A catalogue of omics biological ageing clocks reveals substantial commonality and associations with disease risk

Abstract

Biological age (BA), a measure of functional capacity and prognostic of health outcomes that discriminates between individuals of the same chronological age (chronAge), has been estimated using a variety of biomarkers. Previous comparative studies have mainly used epigenetic models (clocks), we use ~1000 participants to create eleven omics ageing clocks, with correlations of 0.45-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 94% of the variance in chronAge being shared among clocks. The difference between BA and chronAge – omics clock age acceleration (OCAA) – often associates with health measures. One year’s OCAA typically has the same effect on risk factors/10-year disease incidence as 0.46/0.45 years of chronAge. Epigenetic and IgG glycomics clocks appeared to track generalised ageing while others capture specific risks. We conclude BA is measurable and prognostic and that future work should prioritise health outcomes over chronAge.

An atlas of genetic scores to predict multi-omic traits

“…Following internal validation of the genetic scores, we performed external validation of SomaScan protein targets in the FENLAND study 19 ; Olink proteins in the Northern Swedish Population Health Study (NSPHS) 20,21 and the Orkney Complex Disease Study (ORCADES) 22,23 ; Metabolon metabolites in ORCADES 23 ; Nightingale metabolic traits in UK Biobank (UKB) 24,25 , Viking Health Study Shetland (VIKING) 26 and ORCADES 23 studies ( Figure 1 and Table 1 ). For Metabolon metabolites and Illumina RNAseq transcripts, we performed further validation in withheld sets of INTERVAL ( Methods ).…”

“…The UK Biobank, ORCADES and the VIKING health study 26 were used as external cohorts to validate genetic scores of Nightingale traits, and traits identifiers provided in the platform were used to successfully match all 141 traits between these studies and INTERVAL. Quality control for these traits in each external cohort has been described previously in details 23,24 . Before validation, levels of these traits were adjusted for sex, age, BMI, sampling season and sampling year, genotyping array and top 20 genetic PCs in ORCADES, VIKING; in UKB, they were adjusted for sex age, BMI, use of lipid lowering medication, top 10 genetic PCs and technical variance following the protocol of the previous study 24 .…”

“…ORCADES also used the same platform Metabolon HD4 as INTERVAL to measure plasma metabolites of participants, and we used COMP identifier in the platform to match metabolites between the two studies, which resulted in 455 overlapped traits. Detailed quality control steps for metabolites in ORCADES were described in the previous study 23 and their trait levels were adjusted for covariates of sex, age, BMI, sampling season and year, plate number, plate column, plate row, genotyping array and top 20 PCs. The UK Biobank, ORCADES and the VIKING health study 26 were used as external cohorts to validate genetic scores of Nightingale traits, and traits identifiers provided in the platform were used to successfully match all 141 traits between these studies and INTERVAL.…”

Associations of four biological age markers with child development: A multi-omic analysis in the European HELIX cohort

“…More recently, high-throughput 'omic' methods, which provide simultaneous quantification of thousands of epigenetic marks, transcripts, proteins and metabolites, have been used to develop 'biological clocks' that provide a global measure of changes with age at the molecular level [4]. While biological clocks have been primarily trained on chronological age, "age acceleration", commonly defined as the difference between clock-predicted age and chronological age, has been associated with age-related phenotypes and mortality [5][6][7][8][9][10][11], indicating their utility as biological age markers. DNA methylation-based clocks, such as the clock of Horvath [12], have been extensively applied in large-scale studies and remain a research field under active development [13,14].…”

Human age reversal: Fact or fiction?

“…Recent work by Macdonald‐Dunlop et al performed a thorough comparison of 15 different omics‐based clocks. The authors found that, while some clocks correlated well with specific disease risk factors (e.g., systolic blood pressure and cortisol), others were more prognostic of age‐related disease incidence and appeared to better reflect the generalized effects of aging (Macdonald‐Dunlop et al, 2022). Moreover, there is an interesting relationship between accuracy and usability.…”