Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality

Cropley, Vanessa; Maier, Andrea B.; Lautenschlager, Nicola T.; Breakspear, Michael; Zalesky, Andrew

doi:10.1038/s41591-023-02296-6

Cited by 217 publications

(132 citation statements)

References 102 publications

(119 reference statements)

Supporting

Mentioning

118

Contrasting

Unclassified

Order By: Relevance

“…Our results support the concept that CKD promotes accelerated ageing, and recent studies report an association between poor renal function and age acceleration [31][32][33][34]. Indeed, CKD patients had the highest age acceleration in a study comparing 16 chronic diseases [34].…”

Section: Discussionsupporting

confidence: 90%

“…As the uremic milieu promotes accelerated biological ageing, it is of interest to establish whether KRT slows down or reverses accelerated ageing. Identification of a robust biomarker for biological ageing may help nephrologists identify CKD patients that are on 'fast track ageing' and tailor the most appropriate nutritional, lifestyle and therapeutic interventions [34,35]. Our study suggests that neither PA nor SAF alone are suitable biomarkers of ageing in advanced kidney disease, as they produce implausibly high estimates of biological age.…”

Section: Discussionmentioning

confidence: 88%

See 1 more Smart Citation

Epigenetic clocks indicate that kidney transplantation and not dialysis mitigate the effects of renal ageing

Neytchev,

Erlandsson,

Witasp

et al. 2023

J Intern Med

View full text Add to dashboard Cite

BackgroundChronic kidney disease (CKD) is an age‐related disease that displays multiple features of accelerated ageing. It is currently unclear whether the two treatment options for end‐stage kidney disease (dialysis and kidney transplantation [KT]) ameliorate the accelerated uremic ageing process.MethodsData on clinical variables and blood DNA methylation (DNAm) from CKD stage G3–G5 patients were used to estimate biological age based on blood biomarkers (phenotypic age [PA], n = 333), skin autofluorescence (SAF age, n = 199) and DNAm (Horvath, Hannum and PhenoAge clocks, n = 47). In the DNAm cohort, we also measured the change in biological age 1 year after the KT or initiation of dialysis. Healthy subjects recruited from the general population were included as controls.ResultsAll three DNAm clocks indicated an increased biological age in CKD G5. However, PA and SAF age tended to produce implausibly large estimates of biological age in CKD G5. By contrast, DNAm age was 4.9 years (p = 0.005) higher in the transplantation group and 5.9 years (p = 0.001) higher in the dialysis group compared to controls. This age acceleration was significantly reduced 1 year after KT, but not after 1 year of dialysis.ConclusionsKidney failure patients displayed an increased biological age as estimated by DNAm clocks compared to population‐based controls. Our results suggest that KT, but not dialysis, partially reduces the age acceleration.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 88%

Epigenetic clocks indicate that kidney transplantation and not dialysis mitigate the effects of renal ageing

Neytchev,

Erlandsson,

Witasp

et al. 2023

J Intern Med

View full text Add to dashboard Cite

show abstract

“…The advent of artificial intelligence (AI) has made significant progress in its ability to decipher various aspects of human brain health 2,3 , such as normal brain aging 4 , neurodegenerative disorders like Alzheimer's disease (AD) 5 , and brain cancer 6 . Utilizing magnetic resonance imaging (MRI), the AI-derived human brain age [7][8][9] has emerged as a valuable biomarker for the evaluation of brain health. More precisely, the difference between an individual's predicted brain age and their chronological age -brain age gap (BAG) -provides a means of quantifying an individual's brain health by measuring deviation from the typical aging trajectory.…”

Section: Mainmentioning

confidence: 99%

“…A) Genome-wide associations identified sixteen genomic loci associated with GM (6), WM (9), and FC-BAG (1) using a genome-wide P-value threshold [-log 10 (P-value) > 7.30]. Each locus is represented by the top lead SNP and cytogenetic region.…”

Section: Figure 2: Genome-wide Associations Of Multimodal Brain Age Gapsmentioning

confidence: 99%

The Genetic Architecture of Multimodal Human Brain Age

Wen

Zhao

Yang

et al. 2023

Preprint

View full text Add to dashboard Cite

The complex biological mechanisms underlying human brain aging remain incompletely understood. To investigate this, we utilized multimodal magnetic resonance imaging and artificial intelligence (AI) to examine the genetic heterogeneity of the brain age gap (BAG) derived from gray matter volume (GM-BAG), white matter tract (WM-BAG), and functional connectivity (FC-BAG). Sixteen significant genomic loci were identified, with GM-BAG loci showing abundant associations with neurodegenerative and neuropsychiatric traits, WM-BAG for cancer and Alzheimer's disease (AD), and FC-BAG for only insomnia. The gene-drug-disease network further corroborated these associations by highlighting genes linked to GM-BAG for the treatment of neurodegenerative and neuropsychiatric disorders, and WM-BAG genes for cancer therapy. GM-BAG showed the highest enrichment of heritability in conserved regions, while in WM-BAG, the 5' untranslated regions exhibited the highest heritability enrichment; oligodendrocytes and astrocytes showed significant heritability enrichment in WM and FC-BAG, respectively. Notably, Mendelian randomization identified risk causal effects of triglyceride-to-lipid ratio in VLDL and type 2 diabetes on GM-BAG, and AD on WM-BAG. These findings suggest that interventions targeting these factors and diseases may ameliorate human brain health. Overall, our results provide valuable insights into the genetic heterogeneity of human brain aging, with potential implications for lifestyle and therapeutic interventions.

show abstract

“…Likewise, a biological clock built using the gut microbiome (Wilmanski et al, 2021) was used to identify individuals who might be aging slower or faster than average and suggest drugs that might influence gut health. Recently, aging of separate organs has been investigated and linked to age-associated diseases and mortality (Tian et al, 2023), and biological age has been estimated using AI methods (Qiu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A mathematical model that predicts human biological age from physiological traits identifies environmental and genetic factors that influence aging

Libert

Chekholko

Kenyon

2022

Preprint

View full text Add to dashboard Cite

Why people age at different rates is a fundamental unsolved problem in biology. We created a model that predicts an individual's age, taking as input physiological traits that change with age in the large UK Biobank dataset, such as blood pressure, blood metabolites, strength, and stimulus-reaction time. The model's Root Mean Square Error of age prediction (RMSE) is less than 5 years. We argue that the difference between calculated 'biological' age and actual age (delta-Age) reflects an individual's relative youthfulness and possibly their rate of aging. Validating this interpretation, people predicted to be physiologically young for their age have a lower subsequent mortality rate and a higher parental age at death, even though no mortality data were used to calculate delta-Age. A Genome-Wide Association Study (GWAS) of delta-Age, and analysis of environmental factors associated with delta-Age identified known as well as new factors that may influence human aging, including genes involved in synapse biology and a tendency to play computer games. We identify 12 readily-measured physiological traits that together assess a person's biological age and may be used clinically to evaluate therapeutics designed to slow aging and extend healthy life.

show abstract

Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality

Cited by 217 publications

References 102 publications

Epigenetic clocks indicate that kidney transplantation and not dialysis mitigate the effects of renal ageing

Epigenetic clocks indicate that kidney transplantation and not dialysis mitigate the effects of renal ageing

The Genetic Architecture of Multimodal Human Brain Age

A mathematical model that predicts human biological age from physiological traits identifies environmental and genetic factors that influence aging

Contact Info

Product

Resources

About