best fit indicated with solid black lines. n: training sample size; r: Pearson correlation coefficients; MAE: mean absolute error. (b) Scatter plots show associations between gray matter feature weights estimated from the original age prediction model (primary) and the re-trained model using the replication cohort. Lines of best fit indicated with solid black lines. r: Pearson correlation coefficients. (c) Gray matter age (that is, age gap) in individuals diagnosed with mild cognitive impairment (MCI, n = 780, mean age gap=1.07 ± 4.25 years) and dementia (n = 284 mean age gap=3.19 ± 6.13 years), compared to healthy individuals (HC). The mean age gap significantly differs across the three groups (F-statistic=157.49,p = 4.71 × 10−68, two-sided). Asterisks indicate significant between-group differences, adjusting for chronological age and sex (MCI vs HC,t = 10.39,p = 3.56 × 10−25; dementia vs HC,t = 16.94,p < 2.23 × 10−308; MCI vs dementia:t = 10.76,p = 1.11 × 10−25). The bottom and top edges of the boxes indicate the 25th and 75th percentiles of the distribution, respectively. The central line indicates the median. The whiskers extend to the most extreme data points that are not considered outliers (1.5-times the interquartile range).
Extended Data Fig. 3 Synchrony among organ-specific age gaps.
(a) Synchrony in biological ages between each pair of body systems at baseline assessment was estimated using partial correlation, adjusting for sex and chronological age. Correlation coefficients of significant pairs of correlations (p < 0.002, two-sided, t-test, Bonferroni-corrected for 21 pairs) are indicated in the matrix (left) and also visualized as a graph (right). In the graph, each node represents one of the 7 body organs and the edges between them indicate correlations. Edge thicknesses are proportional to correlation coefficients. Edges are suppressed for small effect sizes (|r|<0.05) (b) Same as (A) but shows the correlations at follow-up assessment. Body systems can be differentiated into two groups based on interorgan synchrony in age gaps (Group I: renal, hepatic, musculoskeletal; Group II: pulmonary, cardiovascular, metabolic, immune). (c) & (d) Same as (A) & (B) but the synchrony in age gaps is shown for different brain systems at baseline and follow-up assessment respectively. Biological age is most strongly synchronized between white and gray matter, whereas functional connectivity is only weakly synchronized with other brain systems (Bonferroni-corrected for 3 correlations, p < 0.017, two-sided). GM, gray matter; WM, white matter; FC, functional connectivity. Ward’s linkage clustering was used to determine the reordering and the cluster tree shown.
Extended Data Fig. 4 Relationship between chronic disease and organ-specific biological age.
(a) A clock face represents the extent of body aging for 16 disease categories. Body age is older (younger) in a clockwise (anticlockwise) direction, with a body age gap of zero at the 12 o’clock position. Bar plot shows the mean body age gap in each disease, sorted from the smallest to the largest value. (b) Same as panel (A) but shows the mean brain age gap across disease. Dashed arm indicates replication dementia cohort. (c) Word-cloud representation. The font size was normalized according to the mean age gap across the 16 disease groups within each organ system. Diseases for which organs appear older than chronological age (gap>0) are colored black, whereas diseases for which organs appear younger (gap<0) are colored blue. COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease. Organ image was created with BioRender.com.
Extended Data Fig. 5 Effect size of body and brain age in chronic disease.
Effect sizes of differences in organ-specific age gaps between each disease category and healthy comparison group were quantified using the Cohen’sd. The Cohen’s d value was multiplied by the sign of the mean between-group difference in age gap. Icons representing body systems and organs are positioned to indicate the effect size for each disease category. Icons are not shown for organs with mean age gaps that do not significantly differ from zero (p < 2.6 × 10−4, two-sided, t-test, Bonferroni corrected, Fig.4a). Disease categories are ordered from top to bottom according to increased mean body age gaps as shown in Fig.4b.
Extended Data Fig. 6 Disease comorbidity.
(a) Bar plots show the number of lifetime comorbid diagnoses for individuals who completed assessment of body (left) or, brain (middle) function and all individuals (right). (b) Comorbidity network for females. The Pearson correlation coefficient was used to quantify the extent of lifetime comorbidity between each pair of disease categories. Permutation testing (n = 10,000) was used to estimate p-values and significant correlations were Bonferroni corrected for (16 × 15)/2 = 120 disease pairs (p < 4.2 × 10−4, one-sided). Non-significant correlations were suppressed from the correlation matrix (left) and the network graph (right). Edge thickness is modulated by correlation coefficients. (c) Same as (d) but for males. Also seeMethods.
Extended Data Fig. 7 Biological organ age in chronic disease at different illness stages.
(a) Distribution of body age gaps (columns) for 16 disease categories (rows) in individuals at prodromal stage, compared to healthy individuals (HC, first row). Distributions are colored according to disease- and organ-specific mean age gaps. Colored distributions have a mean that significantly differs from the healthy group (p < 3.9 × 10−4, two-sided, t-test, Bonferroni corrected for 16 disease categories × 8 body systems = 128 tests). Distributions colored gray have a mean that is not significantly different from the healthy group. (b) Same as (A) but in individuals with established diagnosis. Prodromal groups for brain imaging data were insufficient to investigate the impact of disease progression on brain age.
Extended Data Fig. 8 Survival time and premature death prediction.
A logistic regression model was trained (10-fold cross-validation) to predict an individual’s 5- and 10-year survival (left) and premature death (defined as death before 70 or 75 years old, right). Boxplots show prediction accuracy, as quantified with area under curve (AUC). A hierarchy of six logistic models was established to determine the extent to which biological age improves prediction of survival time and premature death above and beyond established predictors (that is, chronological age, sex, diagnoses, lifestyle factors). For prediction of both survival time and premature death, the model including body age gaps (Model 2) significantly outperforms the model including only chronological age and sex (Model 1, 5-year/10-year: p = 4.55 × 10−133/p = 1.15 × 10−141; 70 years old/75 years old: p = 3.40 × 10−77/p = 1.19 × 10−87, two-sided, t-test). Similarly, the model including body age gaps (Model 4) significantly outperforms the model with only chronological age, sex and existing diagnoses (Model 3, 5-year/10-year: p = 3.52 × 10−58/p = 2.02 × 10−88; 70 years old/75 years old: p = 9.44 × 10−47/p = 1.56 × 10−54, two-sided, t-test). Nevertheless, Model 5, which includes all predictors, achieves the most accurate predictions of survival time (5-year: AUC = 0.774 ± 0.006; 10-year: AUC = 0.770 ± 0.003) and premature death (70 years old: AUC = 0.86 ± 0.003; 75 years old: AUC = 0.86 ± 0.003). Omitting body age gaps (Model 6) leads to significantly reduced accuracy (5-year/10-year: p = 4.43 × 10−29/p = 4.89 × 10−40; 70 years old/75 years old: p = 1.86 × 10−42/p = 4.31 × 10−46, two-sided, t-test) for predictions of survival time (5-year: AUC = 0.76 ± 0.005; 10-year: AUC = 0.76 ± 0.003) and premature death (70 years old: AUC = 0.85 ± 0.003; 75 years old: AUC = 0.85 ± 0.003). Confidence intervals for AUC estimated with bootstrapping (100 samples). The bottom and top edges of the boxes Indicate the 25th and 75th percentiles of the distribution, respectively. The central line indicates the median. The whiskers extend to the most extreme data points that are not considered outliers (1.5-times the interquartile range).
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Tian, Y.E., Cropley, V., Maier, A.B.et al. Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality.Nat Med29, 1221–1231 (2023). https://doi.org/10.1038/s41591-023-02296-6
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