An ELOVL2-Based Epigenetic Clock for Forensic Age Prediction: A Systematic Review

Paparazzo, Ersilia; Lagani, Vincenzo; Geracitano, Silvana; Citrigno, Luigi; Aceto, Mirella Aurora; Malvaso, Antonio; Bruno, Francesco; Passarino, Giuseppe; Montesanto, Alberto

doi:10.3390/ijms24032254

Cited by 12 publications

(4 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the paradox of biomarkers mentioned above, it is highly unlikely that the same model could combine both these properties. Yet, the good predictors of chronological age are believed to be useful in forensics [101] or data labeling, where the chronological age information is lacking. We chose median absolute aging acceleration ( Med ( | Δ | )), a full equivalent of median absolute error, for testing clock performance.…”

Section: Methodsmentioning

confidence: 99%

ComputAgeBench: Epigenetic Aging Clocks Benchmark

Kriukov,

Efimov,

Kuzmina

et al. 2024

Preprint

View full text Add to dashboard Cite

The success of clinical trials of longevity drugs relies heavily on identifying integrative health and aging biomarkers, such as biological age. Epigenetic aging clocks predict the biological age of an individual using their DNA methylation profiles, commonly retrieved from blood samples. However, there is no standardized methodology to validate and compare epigenetic clock models as yet. We propose ComputAgeBench, a unifying framework that comprises such a methodology and a dataset for comprehensive benchmarking of different clinically relevant aging clocks. Our methodology exploits the core idea that reliable aging clocks must be able to distinguish between healthy individuals and those with aging-accelerating conditions. Specifically, we collected and harmonized 66 public datasets of blood DNA methylation, covering 19 such conditions across different ages and tested 13 published clock models. We believe our work will bring the fields of aging biology and machine learning closer together for the research on reliable biomarkers of health and aging.Code:https://github.com/ComputationalAgingLab/ComputAgeDataset:https://huggingface.co/datasets/computage/computage_bench

show abstract

Section: Methodsmentioning

confidence: 99%

ComputAgeBench: Epigenetic Aging Clocks Benchmark

Kriukov,

Efimov,

Kuzmina

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The ELOLV2 single locus age prediction model has been reported by several studies. [16][17][18][19][20] ELOVL2 methylation of a reference DNA sample was measured on separate days to control for between-day variations. The between-day coefficient of variation was less the 5% for all CpG loci (Table S2).…”

Section: Methods Of Measuring Elovl2 Gene Methylationmentioning

confidence: 99%

“…Per cent methylation at each CpG locus was calculated using the Pyro Q CpG software. The ELOLV2 single locus age prediction model has been reported by several studies 16–20 …”

Section: Methodsmentioning

confidence: 99%

ELOVL2‐methylation and renal and cardiovascular event in patients with chronic kidney disease

Obeid,

Rickens,

Heine

et al. 2023

Eur J Clin Investigation

View full text Add to dashboard Cite

BackgroundMethylation of the Elongation Of Very Long Chain Fatty Acids‐Like 2 (ELOVL2) gene promoter may predict premature ageing and cardiovascular risk.MethodsWe studied the cross‐sectional associations between blood ELOVL2‐methylation and cardiovascular risk factors in 350 patients with chronic kidney disease (CKD) stage G2–G4 aged between 22 and 90 years. In a follow‐up study for a mean of 3.9 years, we investigated the association between baseline ELOVL2‐methylation and renal or cardiovascular events including death.ResultsELOVL2‐methylation at seven CpG cites increased with age (the correlation coefficients between 0.67 and 0.87, p < 0.001). The ELOVL2‐CpGs methylation was lower in patients with CKD stage G2 versus those in stage G3a, G3b and G4, but the differences were explained by age. ELOVL2‐CpGs methylation showed no correlations with cardiovascular risk factors after adjusting for age. During the follow‐up, 64 patients showed deterioration in renal function or died and 77 showed cardiovascular events or died. The hazard ratio and 95% confidence intervals for renal or cardiovascular events according to baseline ELOVL2‐CpGs methylation were not significant after adjustment for covariates.ConclusionsELOVL2‐hypermethylation showed a strong association with age, but was not independently associated with cardiovascular risk factors or with future renal or cardiovascular events in patients with CKD. ELOVL2 gene methylation is not likely to be itself a cause for ageing or illnesses, but it could be rather influenced by other upstream processes that deserve investigation.

show abstract

“…Similarly, biological age prediction could be used to monitor therapeutic success in patients subjected to such aging-preventive interventions. And finally, accurate prediction of chronological age has applications beyond biological age estimation, such as in forensic science [ 7 , 8 ], in the resolution of legal disputes [ 9 ], or in anthropological and ecological studies when the chronological age of individuals is unknown [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis

Salignon

Faridani

Miliotis

et al. 2023

Aging

View full text Add to dashboard Cite

Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R 2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.

show abstract

An ELOVL2-Based Epigenetic Clock for Forensic Age Prediction: A Systematic Review

Cited by 12 publications

References 67 publications

ComputAgeBench: Epigenetic Aging Clocks Benchmark

ComputAgeBench: Epigenetic Aging Clocks Benchmark

ELOVL2‐methylation and renal and cardiovascular event in patients with chronic kidney disease

Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis

Contact Info

Product

Resources

About