Frailty and Biological Age

Abstract

A reliable model of biological age is instrumental in the field of geriatrics and gerontology. This model should account for the heterogeneity and plasticity of aging and also accurately predict aging-related adverse outcomes. Epigenetic age models are based on DNA methylation levels at selected genomic sites and can be significant predictors of mortality and healthy/unhealthy aging. However, the biological function of DNA methylation at selected sites is yet to be determined. Frailty is a syndrome resulting from decreased physiological reserves and resilience. The frailty index is a probability-based extension of the concept of frailty. Defined as the proportion of health deficits, the frailty index quantifies the progression of unhealthy aging. The frailty index is currently the best predictor of mortality. It is associated with various biological factors and provides insight into the biological processes of aging. Investigation of the multi-omics factors associated with the frailty index will provide further insight.

Keywords: Aging; DNA Methylation; Frailty; Gene expression; Healthy aging.

Fig. 1.

A bar plot of mean DNA methylation ages and standard errors. This plot was generated using the data from eight autistic subjects available in the MethylationDataExample55 and SampleAnnotationExample55 datasets provided.16) DNA methylation age was calculated in R using the tutorial provided. The y-axis shows mean DNAmAge±standard error corresponding to three groups on the x-axis: the autistic subjects (“initial”), after adding 0.1 to the original beta values of 353 CpGs (“+.1_all”), or subtracting 0.1 from the original beta values of 353 CpGs (“-.1_all”).

Fig. 2.

A Venn diagram of three sets of genes. SH_353 represents genes annotated for 353 CpGs used in the DNA methylation age model.16) GH_54 represents genes annotated for 71 CpGs used to determine the apparent methylomic aging rate.15) MS.VG_1000 represents top 1,000 genes whose transcript levels are important in predictive modeling of chronological age.37)

Fig. 3.

Exponential increase in the frailty index and mortality. (A) Box plot of FI28 scores of 592 individuals in the Louisiana Healthy Aging Study.51) FI28 is a frailty index based on 28 health items. Each box represents an inter-quartile range with the line in the middle showing the median position. (B) Proportions of the deceased in the same age groups.

Fig. 4.

Effect sizes (Z-scores) of mortality predictors in Cox proportional hazards regression analysis of 262 Louisiana Healthy Aging Study subjects. (A) Z scores of the three covariates (+sex) included in a Cox regression model for all the subjects aged 60–103 (all) and Z scores of the same covariates included in the Cox regression for the subjects aged 90–103 (old). DNAmAge is the DNA methylation age calculated according to Horvath.16) FI34 is a frailty index based on 34 health items.73) (B) Z scores of the four covariates included in the Cox regression for all the subjects aged 60–103 (all) and Z scores of the same covariates included in the Cox regression for the subjects aged 90–103 (old). BEC28 is the Klemera-Doubal biological age estimate using 28 health items.51,53) FI28 is the frailty index based on the same 28 health items.51,53) *p≤0.05, **p≤0.01, ***p≤0.001.