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Results for “aging biomarkers”

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At least 19 recordsLinked to original sources
PubMed2025

Impact of sodium-glucose cotransporter-2 inhibitors on aging biomarkers and plasma ceramide levels in type 2 diabetes: beyond glycemic control.

BACKGROUND: Aging is a complex biological process marked by the decline of physiological functions and heightened susceptibility to chronic illnesses, notably cardiometabolic disorders. Ceramides (Cer) are lipid derivatives linked to aging and metabolic diseases. Sodium-Glucose Cotransporter-2 inhibitors (SGLT2i), widely used in managing type 2 diabetes, have an unclear impact on aging biomarkers and Cer profiles. OBJECTIVE: This study explored the association between SGLT2i use, plasma Cer levels (CerC16:0, CerC18:0, CerC22:0, CerC24:0, and CerC24:1), and aging biomarkers-Human Insulin-Like Growth Factor 1 (IGF-1), mammalian target of rapamycin (mTOR), 5-Methylcytosine (5MC), and Human H2AFX (Histone H2AX) in patients with type 2 diabetes mellitus (T2DM). METHODS: In this retrospective study, 95 participants were divided into three groups: patients on SGLT2i (n&#x2009;=&#x2009;34), patients on non-SGLT2i anti-diabetic treatments (n&#x2009;=&#x2009;36), and healthy controls (n&#x2009;=&#x2009;25). Plasma Cer and aging biomarkers were quantified using Liquid Chromatography with tandem mass spectrometry (LC-MS-MS) and ELISA, respectively. Principal component analysis (PCA) assessed group-based clustering, while ANCOVA evaluated group differences with confounder adjustment. RESULTS: SGLT2i-treated patients showed significantly lower CerC16:0, CerC22:0, and CerC24:1 levels (p&#x2009;<&#x2009;0.01) and decreased 5MC and H2AX (p&#x2009;<&#x2009;0.05) compared to non-SGLT2i patients. IGF-1 was significantly elevated in the SGLT2i group (p&#x2009;<&#x2009;0.01), suggesting a possible protective effect on metabolic health. PCA distinguished control from diabetic groups but revealed overlap between SGLT2i and non-SGLT2i groups. CONCLUSION: Beyond glucose control, SGLT2i may improve plasma Cer and aging markers in diabetic patients, supporting their broader therapeutic potential in aging and age-related diseases. Further large-scale studies are warranted to confirm these effects and underlying mechanisms.

Humans↗
PubMed2026

Aptamer-Based Platforms for Human Aging Biomarkers: Multiplexed Proteomics, Biosensors and Translational Perspectives.

Aptamer-based multiplexed proteomic platforms, especially the SOMAmer-based SomaScan assay, are widely used for large-scale discovery of circulating biomarkers relevant to human aging. This review summarizes 42 original research articles published from 2020 through 2026 in which aptamers or aptamer-derived biosensors were used to characterize aging-related biomarkers in human samples or clinically relevant human-disease contexts. The eligible literature falls into several thematic areas: whole-plasma and organ-specific proteomic aging clocks; inflammaging and senescence-associated secretory phenotype (SASP) markers; cardiovascular, metabolic, renal, hepatic, musculoskeletal and neurodegenerative biomarker panels; and aptasensor platforms for detection of individual analytes. Only a small number of studies have compared aptamer- and antibody-based platforms in the same specimens; we tabulate these and show that median between-platform agreement is low to moderate, which constrains the pooling of findings across technologies. We also make explicit an interpretive point that is usually left implicit: because proteomic clocks are trained against chronological age, their correlation with chronological age measures fit to the training target rather than biological validity, and the informative quantity is the residual age gap. In the reviewed literature, SomaScan-based studies are concentrated in cardiovascular, neurodegenerative, frailty, and proteomic aging-clock research, whereas de novo SELEX campaigns targeting aging-specific epitopes and longitudinal human validation of wearable aptasensors were not identified. The main barriers to translation are cross-platform discordance, limited replication across ancestries, under-reported pre-analytical variability, cost, and the research-use-only status of most assays.

Humans↗
PubMedDate not supplied

Telomere attrition as ageing biomarker.

Telomeres, the tandem-repeated hexamers at the termini of mammalian chromosomes, form protective complexes in association with specific proteins that together with telomerase, a specialised telomere-synthesizing enzyme, regulate telomere length. Telomere shortening is associated with cellular senescence and is implicated in tumorigenesis and cancer. Hence, mean telomere length has emerged as a replicative clock within each population of cells and the tissues and organs they build up in vitro and, consequently, as a biomarker for biological ageing in vivo. Chronological ageing per se does not parallel biological ageing, yet accurate and reliable biomarkers are lacking to distinguish between them. The question remains as to whether telomere dynamics is a determinant or merely a predictor of human biological age over and above chronological ageing. Although several reports have suggested a link between telomere attrition and ageing phenotypes and disorders, both reference values and a complete set of determinants are missing. Within this review, current evidence and knowledge on telomere length and telomere erosion rates reported, are summarised.

Aging↗
PubMed2004

mRNA level of alpha-2-macroglobulin as an aging biomarker of human fibroblasts in culture.

Cellular senescence is a well-established model system for studying the molecular basis of aging. To identify a reliable biomarker for cellular age and further study the gene expression of aging, we profiled the gene expression difference between aged and young cultured human embryonic lung fibroblasts by high-density complementary deoxyribonucleic acid (cDNA) arrays. Among the differentially expressed genes, alpha-2-macroglobulin (alpha(2)M) was selected for further study. Its gene expression level as a function of population doubling level (PDL) in cultured fibroblasts was determined by RT-PCR and northern hybridization. mRNA level of alpha(2)M showed a positive linear-correlation with cumulative PDL. Additional assays revealed that the levels of alpha(2)M increased in irreversible growth arrest induced by sublethal H(2)O(2), but not in quiescent state of cultured fibroblasts induced by serum-deprivation, and remained stable in Hela cells. These results suggest that mRNA level of alpha(2)M can be used as a biomarker of aging in cultured fibroblasts. mRNA level of alpha(2)M showed significant difference between newborn and old human leucocytes, which suggest that the mRNA level of alpha(2)M may be used as a biomarker of aging in vivo.

Biomarkers↗
PubMed1998

Mortality and biomarkers of aging in heterogeneous stock (HS) mice.

A longitudinal study was undertaken to evaluate the relationships among a battery of aging biomarkers and subsequent survival time in 319 genetically heterogenous stock (HS) mice. The biomarker variables chosen were selected from the broad domains of behavior, homeostatic physiology, oxidative defense, and immune function; biomarkers were measured at 45, 90, 360, 630, and 900 days of age. Sex differences were found in the survivor and mortality functions, with a mortality rate crossover occurring at about 525 days and a survival curve crossover at about 750 days of age. Females experienced lower initial mortality but had more sharply increasing mortality with age than did males. Survival analysis using Gompertz parametric models with biomarkers as time-varying covariates yielded significant biomarkers from each domain. Following backward elimination procedures, the final set of independent mortality predictors included headpokes in the File activity apparatus, maximum cord drop time, weight, hematocrit, urine concentration, natural killer cell activity, and concanavalin A response.

6-Ketoprostaglandin F1 alpha↗
PubMed2004

Current status of efforts to measure and modulate the biological rate of aging.

Biomarkers of aging would be highly desirable, but so far, a definitive panel of biomarkers to predict mortality risk has not been obtained, even though many traits that vary with age have been identified. This lack hinders the search for interventions that may retard the rate of aging in mammals. The recent discovery and characterization of many longevity genes in animal model systems, such as nematodes, fruit flies, and mice, are providing new targets for research by providing insight into mechanisms of longevity regulation in these model systems. It is hoped that this will ultimately lead to interventions to delay the development of age-related pathology in humans.

Aging↗
PubMed2026

Identifying biomarkers of accelerated ageing in cancer patients from routine clinical data.

INTRODUCTION: Cancer and ageing have a bidirectional relationship: age is the strongest risk factor for cancer, and cancer and treatments can accelerate ageing. Therefore, biological age can differ from chronological age; biomarkers are needed to stratify interventions to minimise accelerated ageing. METHODS: PhenoAge was calculated from routine blood test results of patients attending a Geriatric Oncology clinic. PhenoAgeAccel was the residual from a regression of PhenoAge against age. RESULTS: Data were available for 173 patients (62% male). Mean PhenoAge was higher than age (84.3 (12.6) vs 76.2 (7.24), p&#x202f;<&#x202f;0.001), though the two were correlated (r&#x202f;=&#x202f;0.579, p&#x202f;<&#x202f;0.001). Unlike age, PhenoAge and PhenoAgeAccel were associated with one-year mortality (PhenoAge OR=1.083, 95% CI: 1.038-1.136; PhenoAgeAccel OR=1.096, 95% CI: 1.047-1.155). PhenoAge correlated with Clinical Frailty Score and Timed Up and Go (CFS: Rs=0.31, p&#x202f;<&#x202f;0.001; TUG: Rs=0.25, p&#x202f;<&#x202f;0.005); there were no correlations with age. PhenoAgeAccel correlated with the number of CGA interventions made (Rs=0.17, p&#x202f;<&#x202f;0.05), unlike age and PhenoAge. Patients with diabetes mellitus had a higher PhenoAgeAccel compared to those without (3.40 vs -1.71, p&#x202f;=&#x202f;0.002). In patients receiving systemic anti-cancer treatment, patients with PhenoAgeAccel calculated pre-treatment had less age acceleration than those with PhenoAgeAccel calculated post-treatment, both overall (2.18 vs -2.87; p&#x202f;=&#x202f;0.048) and in matched samples (n&#x202f;=&#x202f;21, 7.76 vs -2.87, p&#x202f;<&#x202f;0.001). CONCLUSIONS: PhenoAgeAccel is a greater predictor of risk than chronological age in older people with cancer. This makes it a promising biomarker to stratify patients for holistic geriatric assessment, dose reductions, or future geroprotective measures which could be integrated within electronic healthcare record systems.

Humans↗
PubMedDate not supplied

Evaluating measures of hematology and blood chemistry in male rhesus monkeys as biomarkers of aging.

Reliable and valid biomarkers of aging can provide valuable tools for examining the effectiveness of interventions that may influence the rate of aging processes. However, a standardized method for identifying biomarkers of aging has yet to be developed. The current analysis focused on hematology and blood chemistry variables obtained from a 5-year longitudinal study of male rhesus monkeys (N = 29) on a diet restriction regime known to retard aging processes and extend lifespan in laboratory rodents (70% of the diet intake of controls). For the current analysis, the major screening criteria for identifying candidate biomarkers of aging were cross-sectional and longitudinal correlation with chronological age (CA) and stability of individual differences. Six potential variables from the battery of blood chemistry tests were identified: 1) serum glutamic oxalacetic transaminase; 2) alkaline phosphatase; 3) total protein; 4) globulin; 5) blood urea nitrogen to creatinine ratio; and 6) phosphates. When submitted to principle component analysis, these variables loaded onto a single component that accounted for over 50% of the total variance to indicate marked covariance among them. By applying the factor score coefficients from the first principle component, an equation was derived for estimating a biological age score (BAS) for each individual monkey. A comparison of BAS between control and diet-restricted monkeys revealed no statistically significant difference at present; however, the slope of the regression of BAS onto CA appeared steeper for the control group compared to the experimental group. Thus, while demonstration of the validity of the candidate biomarkers awaits further evidence, a strategy by which additional biomarkers of aging can be identified is proposed as an improvement over past approaches.

Aging↗
PubMed2006

Biomarkers of aging: combinatorial or systems model?

Systemwide functional and structural changes caused by the aging process encourage the implementation of new bioinformatics search strategies for markers of aging. Combinatorial biomarkers should be particularly favored, as they can quantify processes on multiple levels of biological organization and overcome an otherwise limited ability to access heterogeneities in populations. An even more challenging but rational approach is the development of systems biology models to describe molecular pathways and key networks mechanistically as they relate to age. Such reverse engineered models not only indicate critical and diagnostic components (that is, potential biomarkers) but also should be able to predict the progression of aging through computer simulation.

Aging↗
PubMed1988

Biomarkers of aging.

This article presents a conceptual discussion of some aspects involved in biomarkers of aging. A biomarker of aging is a biological parameter of an organism that either alone or in some multivariate composite will, in the absence of disease, better predict functional capability at some late age than will chronological age. The reasons for undertaking biomarker research, criteria for putative biomarkers, measurement and assessment of putative biomarkers, and the new initiative by the National Institute on Aging in biomarker research are discussed.

Aging↗
PubMed2004

The search for biomarkers of aging: next stop INK4a/ARF locus.

Although several biomarkers of aging have been described in the literature, it is only recently that gerontologists have started to search for molecular biomarkers of aging. A gene or a set of genes that are expressed in a wide range of tissues and exhibit an age-dependent, easily quantifiable increase in their expression represent a possible molecular biomarker of aging. Because the physiology of an organism is profoundly affected by the pattern of gene expression, it is hoped that molecular biomarkers of aging will more accurately predict the physiological age of an organism than the chronological age. A recent report from Sharpless's laboratory examines the possibility that the tumor suppressors p16 and ARF (encoded by the INK4a/ARF locus) represent molecular biomarkers of aging in rodent models.

Amyloid beta-Peptides↗
PubMed2005

Accumulation of dolichol in older tissues satisfies the proposed criteria to be qualified a biomarker of aging.

Criteria for defining biomarkers have been suggested. Accumulation of dolichol in tissues of older animals meets the following criteria: (a) levels of dolichol exhibit a quantitative correlation with age in all tissues and are not altered by several age-dependent diseases in the same direction as that of aging; (b) accumulation is not secondary to metabolic changes of aging and is altered appropriately by factors that modulate the aging rate like caloric restriction and physical exercise; (c) biomarker is applicable to different tissues across mammalian species, including humans, and to trisomy 21 and its hypothalamic digoxin-mediated model. Reliable changes in tissue dolichol levels are seen in relatively short intervals of time compared to over a life span, and levels can be tested on a small amount of tissue without causing death of the animal. In this article, we show applications to the study of host-graft interaction and detection of gender-related differences in biological age, and we discuss mechanism(s) of accumulation.

Age Factors↗
PubMed2004

Biomarkers of aging: from primitive organisms to humans.

Leading biologists and clinicians interested in aging convened to discuss biomarkers of aging. The goals were to come to a consensus, construct an agenda for future research, and make appropriate recommendations to policy makers and the public-at-large. While there was not total agreement on all issues, they addressed a number of questions, among them whether biomarkers can be identified and used to measure the physiological age of any individual within a population, given emerging information about aging and new technological advances. The hurdles to establishing informative biomarkers include the biological variation between individuals that makes generalizations difficult; the overlapping of aging and disease processes; uncertainty regarding benign versus pathogenic age-related changes; the point at which a process begins to do damage to the organism, and, if so, when does it occur; and when to distinguish critical damage from noncritical damage. Finally, and significantly, it is difficult to obtain funding for this research.

Aged↗
PubMed1998

Age-dependent accumulation of dolichol in rat liver: is tissue dolichol a biomarker of aging?

Dolichols are long hydrophobic molecules broadly distributed in all tissues and cellular membranes of eukariotic cells. Dolichol affects membrane structure and fluidity, membrane-associated protein activities, and membrane sensitivity to oxidative stress. Reports have shown that dolichols exhibit a remarkable (6- to 30-fold) age-related increase in the tissues of adult and mature rats and of old flies, mice, and humans. In our longitudinal study, the age-related accumulation of dolichol was monitored in the liver tissue of male Sprague Dawley rats fed ad libitum up to age of 27 months. In addition 24-month-old rats subjected to different regimens of anti-aging diet restriction (40% calorie restriction or every-other-day feeding ad libitum) were tested. A parallel study of the accumulation of carbonyl in liver protein (a proposed biomarker of aging) was made. In addition, the age-related decline of liver autophagy/proteolysis was studied in isolated liver cells, in view of the essential role of this function in liver membrane maintenance. Results show that an age-dependent accumulation of dolichol can be observed in the liver of the rats fed ad libitum but not in the liver of 24-month-old food-restricted rats, that accumulation of dolichol precedes the accumulation of altered liver proteins, and that dolichol accumulation is accompanied by a decline in liver autophagy. It is concluded that dolichol accumulation satisfies the proposed primary and secondary applicable criteria and the desirable features required to be qualified as a biomarker of aging.

Aging↗
PubMed1990

Biomarkers of aging: do we know what to look for?

The identification of specific biomarkers of aging would be an important milestone in gerontologic research. In this communication, the goals of identifying biomarkers of aging are summarized and some criteria for defining biomarkers are suggested. An age-related alteration in a biological parameter is not necessarily a biomarker of aging. None of the previously observed age-related changes satisfies all the criteria. Potential biomarkers that are applicable to human aging include in vitro proliferative capacity of fibroblasts, glycation of collagen, and DNA unwinding rate. Future research should focus on identifying age-related changes that are not merely expressions of aging, but also have some causal link to aging.

Aging↗
PubMed1990

Biomarkers of aging.

This paper summarises a conceptual discussion of some aspects of biomarkers of aging and the logical strategy for developing them. A biomarker of aging is a biological parameter of an organism that either alone or in some multivariate composite will, in the absence of disease, better predict functional capability at some late age than with chronological age. The criteria for putative biomarker measurement and assessment of putative biomarkers are discussed.

Aged↗