Using different molecular omics data, spanning 2-3 years and 106 healthy individuals a study looked into personal aging markers and pathway enrichment analysis to make suggestions of different ‘ageotypes’ existing at an individual level.
omics: transcriptomics on peripheral blood mononuclear cells, proteomics/metabolomics on plasma, cytokine assays on serum, nasal and gut microbiome using 16S RNA sequencing.
age range: 29-75, years of age median 55.74 years.
number of participants per study type: 106 healthy individuals for cross-sectional snapshot analysis and 2-3 years with quarterly visits, only 43 individuals for the longitudinal analysis up to 4 years to use pathway enrichment analysis extra to find ‘ageotypes’.
‘ageotpyes’: immunity, kidney, liver, metabolic-inflammatory.
Good mainstream coverage of the study: Our body systems age at different rates, study finds, pointing to personalized care to extend healthy life, STATNews
Original paper in Nature Medicine, unfortunately closed access: Personal aging markers and ageotypes revealed by deep longitudinal profiling
3 Comments on the study:
Comment #1. There is no discussion in the paper on linking the accepted Hallmarks of aging framework (9 molecular and cellular hallmarks) to the 4 ageotypes and other signals suggested.
Comment #2: Paper has been submitted March, 2019, so it could not reflect to the Undulating changes in human plasma proteome profiles across the lifespan study, published by a lab at the same university, providing the long expected validation of proteomics data delivering deep aging signatures using data from 4000+ individuals in the 18-95 age range.
Comment #3: Although the Data Availability section of the paper lists several links, the data is not easily accessible, making DIY bioinformatics re-analysis of the data complicated. This would be needed for instance to compare data from this paper to the paper mentioned in the previous comment.