Biomarker signatures of aging

Abstract

SummaryBecause people age differently, age is not a sufficient marker of susceptibility to disabilities, morbidities, and mortality. We measured nineteen blood biomarkers that include constituents of standard hematological measures, lipid biomarkers, and markers of inflammation and frailty in 4704 participants of the Long Life Family Study (LLFS), age range 30–110 years, and used an agglomerative algorithm to group LLFS participants into clusters thus yielding 26 different biomarker signatures. To test whether these signatures were associated with differences in biological aging, we correlated them with longitudinal changes in physiological functions and incident risk of cancer, cardiovascular disease, type 2 diabetes, and mortality using longitudinal data collected in the LLFS. Signature 2 was associated with significantly lower mortality, morbidity, and better physical function relative to the most common biomarker signature in LLFS, while nine other signatures were associated with less successful aging, characterized by higher risks for frailty, morbidity, and mortality. The predictive values of seven signatures were replicated in an independent data set from the Framingham Heart Study with comparable significant effects, and an additional three signatures showed consistent effects. This analysis shows that various biomarker signatures exist, and their significant associations with physical function, morbidity, and mortality suggest that these patterns represent differences in biological aging. The signatures show that dysregulation of a single biomarker can change with patterns of other biomarkers, and age‐related changes of individual biomarkers alone do not necessarily indicate disease or functional decline.

Emerging Insights into the Metabolic Alterations in Aging Using Metabolomics

“…The loss of strength, muscle tone, and skin elasticity, reduced visual acuity, flexibility, and cardiovascular stamina, as well as increased body fat are included as some examples of phenotypic biomarkers of aging, whereas examples of the molecular biomarkers of aging include the altered levels of hormones, lipids, glycosylated hemoglobin, glucose, amino acids, and cholesterol. These biomarkers are mainly used in the human population based studies to understand the physiological changes that occur with age, the process of aging, and the onset of age-related diseases [73][74][75][76].…”

The role of lipid metabolism in aging, lifespan regulation, and age‐related disease

“…Myriad types of aging biomarkers exist. They can take the form of physiological and clinical data such as white blood cell count, absolute monocyte count, blood pressure, body mass index, resting heart rate, forced expiratory volume, gait speed, and grip strength (Burkle et al, ; Sebastiani et al, ; Xia, Chen, McDermott, & Han, ). As an example of how useful one of these biomarker parameters can be, grip strength is highly predictive of mortality, morbidity, and future disability (Leong et al, ).…”

“…Efforts are currently underway to initiate a clinical trial that will utilize DNA methylation information to assess the efficacy of various antiaging interventions (Mitteldorf, ). Biomarkers can additionally manifest as molecules such as carbohydrates, apolipoproteins, glycoproteins, hormones, cytokines, and lipids (Burkle et al, ; Sebastiani et al, ; Xia et al, ). Interleukin‐6, for instance, is a pro‐inflammatory cytokine and glycoprotein that increases in concentration with age (Maggio, Guralnik, Longo, & Ferrucci, ).…”

Population-based RNA profiling in Add Health finds social disparities in inflammatory and antiviral gene regulation to emerge by young adulthood

“…Our analyses focused on two molecular pathways involved in the pathogenesis of multiple chronic diseases (16): 1) genes involved in inflammation and 2) genes involved in type I interferon (IFN) responses. These two gene sets represent functionally distinct immunoregulatory programs (18,19) and were selected for analysis based on their well-established relationship to chronic disease and longevity, both as empirical predictors (16,(20)(21)(22)(23)(24)(25)(26)(27) and as molecular mechanisms of disease (16,(28)(29)(30)(31)(32)(33). Both gene sets are subject to physiological regulation by tissue injury and microbial stimuli as well as by the neural and endocrine systems (34).…”