Editorial: The Quest for Biomarkers of Biological Age and Longevity Identifies Roles for Small Noncoding RNAs, Including PIWI-Interacting RNAs

More than 120 years ago, at the turn of the 20th century, life expectancy in developed countries ranged from 45 to 50 years, with women outliving men [1]. In the US, up to 20% of all individuals born in 1900 died before reaching the age of 10 years, mainly due to infectious diseases, and the common diseases of old age that are recognized today were mainly unknown [1,2]. Since 1900, longevity, measured by cohort life expectancy, has risen steadily in high-income countries, leading to the expectation that this trend will continue [1,2]. Recently, Andrade and colleagues applied multiple mortality forecasting methods to estimate cohort life expectancy for individuals born in 23 high-income countries between 1939 and 2000 [2]. All forecasting methods indicated a deceleration in cohort life expectancy, primarily driven by a slower pace of mortality improvement at very young ages (under 5 years) and in the under-20s [2].

Chronological age plays a major role in medical decision-making but has limitations. Chronological age is often used as a proxy for non-quantifiable variables, including physical decline, accumulated injury, and potential for recovery, leading to the overlooking of important physiological variability [3]. However, biological aging involves variable changes in cells, tissues, and organs across individuals, potentially affecting molecular processes, including mitochondrial and immune changes, vascular changes, epigenetic drift, and metabolic changes that progress at varying rates [4]. Because identical twins can age biologically at different speeds, and disease history modulates the rates of aging, resulting in divergent age trajectories that could be modified with interventions [4]. Biological factors may predict outcomes more strongly than chronological age, identifying older adults with preserved physiological reserve and younger adults who may experience more rapid functional decline [4]. Recently, studies in acute care settings have shown that physiological reserve (a measure of biological age) correlates more closely with in-hospital recovery than chronological age [5]. Therefore, reliable biomarkers of biologic age could recalibrate medical decision-making and replace chronologic age in evaluating medical and surgical treatments, transplant evaluation, and cancer screening, which are currently based on chronologic age thresholds that do not account for individual variation [4]. The real advantages of biomarkers of biological age include the ability to appropriately evaluate older adults with preserved physiological reserve who may currently be denied beneficial treatment interventions and to help them achieve real health benefits [4]. Also, opportunities for disease prevention may be missed in younger adults with accelerated functional decline, which could be addressed at an earlier stage [4].

Mortality has been described as the outcome of the pressure of causes of death, which correlates with changes in the ability to resist them [6]. However, the rate of aging and the pressure from causes of death may not be constant throughout the lifespan [6]. In 2015, Olshansky analyzed survival and life expectancy at older ages in the US from 1900 with projections to 2040 and identified that there was a possibility that factors other than genetic factors were associated with longevity, and if biomarkers could be identified, therapeutic interventions could confer these advantages in biological age [7]. Therapies that slow biological processes of aging can prevent disease and extend healthy years of life and have been referred to as geroprotective [7,8]. In 2018, Belsky and colleagues evaluated methods to quantify biological aging using repeated-measures physiological and genomic data from 964 middle-aged individuals in the New Zealand Dunedin Study, born between 1972 and 1973, including telomere length, three epigenetic clocks, and three biomarker composites [8]. The authors found that these attempts to quantify biological aging may not measure the same aspects of the aging process and highlighted the need for specific biomarkers of biological aging [8].

Recently, Sánchez-Sánchez and colleagues reported outcomes from a study that validates the importance of identifying biomarkers of biological age [9]. The INSPIRE-T Cohort study on the associations between biological age acceleration (BAA) and functional capacities in 1,014 individuals aged 20–104 years from the Southwest France community-based program, Inspire Translational Human cohort [9]. DNAm GrimAge, an advanced epigenetic biomarker or clock, was evaluated to estimate biological age and predict morbidity and mortality by analyzing DNA methylation markers [9]. Findings from the evaluation of four epigenetic biological clocks showed that, among the four DNA methylation epigenetic clocks and one inflammatory clock, GrimAge was the biological aging clock most strongly associated with measures of functional capacity across young to older adulthood [9]. Importantly, biological age should be recognized as dynamic, capable of changing over time.

Small noncoding RNAs (sncRNAs) are transcribed from DNA but are not translated into proteins (unlike mRNA), are critical regulators of gene expression, and act by post-transcriptional silencing, RNA modification, and mRNA degradation [10]. The main types of sncRNAs include micro-RNAs (miRNAs), small interfering RNAs (siRNAs), small nucleolar RNAs (snoRNAs), small nuclear RNAs (snRNAs), transfer RNAs (tRNAs), and PIWI-interacting RNAs (piRNAs) (Table 1) [10]. Also, sncRNAs maintain cellular homeostasis, regulate gene networks, protect against viral infections via RNA interference, and modify other RNA molecules, and are increasingly identified as diagnostic biomarkers (Table 1) [10]. PIWI-interacting RNAs (piRNAs) are between 20 and 35 nucleotides in length and have known regulatory roles in silencing genomic transposons and protecting germline cells (Table 2) [11,12]. Studies in preclinical models, including the nematode Caenorhabditis elegans and the fruit fly Drosophila melanogaster, have shown that disruption or knockdown of piRNA components can significantly extend lifespan [13,14]. In the past decade, several piRNAs have been identified, but their functions have only just begun to be understood (Table 2) [11,12].

In January 2026, Kraus and colleagues reported findings from a study investigating the role of circulating small noncoding RNAs (sncRNAs), including PIWI-interacting RNAs (piRNAs) and microRNAs (miRNAs) in human longevity by validating epigenetic factors and survival in older adults, developing clinical prediction models of survival, and identifying potential targets to prolong longevity [12]. The study evaluated biobank blood samples from 1,271 participants aged ≥71 years in the community-based Duke Established Populations for the Epidemiologic Study of the Elderly (D-EPESE) cohort, established by the National Institute on Aging [15]. The D-EPESE study was originally designed to identify predictors and risk factors for chronic diseases and functional loss [15,16]. The North Carolina (NC) urban-rural cohort included individuals >65 years [12]. Kraus and colleagues evaluated 828 small noncoding RNAs, including 687 miRNAs and 141 piRNAs, in baseline plasma samples from the Duke-EPESE cohort and incorporated clinical variables, demographics, lifestyle, physical function, laboratory tests, lipid measurements, and comorbidities [12]. Next-generation sequencing was used to quantify sncRNA levels in 707 participants from the D-EPESE cohort to ensure balance by race, sex, age, and survival status [12]. Nine piRNAs were reduced in longer-lived individuals and identified as potential therapeutic targets [12]. These findings indicate that the piRNA signature may act as a molecular clock that reflects biological aging and functional cell aging, and unlike chronological aging, biological aging could be modifiable [11,12].

In parallel with studies on molecular biomarkers of aging, artificial intelligence (AI) technologies are anticipated to have a role in evaluating biological age using image analysis, metabolic analysis, and functional analysis tools [17]. Deep learning models can estimate biological age from face photographs to improve survival prediction in patients with cancer [18]. Analysis of lung age from chest radiographs has been shown to predict longevity beyond chronological age and long-term all-cause and cardiovascular mortality [19]. Recently, Lee and colleagues published a study of 670 patients aged ≥60 years with early-stage non–small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy (SBRT) to evaluate photography-based facial age estimates, spirometry-based lung age estimates, and outcomes [20]. This study showed that chronological age was not an independent predictor of treatment outcome, whereas facial age was an independent biomarker associated with survival and early mortality, complementing lung age [20].

Conclusions

The way clinicians measure age shapes medical decision-making, but reliance on chronologic age may obscure the complexity of aging. A more individualized approach to evaluating biological aging could improve alignment between clinical diagnosis and management and physiological function and survival outcomes. These important factors will continue to drive the quest for biomarkers of biological aging with the hope of identifying targets for reducing the effects of aging and improving longevity and quality of life. Also, molecular biomarkers of biological age and health, combined with functional and physical biomarkers, could be supported by AI technologies to personalize future treatment decisions in adults at all stages of life.