Biomarkers of ageing: Current state‐of‐art, challenges, and opportunities

Abstract

Given the unprecedented phenomenon of population ageing, studies have increasing captured the heterogeneity within the ageing process. In this context, the concept of “biological age” has been introduced as an integrated measure reflecting the individualized ageing pace. Identifying reliable and robust biomarkers of age is critical for the accurate risk stratification of individuals and exploration into antiageing interventions. Numerous potential biomarkers of ageing have been proposed, spanning from molecular changes and imaging characteristics to clinical phenotypes. In this review, we will start off with a discussion of the development of ageing biomarkers, then we will provide a comprehensive summary of currently identified ageing biomarkers in humans, discuss the rationale behind each biomarker and highlight their accuracy and clinical value with a contemporary perspective. Additionally, we will discuss the challenges, potential applications, and future opportunities in this field. While research on ageing biomarkers has led to significant progress and applications, further investigations are still necessary. We anticipate that future breakthroughs in this field will involve exploring potential mechanisms, developing biomarkers by combining various data sources or employing new technologies, and validating the clinical value of existing and emerging biomarkers through comprehensive collaboration and longitudinal studies.

INTRODUCTION

The scientific and medical community has observed enormous advances in prolonging life expectancy in the past few decades. According to a report by World Health Organization (WHO), the global population aged 65 and over is projected to double to 1.5 billion between 2019 and 2050, with one in six people being aged 65 years or over. While the rapid global increase in lifespan is striking, the health span, also known as a disease-free lifespan, has not increased at the same pace. Age-related comorbidities now occupy roughly 16%–20% of one's time while they are alive, bringing into question the quality of life for elderly people living longer. In addition, the global burden of disease is increasing dramatically due to high rates of age-related functional decline, non-communicable chronic diseases, and mortality. In 2010, there were 777 million years lived with disability (YLD) from all causes, up from 583 million in 1990. Age-related disorders, including cancer, cardiovascular diseases (CVD), and neurodegenerative diseases contributed to over 30% of the YLD and are major life threats to adults of advanced age. The elderly are also increasingly vulnerable to other nonnegligible social-public issues including elder abuse and poor mental health, and in many developed countries, healthcare expenditure consumes over 10% of gross domestic product (GDP). This makes the burden of ageing an unignorable problem, and effective, accessible, and affordable strategies to handle the ageing population are urgent priorities for health systems.

Chronological age, measured as the time since the date of birth, is strongly linked with deteriorating health, morbidity, and mortality. However, ageing is a heterogeneous process with great variation in health outcomes among people of the same chronological age. At the individual level, different cells, tissues, and organs exhibit different ageing trajectories. For example, different models to predict an organ's age have been proposed for the brain, heart, retina, and other major organs. At the population level, heterogeneous ageing is evident by elderly individuals of the same age living completely different lives, with some being completely independent and others requiring complex care for daily activities. Additionally, centenarians often concentrate in certain geographical regions (e.g., Ikaria in Greece, Sardinia in Italy) and are healthier than their neighbors. Taken together, the heterogeneity of ageing is evident at all levels.

Given ageing is a heterogenous process, chronological age poorly reflects internal biological processes and intra-individual variation. As such, the scientific community has shifted their focus to biological markers of ageing, which quantify biophysiological ageing processes. The concept of “a biomarker of ageing” was firstly introduced in 1988 by Sprott et al., described as a biological parameter of an organism to predict functional capability. Based on this, the concept of biological age has gradually emerged as a measurement which determines individual-specific, age-related risk of adverse outcomes (Figure ). Though biological age lacks a clear definition so far, it can be indicated that this parameter is driven by interactions between cellular and biochemical processes, leading to a thoughtful and individual-specific reflection on physiological function and overall health. Countless potential candidate biomarkers of ageing have been proposed, ranging from molecular changes and imaging characteristics to clinical phenotypes. Identifying reliable and robust biomarkers of age is critical for the accurate risk stratification of individuals and exploration into anti-ageing interventions.

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In this review, we focused on ageing biomarkers identified in humans. We will start off with a discussion of the development of ageing biomarkers, followed by an introduction of the main ageing biomarkers, summarized in Figure . Our aim was not to present an exhaustive account of all ageing biomarkers but to introduce the rationale underlying each biomarker, highlight their accuracy in age prediction and summarize their clinical value in determining age-specific outcomes all with an up-to-date perspective. In the following sections, challenges, potential applications, and future opportunities in the field are also discussed.

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DEVELOPMENT OF AGEING BIOMARKERS

General requirements of ageing biomarkers

The American Federation for Aging Research (AFAR) developed criteria for ageing biomarkers based on Johnson and Butler et al. According to their guidelines, a biomarker of age needs (1) better performance for predicting age and age-associated outcomes, (2) monitor the ageing process in systems rather than effects of the disease, (3) have the ability to be repeatedly tested in a harmless way, both in humans and experimental animals. As these requirements are too strict that few biomarkers could fulfill, currently there are no gold standard criteria or method established for developing ageing biomarkers.

General methods for developing ageing biomarkers

Methods for identifying ageing biomarkers have been developed mainly through the assumption that biological age is aligned with the chronological age in healthy populations. In addition, numerous groups have trained prediction models on age-related phenotypes and used them as proxies of biological ageing (i.e., mortality risk, health life span, composite clinical measures of phenotypic age, or subjective age).

The accuracy of age estimators could be assessed through mean absolute error (MAE) determined as the mean absolute value of the difference between the predicted age and the ground truth. In brief, a smaller MAE indicates a higher accuracy of the ageing biomarker to predict chronological age. When the model is then applied to general populations, any deviation between predicted age and chronological age suggests the rate of ageing. This was coined the “biological age gap,” where a positive age gap infers an accelerated ageing process while a negative one shows a decelerated process. The age gap is a widely accepted and validated concept in the field of ageing biomarkers. Figure  depicts the overview of the classic method of developing ageing biomarkers.

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MOLECULAR AND CELLULAR AGEING BIOMARKERS

Telomere length

Telomeres are regions of repetitive nucleotide sequences at the ends of chromosomes which can protect DNA from damage and instability. In most somatic cells, telomeres will shorten 50–150 base pairs after every cell cycle. Telomere length is considered as a marker of ageing considering most human somatic cells will undergo a limited number of divisions until the protective function of telomeres is exhausted, leaving cells vulnerable to mutations. Previous studies found telomere length shortened predictably with age, and this process occurred in most cells and tissues including fibroblasts, peripheral blood cells, and colonic mucosa. Growing evidence also demonstrates a strong correlation between age and telomere attrition.

Telomere length has proved itself to be a promising ageing biomarker and was significantly associated with frailty and functional decline of the lung, heart, and kidney. Participants with Type 2 diabetes mellitus (T2DM), coronary heart disease, lung cancer, lymphocytic leukemia, and lymphoma generally exhibited shorter cellular telomere length compared to that of control groups. A large number of cohort studies have consistently reported an association between telomere length and mortality risk. Shortening of leukocyte telomere length (LTL) is also associated with the incidence and progression of age-related diseases, including CVD, neuropsychiatric disorders, diabetes, and multiple cancers (e.g., gastrointestinal, head and neck cancers). However, due to the high sensitivity of telomere length to a wide range of factors and great variability within different cell types and tissues, controversies still exist over the relationship between telomeres and ageing. Ongoing research is focusing on refining its measurement and interpretation, which may ultimately promote the reliability of telomere length as an ageing biomarker.

Omics biomarkers

The term “omics” refers to the high-dimensional analysis of whole molecular biology. The concept of omics started with genomics (the study of genes), as DNA was the first discovered biological macromolecule. More recently, technical advances in methodologies including proton nuclear magnetic resonance (NMR), mass spectrometry, and array/chip-based methodologies, transcriptomics (the study of all RNA molecules) and proteomics (the study of proteins) have brought to light biomarkers of ageing.

Currently, omics can interpret near-entire biologic data from organisms including epigenomics (the study of supporting structure of genome including modifications on DNA), transcriptomics (the study of all RNA types), metabolomics (the study of small molecules), microbiotics (the study of genes and genomes of microbiota), and so on.

Epigenetics

DNA methylation (DNAm), which refers to the covalent attachment of a methyl group to the fifth carbon of a cytosine residue, determines the epigenetic clock that regulates gene transcription. DNAm is a sophisticated chemical modification system, where depending on its activation or inhibition, can alter genomes of different cell types and development processes. In human, DNAm occur most frequently at cytosine–guanine dinucleotides (CpGs), which is associated with ageing.

DNAm status is important in the field of ageing because as one gets older the profile of the methylation status of CpGs alters at different sites of the genome. It was also demonstrated that millions of CpGs are involved in the ageing process, and a growing body of evidence revealed DNAm status to be significantly associated with chronological age and age-related diseases.

Given the strong links between DNAm status and ageing, DNA methylation is the most studied epigenetic trait to estimate biological age in the last decade. In 2013, the epigenetic clock was proposed by Hannum et al. based on whole blood from full age range samples including 71 CpGs. Soon after, Horvath proposed the multi-tissue-based epigenetic clock and identified 353 CpGs within their database. Subsequently, many other epi-clocks have been discovered based on different biological samples, populations, and statistical modelling methods. Existing epi-clocks may vary in the amount and site of CpGs, but most of them exhibited high accuracy in predicting chronological age with a correlation coefficient of approximately 0.9 with chronological age and an MAE < 5 years. Table  summarises the accuracy and other characteristics of the epi-clocks.

Table 1 Summary of the accuracy and other characteristics of the epi-clocks.

Clock (author, year)	Bio-sample	Sample size	Sample age range (years)	Identified CpG sites	Platform	Target	Regression model	Accuracy
Hannum, 2013	Whole blood	656	19–101	71	Illumina HiSeq platform	Chronological age	Multivariate linear model (elastic net algorithm)	R: 0.96; MAE: 3.9 years
Horvath, 2013	Multitissue (51)	72	0–100	353	Illumina 27 K or Illumina 450 K array platform	Chronological age	Penalized regression model	R: 0.96; MAE: 3.6 years
Horvath, 2013	Multitissue (51)	72	0–100	110	Illumina 27 K or Illumina 450 K array platform	Chronological age	Penalized regression model	R: 0.95; MAE: 4.0 years
Weidner, 2014	Blood	575	0–78	102	HumanMethylation27 BeadChip platform	Chronological age	Multivariate linear model and leave-one-out validation	R2: 0.98; MAE: 3.34 years
Lin, 2016	Blood	2756	-	99;	Illumina 450 K	Chronological age	Multivariate linear model	R: 0.97 Median error: 3.45 years
Lin, 2016	Blood	2756	-	3	Illumina 450 K	Chronological age	Multivariate linear model	R: 0.79 Median error: 10.9 years
Horvath, 2018	Multi-tissue	896	0.28–94	391	Infinium 450 K and the EPIC array	Chronological age	Elastic net regression	R: 0.74– 0.99 MAE: 1–18 years
McEven 2020,	Buccal cell, saliva, and blood	1721	0-20	94	Infinium 450 K and the EPIC array	Chronological age	Elastic net regression	R: 0.98 Median absolute error: 0.35 years
Zhang, 2017	Blood and saliva	13,661	2–104	514	HumanMethylation450 chips and Illumina EPIC (850 K) arrays	Chronological age	Elastic net and best linear unbiased prediction	R: 0.99 RMSE: 2.04 years
Levine, 2018, (PhenoAge)	Blood	456	21–100	513	Illumina Infinium 450 K array and Illumina EPIC methylation array	Phenotypic age	Cox penalized regression model	R: 0.71
Lu, 2019 (GrimAge)	Blood	2356	-	1030	Illumina Infinium 450 K array and Illumina EPIC methylation array	Lifespan	Elastic net regression	R: 0.82
Liu, 2020	Blood	2993	-	-	Illumina array	All-cause mortality	Elastic net Cox regression	R: 0.63–0.82

Data from participants with age-related diseases have established the concept of epigenetic age acceleration (EAA), which is either defined as the residual of regressing DNAm age on chronological age or the difference between DNAm age and chronological age. For instance, patients with ischemic stroke are considered epigenetically older than healthy controls despite having the same chronological age. In African Americans, EAA was significantly associated with CVD risk factors, such as systolic blood pressure and fasting glucose. In terms of functional markers, it was found that people with greater EAA had poorer performance on lung function, walking speed, grip strength, cognitive ability, verbal fluency, selective attention task, and frailty.

Longitudinal studies have further revealed an association between EAA and a higher risk of all-cause mortality, and specific leading causes of deaths, such as cancer-related mortality and CVD-related mortality in EAA. EAA is not only associated with risk of cancer but also the prognosis of cancer. Independent of chronological age and traditional CVD risk factors, greater EAA was a promising predictor for incident fatal coronary heart disease, peripheral arterial disease, heart failure, and ischemic stroke outcome. Moreover, EAA was associated with higher risk for incident CVD events, and a predictor for dementia, Alzheimer's Disease, Parkinson's Disease, and posttraumatic stress disorder (PTSD). EAA is also documented to be associated with changes in frailty over time.

Transcriptomics

Transcriptomics study the complete set of RNA transcripts in cells or tissues. Initially the transcriptome was restricted to messenger RNA (mRNA) coded by DNA when the central dogma was established, but was later extended to transfer RNA and ribosomal RNA. The recent advance of Next-Generation Sequencing (NGS) technology founded noncoding RNAs (ncRNA), which expanded the bank of transcriptomics available for analysis.

Previous studies show age-associated changes in RNA expression patterns of different tissues including the brain, skin, blood, and kidney. MicroRNAs (miRNAs), which are newly identified noncoding RNAs, were strongly correlated with ageing and age-related diseases.

Harries and colleagues first developed the concept of transcriptomic age, which used five transcripts derived from blood samples based on linear model analysis to predict chronological age. It achieved 95% accuracy when distinguishing young (<65 years) from old individuals (≥75 years). Later, Fleischer et al. constructed a transcriptome-derived ageing clock using RNA sequences from fibroblast cell lines which had a median error of 4 years. Using similar statistical models, subsequent studies on transcriptomic age achieved correlations of 0.348–0.744 when comparing chronological versus transcriptome-estimated age in different cohorts, achieving an MAE of 7.8 years. Recently, Holzscheck leveraged artificial neural networks to predict age using mRNA-sequenced data from 887 subjects aged 30–89 years, and the MAE was reduced to 4.7 years. In addition to mRNA, miRNA age constructed by Huan and colleagues used an elastic net regression model which incorporated 80 miRNA expressions and showed moderate correlation with chronological age in the replication sets (r = 0.65). Another study used seven machine learning algorithms to predict age from six age-related miRNAs from blood samples, with the AdaBoost model exhibiting the best performance and an MAE of 5.52 years. Table  summarizes performance-related characteristics of transcriptomic age.

Table 2 Summary of other omics-based ageing biomarkers.

Clock	Author year	Biosample	Sample size	Sample age range (years)	Identified sites	Sequence technology	Target	Regression Model	Accuracy
Transcriptomic age	Harries, 2011	Blood	698	30–104	5 transcripts	Transcriptomic microarray	To distinguish young (<65 years) from old (≥75 years)	Linear model analysis	0.95% variation
transcriptomic age	Fleischer, 2018	Dermal fibroblasts	133	1–94	Transcripts	Genome-wide RNA-seq	Chronological age	Linear discriminant analysis (LDA) classifiers	Median error: 4 years; MAE: 7.7 years
Transcriptomic age	Peter, 2015	Blood	7074 samples	-	1497 transcripts	Gene chips	Chronological age	Linear model analysis	MAE: 7.8 years
Transcriptomic age	Holzscheck, 2021	Skin	887	30–89	Transcripts	RNA sequencing	Chronological age	Artificial neural networks	MAE: 4.7 years
MicroRNA age	Huan, 2018	Blood	2610	-	80 microRNAs	TaqMan chemistry-based miRNA assays	Chronological age	Elastic net regression	R: 0.65
MicroRNA age	Fang, 2020	Blood	100	21–68	6 miRNAs	Massive parallel sequencing	Chronological age	AdaBoost-machine learning	MAE: 5.52 in male; MAE: 7.46 in female
GlycanAge	Kristic, 2013	Blood	2035	-	-	Hydrophilic interaction chromatography	Chronological age	Regression	58% variance
IgG N-glycan age	Yu, 2016	Plasma	701	NA	10 IgG N-glycans	Hydrophilic interaction liquid chromatography	Chronological age	Binominal regression	R: 0.56 29.4% variation
Proteomics age no	Tanaka, 2018	Plasma	240	22–93	76 proteins	SOMAscan assay	Chronological age	Elastic net regression	R: 0.94 MAE: 5.7 years
Proteomics age	Johnson, 2020	Plasma	3301	18–76	23 proteins	-	Chronological age	Linear models	R: 0.97 MAE: 5.5 years
IgG N-glycan age	Lehallier, 2019	Plasma	4263	18–95	373 proteins	The SOMAScan platform	Chronological age	LASSO model	R: 0.93–0.97
metabolic age	Hertel, 2016	Urine	1700 women; 1911 men	NA	59 metabolites	NMR spectroscopy	Chronological age	Linear regression	R: 0.74 in men R: 0.78 in women
MetaboAge	Robinson, 2020	Urine, serum	2239	19.2–65.2	1311 features	NMR and liquid chromatography–mass spectrometry (UPLC-MS)	Chronological age	Elastic net models	R: 0.86, MAE: 3.71 years
MetaboAge	Akker, 2020	Blood	25,000	-	56 metabolites	NMR	Chronological age	Liner regression	-
Microbiome age	Galkin, 2020	Stool	1165	18–90	-	Gut metagenomics	Chronological age	Deep neural network (DNN)	MAE: 5.91 years
Microbiome age	Gopu, 2020	Stool	90,303	<1–104	-	Microbial genes	Chronological age	Elastic Net	MAE: 7.64 years
Biophysical age	Denis Wirtz, 2017	Dermal fibroblast	32	2–96	2 biophysical parameters	-	Chronological age	Regression	6–7 years

Cross-sectional studies have shown older transcriptome-derived age was associated with several age-related phenotypes including higher blood pressure, body mass index, serum cholesterol, glucose, urea and albumin levels, and lower interleukin-6. The transcriptomic clock based on skin fibroblasts also predicted higher ages in progeria patients compared with age-matched controls. This miRNA age was also associated with coronary heart disease, hypertension, blood pressure, glucose levels, and an increased risk of all-cause mortality. The validity of transcriptomic age in predicting age-specific outcomes requires further validation in larger and more diverse longitudinal cohort studies.

Proteomics

Proteomics study the entire set of proteins produced or modified by an organism or system. The recent advance in mass spectrometry allows for high-throughput characterization of large-scale proteins, making it suitable for disease screening.

Proteins drive different biological functions and processes, including ageing, and numerous studies have demonstrated that proteins may drive ageing, as evidenced by samples from serum, bone marrow, cerebrospinal fluid (CSF), and skin. For example, Baird et al. detected age-dependent changes of 82 proteins in CSF samples from normal ageing adults.

A GlycanAge was constructed by Kristic et al. that utilized immunoglobulin G (IgG) glycosylation data from multiple cohorts to demonstrate this protein explains 58% of the variance in chronological age. Yu developed a similar IgG glycosylation age using 10 IgG N-glycans and found a moderate correlation between predicted age and chronological age, with a correlation efficient of 0.56. Other studies have built proteomic clocks based on plasma protein levels that change with age and reported good accuracy in predicting chronological age. The proteomic age based on 76 proteins created by Tanaka et al. was highly correlated with chronological age (r = 0.94) with an MAE of 5.7 years. Another study built a proteomic age profile using 23 proteins and reported an MAE of 5.5 years and correlation efficiency of 0.97. Detailed characteristics of proteomics-derived age are described in Table .

Proteomic clocks are reassociated with several ageing phenotypes, including physical function, cognitive function, and ageing markers such as glucose and triglycerides levels. From a longitudinal perspective, proteomic age is also an independent predictor for future risk of all-cause mortality, multimorbidity, health-span, and lifespan. Despite these promising findings, the high complexity of the proteome and lacking of standardized measurement tools make the proteomic age still in its early stages.

Metabolomics

Metabolomics is the systematic study of metabolites. Previously the definition of a metabolite was defined as a molecule less than 1.5 kDa in size, but technical advancements have evolved this definition to amino acids, nucleic acids, carbohydrates, fatty acids (FAs), functional nutrients (e.g., vitamins and cofactors), inflammatory factors, hormones, and any other cell metabolism products.

Ageing and metabolism are undoubtedly linked, given the evidence that metabolites are actively involved in the ageing processes. For example, alterations in metabolites such as lipid profiles, steroid hormones, oxidative products, and inflammatory factors are considered to underpin parts of the ageing processes. Thus, these metabolites could be used to develop biomarkers of age.

As detailed in (Table ), the metabolic age score was first constructed by Hertel in 2016 based on 59 metabolites from human urine samples, where metabolome-derived age was highly correlated with the chronological age (r = 0.74–0.78). Using multiple metabolomic data sets, Robinson and his colleagues proposed a prediction model using elastic net models, observing an MAE of 3.71 years and a correlation of 0.86 between predicting and chronological age in the training set. Another study used over 25,000 samples to develop a 56 metabolite-based age predictor (metaboAge).

Metabolic age is associated with obesity, diabetes, and depression. In independent cohorts, the value of the metaboAge gap, the difference between the metabolic age and chronological age, was significantly associated with an increased risk for mortality, cardiovascular events, and prospective functional decline. Similar to proteomic age, metabolic age also presents several challenges including the complex and interconnected nature of metabolic pathways and the lack of standardized protocols.

Microbiotics

Microbiotics refers to the study of the microbiome to characterize its structure, function, and dynamics. The gut microbiota plays a major role in human healthy ageing through its principle functions of maintaining gut diversity and homeostasis. In the past decade, a large number of age-related changes in gut microbiota has been noted, and these modifications were associated with the interactions between the gut immune system and microbiota.

In 2020, Galkin first developed a gut microbiome age clock based on gut metagenomic profiles. This was trained by data from 1165 healthy individuals using a deep neural network (DNN), achieving an MAE of 5.91 years (Table ). In an elastic net model, Gopu et al. built a similar clock with the expression of microbial genes in stool samples from 90,303 individuals and reported an MAE of 7.64 years. The microbiome clock is currently limited to one cross-sectional study and was associated with diabetes.

Biophysical biomarkers

Apart from biochemical signals, cell mechanics and tissue rigidity have also emerged as crucial aspects of ageing. Cell mechanical properties refer to the cells' dynamic resistance to deformation due to applied forces or cell rheology. Previous studies have shown age-associated biophysical properties of cells. In general, cell stiffness increases while the cell's ability to undergo reversible deformations and rearrangement of the cytoskeleton reduces with ageing. Alterations in cell mechanics have been linked to functional impairments of tissues and organs, leading to a variety of age-related diseases.

Denis Wirtz and colleagues have recently reported a new set of biomarkers based on cellular biophysical properties. By using a combination of two biophysical parameters and a cohort aged between 2 and 96 years, they achieved an average predictive error ranging from 6 to 7 years. A recent review has discussed the promising potential of combining biophysical and biochemical changes of T cells in age prediction. However, further studies are still needed to validate this.

Blood-based biomarkers

Blood tests are common in clinical practice for the surveillance of certain diseases and conditions. With an abundance of historical data sets, blood tests provide biologically relevant and standardized data for age prediction. Table  summarizes blood-based ageing biomarkers.

Table 3 Summary of blood-based ageing biomarkers.

Clock	Author, year	Bio-sample	Sample size	Sample age range (years)	Blood-profile	Target	Regression model	Accuracy
Blood Biochemistry and cell count	Putin, 2016	Blood	62,419	-	41 blood markers	Chronological age	DNN	R: 0.91 R2: 0.82 MAE: 5.55
blood biochemistry and blood count	Mamoshina, 2018	Blood	55,751	20–80 years	19 blood biochemistry and blood count	Chronological age	DNN	MAE: 5.94 years R2: 0.65
Blood Biochemistry and cell count	Mamoshina, 2019	Blood	149,000	51–60 years	23 blood tests	Chronological age	Deep neural network	MAE: 5.72 years R2 = 0.56
blood biochemistry and blood count	Gialluisi, 2022	Blood	23,858	≥35 years	36 circulating markers	Chronological age	DNN	R: 0.76 R2: 0.57 MAE: 6.00 years
Physiological age	PeretZ, 2022	Blood	472,189	37–82 years	61 laboratory tests	Chronological age	XGboost model	MSE: 6.67 years
IMM-AGE	Alpert, 2019	Blood	135	20–32, 60–96 years	Cell subsets	Chronological age	Linear regression	-
Immune age	Lambert, 2022	Blood	28 + 28 + 25	2–55 years	19 immune cell subsets	Chronological age	Linear regression model	R2: 0.92
iAGE	Sayed, 2021	Blood	1001	8–96 years	50 circulating immune proteins	Chronological age	Auto-encoder (DNN)	R: 0.78
ipAGE	Yusipov, 2022	Blood	159	24–89 years	38 circulating inflammatory/immunological proteins	Chronological age	Elastic net regression model	R: 0.79 MAE: 6.82 years

Blood routine and biochemistry

Blood draws are part of routine examinations which test the quantitative and morphological changes in blood cells, including red blood cells, white blood cells, and platelets. Analysis of blood biochemistry provides the biological characteristics to estimate the function of organs. These are commonly used and carry low economic burden for tracking overall health in clinical settings.

Since 1991, studies exploring the influence of age on peripheral blood parameters have detected changes in blood cell counts with ageing. With advancing age, routine blood tests exhibited decreased levels of hemoglobin levels, white blood cell, and platelet count, while blood biochemistry revealed impaired physiological function as indicated by decreased glomerular filtration rate and elevated creatinine levels. Liver enzymes such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), and γ-glutamyl transferase (GGT) also showed age-dependent changes both in men and women.

The first biochemistry-based clock for ageing was developed by Zhavoronkov's lab in 2016 using 41 markers from blood biochemistry. Using DNNs, this clock predicted chronological age with an MAE of 5.55 years (Table ). Similar ageing clocks were developed from the same authors using DNN but with just 19 and 23 parameters, achieving an MAE of 5.94 and 5.72 years, respectively. Another blood-based ageing clock, using DNNs for 36 circulating biomarkers from Italian populations found an MAE of 6.00 years. Lastly, Peretz et al. trained an XGboost model using 61 blood parameters from 472,189 subjects aged 37–82 years old and achieved a RMSE of 6.67 years.

Biochemistry-based clocks suggested younger biological age is associated with fewer comorbidities, operations, and prevalence of age-related diseases, alongside a better physical and mental wellbeing than age-matched controls. Biochemistry-based age clocks could predict mortality, dementia, and hospitalization risk.

Immune profiles

The immune system defends and protects the body from foreign pathogens. The important role of the immune system in maintaining the homeostasis within human body has been recognized for hundreds of years. The term immunosenescence has been developed to describe the age-related changes of both innate and acquired immunity at the cellular and serological level. This is usually characterized by the senescence of specific immune cell populations and impaired immune function to mount appropriate responses to immunogenic stimuli, leading to an increased vulnerability to infectious and chronic diseases. Additionally, age-related changes in the immune system's composition can contribute to low-grade systemic inflammation, which is one of the key factors for the pathogenesis of many morbidities including cardiovascular diseases and neurodegenerative diseases.

Alpert and colleagues have recently developed an aggregated score IMM-AGE which remodeled the immune cell composition derived from flow cytometry (Table ). Another study generated immune clocks using 19 immunity-related parameters trained using healthy participants and demonstrated a high correlation with chronological age (r² = 0.92). People with Down's syndrome have shown accelerated immune age from childhood. Immune age can also predict all-cause mortality, but a paucity of data exists.

Inflammatory profiles

Inflammation is a defense mechanism mediated by inflammatory proteins in response to perceived noxious stimuli. Concomitant with the broad deregulation of functioning immune cells with ageing, accumulating evidence indicates that inflammatory molecules moderately rise, touted as “inflammageing.” Most older individuals have numerous elevated proinflammatory markers (termed the senescence-associated secretory phenotype or SASP) like C-reactive protein (CRP), interleukin 6 (IL-6), interferon α and interferon β, transforming growth factor-β (TGF-β), and serum chemokines MCP-1. These SASP would modify the tissue microenvironment and alter the function of nearby normal cells. A prolonged and persisting inflammation will result in the accumulation of tissue damage, ultimately leading to age-related pathologies.

Sayed et al. proposed an inflammatory ageing clock (iAge) from 50 circulating markers of inflammation, including cytokines and growth factors, all of which highly correlated with chronological age (r = 0.78). A similar inflammatory clock derived from 38 inflammatory markers was subsequentially developed using machine learning and achieved an MAE of 6.8 years. Inflammation age was associated with ESRD status and could predict multimorbidity and frailty.

IMAGE-BASED AGEING BIOMARKERS

Brain age

The concept of brain age was proposed with the knowledge that structures and functions of the brain change with the ageing process. Neural ageing is a significant part of human ageing, and neuroimaging, such as magnetic resonance imaging (MRI) and Positron Emission Tomography (PET), provides a unique window to identify neural ageing. For instance, structural MRI showed various age-related brain alterations including gray matter (GM) volume, GM concentration, connectivity of white matter, cortical thickness, cerebrospinal fluid increment, and so forth.

Brain age was first built upon structural MRI scans where features such as the gray matter volume, cortical thickness were extracted to predict chronological age. In 2010, Franke et al. used a correlation vector machine to predict age based on gray matter density with an MAE of 4.98 years and correlation coefficient of 0.92. Later, other processing methods, such as Gaussian processes regression model and support vector regression were applied to predict age using neuroimaging. Though overall these models achieved high accuracy in age prediction, the image-processing before model constructions was time-consuming and labor-intensive.

Recent emergence of artificial intelligence (AI) techniques, especially deep learning (DL), could enable age predictions from raw and unprocessed neuroimaging data, thus obviating the reliance on time-consuming preprocessing and improving the efficiency of age prediction. Cole et al. leveraged convolutional neural networks (CNN) to predict brain age based on raw T1-weighted MRI data, achieving an MAE of 4.16 years and correlation of 0.96. Various models trained on neuroimaging (T1/2 weighted MRI,magnetoencephalography, etc.) by DL techniques (Table ) have been developed to predict age with reasonable accuracy and demonstrate strong correlation between predicted brain age and chronological age. Recent studies demonstrated the utility of multimodality neuroimaging data in terms of age prediction.

Table 4 Summary of brain age.

Clock (author, year)	Sample size	Sample age range (years)	Image modality	Target	Regression model	Accuracy
Franke, 2010	655	19–86	T1W MRI	Chronological age	Kernel method	R: 0.92 MAE: 5 years
Lin, 2016	112	50–79	T1W MRI	Chronological age	Principal component analysis	R: 0.8 MAE: 4.29 years
Cole, 2017	2001	18–90	T1W MRI	Chronological age	CNN	R: 0.96 MAE: 4.16 years
Chung, 2018	953	3–21	T1W MRI	Chronological age	Supervised machine learning	R2: 0.84 MAE: 1.69 years
Kuhn, 2018	765	20–78	Diffusion tensor imaging	Chronological age	Support vector regression	R: 0.84 MAE: 7.39 years
Beheshti, 2019	675	45–95	T1W MRI	Chronological age	Support vector regression	R2: 0.88 MAE: 2.36 years
Wang, 2019	3688	-	T1W MRI	Chronological age	CNN	R: 0.85 MAE: 4.45 years
He, 2021	16,705	0–97	T1W MRI	Chronological age	CNN	R: 0.98 MAE: 3 years
Hwang, 2021	1530	20–90	T2W MRI	Chronological age	CNN	R: 0.98 MAE: 4.22 years
Rockicki, 2021	750	18–86	Multimodal MRI (T1W, T2W ASL)	Chronological age	Random forest	R2: 0.77 MAE: 6.4 years
Porxas, 2021	613	18–88	MRI + MEG	Chronological age	Principal Component Analysis (PCA) and Canonical Correlation Analysis with Gaussian process regression model	R: 0.98 MAE: 4.88 years

The difference between brain age and chronological age, also known as brain age gap, has been found to be associated with brain conditions in an array of cross-sectional studies. Accelerated brain ageing, indexed by a positive value of brain age gap, was reported to be associated with poorer cognitive performance, increased odds of dementia (Alzheimer's disease), major depressive disorders (depression, anxiety), schizophrenia, and epilepsy. In addition, larger brain age gap was linked with diabetes.

In longitudinal studies, brain age gap was found to be an independent predictor for incident dementia. Subjects with older brain age in childhood had lower cognitive ability in adulthood. In addition to brain diseases, accelerated brain ageing was also associated with increased future risk of mortality.

Retinal age

An increasing amount of evidence points to the retina as a window to the body. The retina shares similar embryological origins, anatomical structures, and physiological features with vital organs, such as the heart, brain, and kidney. Originating from the diencephalon, the retina is composed of several neuronal cell types and is connected to the central nervous system (CNS) through the optic nerve. The microvasculature of the retina is closely connected with that of the brain, heart, and kidney. A growing number of studies suggest retinal microvascular alterations in the neurons and microvasculature could reliably reflect cerebrovascular diseases, neurodegenerative diseases, and systemic circulation health outcomes.

Given the strong association between eyes and vital organs of the human body, age estimation from retinal imaging has become a hot topic. Liu et al. proposed a CNN model for age estimation from fundus images and achieved an MAE of 3.73 years in a Chinese population. Similarly, Zhu et al. developed an age prediction model based on fundus images from a healthy population in the UK biobank data set, which achieved a correlation of 0.81 between predicted retinal age and chronological age, and an MAE of 3.55 years (Table ). A research team from Singapore developed RetiAGE, which determined the probability of age being ≥ 65 years from a fundus photo, achieving an AUROC of 0.968 and correlation of 0.62 with chronological age.

Table 5 Summary of other image-based biomarkers.

Clock	Clock (author, year)	Sample size	Sample age range (years)	Image modality	Target	Regression model	Accuracy
Retinal age	Liu, 2019	8736	>50	Fundus image	Chronological age	CNN	MAE: 3.73
Retinal age	Zhu, 2022	19200	40–70	Fundus image	Chronological age	Xception architecture	R: 0.81 MAE: 3.55
RetiAGE	Nusinovici, 2022	40,480	-	Fundus image	Probability of age being ≥65 years	Visual Geometry Group (VGG)-DNN	R: 0.62
OCT age	Shigueoka, 2021	7217	20.8–85.8	Optical coherence tomography	Chronological age	CNN	R: 0.86 MAE: 5.82
Facial age	Geng, 2007	1002	0–69	2D human face images	Chronological age	Primary component analysis	MAE: 6.22-14.83
Facial age	Guo, 2008	8000	0–93	2D human face images	Chronological age	Manifold learning and locally adjusted robust regression	MAE: 5.25, 5.30
PhotoAgeClock	Bobrov, 2018	8414	20–80	Eye corners images	Chronological age	Xception model	R: 0.95 MAD: 2.3
Facial age	Chen, 2015	322	17–77	3D human face images	Chronological age	Partial least squares regression model; support vector regression model	R: 0.85 MAD: 6.10–6.21
Facial age	Xia, 2020	4719	20–80	3D human face images	Chronological age	CNN	R: 0.95 MAE: 2.9
ECG-age	Starc, 2012	377	4–75	12-lead ECG	Chronological age	Multiple linear regression	R2: 0.76
ECG-age	Ball, 2014	1438	20+	5-min 12-lead ECG test	Chronological age	Bayesian approach	-
ECG-age	Attia, 2019	774,783	18+	12-lead ECGs	Chronological age	CNN	MAE: 6.9 years R2: 0.7
ECG-age	Lima, 2021	1,558,415	-	12-lead ECG	Chronological age	DNN	MAE: 8.38–10.04 years
abdominal age	Goallec, 2022	82,336	37–82	Liver and pancreas MRIs	Chronological age	CNN	R2: 73.3 MAE: 2.94 years
CXR-Age	Raghu, 2021	116,035	40–100	Chest X-ray	Chronological age	CNN	R: 0.37

A breakthrough using optical coherence tomography (OCT) for age estimates has been noted once, with Leonardo et al. developing a DL-based age prediction model using B-scans, which obtained an MAE of 5.82 years and strong correlation between the predicted age and chronological age (r = 0.860). Compared with two-dimensional (2D) fundus images, OCT could capture three-dimensional (3D) and micrometer level structures of the retina, providing optical biopsy of neuroanatomical and vascular changes with high resolution (~5 μm). More research is needed to test the value of OCT for biological markers of ageing.

Longitudinal studies showed the retinal age gap independently predicted the risk for all-cause and cause-specific mortality and age-related morbidities, including neurodegenerative disease, cardiovascular diseases, and kidney failure.

Face age

Ageing of facial features is a phenotype of human ageing, where alterations in facial skin texture, soft tissue, and bone volume lead to the sunken appearance of eye sockets, reduced angle of brow, and changes to the angles of the lower jaw.

In 2007, 2D facial images first predicted chronological age using regression statistical method and achieved an MAE of 6.22 years (Table ). Using a manifold learning method and a locally adjusted robust regression model, Guo et al. devised an automatic 2D-image estimation system for age prediction with an MAE of 5.25 years for female and 5.30 years for male. In 2018, Eugene et al. developed the PhotoAgeClock, using deep-learning network that focused on photographs of eye corners from facial images to predict chronological age. It could predict chronological age with an MAE of 2.3 years. As 3D imaging techniques improved, features of 3D facial images were integrated into biomarkers of ageing. Using a partial least squares regression model and support vector regression model, the 3D-based face age revealed a correlation coefficient of 0.95 and an MAE of 6.11 and 6.10 years in females and males, respectively. Later, the application of CNNs on 3D images achieved an MAE of 2.79 years.

The difference between face age and chronological age (face age gap) was associated with health and lifestyle parameters (obesity, blood pressure, transglutaminase, alkaline phosphatase, cholesterol, etc.), inflammation, and innate immunity at the transcriptome level. Notably, face age could better reflect general health status than chronological age. Previous studies reveal face age reflects the heterogeneity of the human ageing rate and the face age gap could predict mortality risk among older people. However, the use of antiageing skin medications (i.e., tretinoin), natural fat mass in the face, and cosmetic procedures could obscure and confound the accuracy of face age.

Electrocardiogram (ECG) age

Recently the ECG was explored for its reflection of biological cardiac age, considering older individuals have age-related changes to wave amplitude, duration, inter-interval variability, and electrical axis of ECG leads. Additionally, older individuals are more likely to have ECG abnormalities such as atrial fibrillation or flutter, complete right bundle branch block, and ischemic changes.

The first ECG-derived age prediction model used multiple linear regression analysis from five ECG parameters with the resulting predicted age being highly correlated with chronological age (r² = 0.76, Table ). Another ECG-based age was developed using a Bayesian approach derived from normal 12-lead ECG of 1438 healthy populations. Attia et al. later trained a CNN model to predict a person's age using the 12-lead ECG signals from 499,727 patients and the ECG-age achieved an MAE of 6.9 years in testing data sets and an r² = 0.7). Later, deep neural networks were trained and achieved an MAE of 8.38–10.04 years in different testing cohorts.

ECG-derived age was associated with lower ejection fraction, and an risk of hypertension, coronary diseases, and mortality.

Others

The usefulness of abdominal MRI scans as an ageing biomarker was explored using CNN analysis and achieved an MAE of 2.94 years (Table ). Chest X-ray age (CXR-Age), also derived through CNNs detecting characteristics of chest radiographs, obtained a correlation of 0.37 with chronological age (Table ) and was associated with cardiovascular risk factors.

Skeletal parameters derived from X-ray images were also used for estimations of both dental age and skeletal age. However, these are used to estimate chronological age for forensic practices rather than to reflect biological ageing.

OTHER CLINICAL MEASURE-BASED AGEING BIOMARKERS

Lung age

The concept of lung age was developed in 1985 to show the premature ageing of the lung in smokers. By recording the volume and speed of air inhaled and exhaled, spirometry provides a subjective indicator of pulmonary function.

Spirometric lung-age (SLA) was first proposed in 1985 using spirometry values from healthy adults. Linear equations were developed using the forced expiratory volume at 1 s (FEV1), and the estimated SLA achieved a standard error of 15.8 years. Newbury et al. updated this equation for Australians and found lung age to be equivalent to chronological age in the never-smoker group. Subsequently, several research teams developed different SLA-calculation equations based on different populations and spirometry variables.

Lung age was significantly older in patients with morbid obesity, severe airflow limitation, and COPD.

Cognitive age

Normal ageing is associated with a decline in cognitive function, specifically in memory, language, visuospatial, and executive function abilities. Besides, older individuals exhibit higher risk for cognition-related illnesses, such as dementia or mild cognitive impairment (MCI). Therefore, cognitive clocks have been developed as a novel indicator of brain health.

Machine learning was first used to generate a model for neurocognitive age in healthy adults based on nine standardized cognitive tests, achieving an MAE of 2.2 years in healthy populations. Another study characterized nonlinear cognitive trajectory patterns indexed as mini-mental state examination scores across years until death. The correlation between this cognitive age and chronological age was 0.56 at baseline, and 0.43 at death.

Cognitive age demonstrated strong associations with neuropathology and brain volume, and this method outperformed chronological age for predicting AD, dementia, MCI, and mortality.

Heart age

The concept of heart age was initially introduced into CVD management as an alternative way to express CVD risk scores. Age, sex, hypertension, dyslipidemia, diabetes, and unhealthy lifestyles are widely established risk factors for developing CVD. Developed from these factors, vascular age now is commonly considered a way for tracking vascular health.

In 2008, the Framingham Heart Study introduced the concept of heart/vascular age based on the CVD risk factor profile. The heart age was calculated as the chronological age of a person with the same predicted CVD risk but with other risk factors in normal ranges. Another study extended the use of heart age with the SCORE project scales most used in Europe. Following studies incorporated atherosclerosis indexed from imaging into the current vascular age calculation, such as intima-media thickness (CIMT) and coronary arterial calcification (CAC). Emerging studies confirmed the value of heart/vascular age in the prediction of all-cause mortality and cardiovascular diseases.

Biopsychological markers

The ageing process is more psychological than just physical. Psychological age refers to an individual's perception or experience of ageing as well as ageing-related physical frailty and mortality. A higher psychological age is related to higher psychological stress and detrimental psychological–physical interplay, which may significantly influence age-related biological changes, including systemic inflammation, obesity, pulmonary, and muscular dysfunction. Subjective age, evaluated by personal feelings of “how old oneself is,” is the most studied form of psychological age and is determined by personal experiences, social relationships, cultural values, and health status.

Recently, a DL-based model enabled quantitative assessment of an individual's psychological age based on multiple features such as health condition, headache frequency, relationship status, and expectations from sex life. This method achieved a mean absolute error of 6.7 years (R² = 0.56). As a biopsychological marker of ageing, older subjective age than chronological age, namely feeling older, has been shown to associate with a higher risk of mortality, frailty, depressive symptoms, activities of daily living, and well-being. DL-based psychological age was also predictive of all-cause mortality risk.

COMPOSITE AGEING BIOMARKERS

Pace of ageing

The pace of ageing acknowledges ageing by tracking and quantifying biomarkers that index cardiovascular, metabolic, renal, immune, and biochemical functions simultaneously to holistically address accelerations in ageing. By combining changes in these system across time, a single measurement (e.g., biomarker z-score) can determine the pace of ageing. In a Dunedin cohort, a faster pace of ageing was associated with cognitive deficits, signs of advanced brain ageing (i.e., volume, hippocampal atrophy), diminished sensory-motor functions, and older appearance.

Frailty index

Frailty is a functional state of physiological vulnerability and the degree of frailty increases with age. Derived from frailty, the frailty index is a composite biomarker of biological age and comprises multiple health items (various signs, symptoms, laboratory measurements, disabilities, and diseases). It is widely acknowledged that the frailty index is a measurement of malnutrition, wasting, weakness, slowness, and inactivity, and a sign of advanced ageing. Frailty index is linked to mental health, risks of falls and fractures, all-cause mortality, and cause-specific mortality.

APPLICATION OF AGEING BIOMARKERS

Anti-ageing research

Interventions aimed at slowing the ageing process and extending a healthy life span are equivalent to preventive interventions for a positive biological age gap (see the concept of biological age gap in Part 2). Future antiageing research will benefit from an understanding of the emerging predictors of ageing and the exploration of potential biological changes in accelerated ageing.

An increasingly important use of biological age in recent years has been as a metric to validate existing behavioural and pharmacological interventions. For example, several clinical trials have used the DNA methylation clock to measure the effects of physical exercise, diet, and antioxidant supplementation. In addition, emerging ageing biomarkers, such as proteomic biomarkers, provide new perspectives for targeted drug discovery, although most of these studies have remained experimental with limited clinical application. Furthermore, the field of anti-ageing regenerative medicine is advancing, with investigations exploring the effects and mechanisms of young stem cell components and regulatory factors. These efforts offer inspiring opportunities for the development of novel therapeutic strategies to combat ageing (Figure ).

[IMAGE OMITTED. SEE PDF]

Behavioural strategies

An inestimable part of biological ageing is the result of people's physical and social behaviours, including physical activity, diet, smoking and drinking habits, and socioeconomic status, and so forth. Physical activity, especially at moderate to vigorous levels, can provide robust improvements in physiological capacity and multiple organ function, as well as reduce the risk of age-related adverse events. Physical activity has also been shown to be effective in ameliorating age-related immune decline, another major contributor to multimorbidity and mortality. Another important behavioural approach to ageing is diet. Dietary interventions in patients with diabetes and hypertension have been recommended to reduce the risk of age-related morbidities, with benefits equivalent to or even better than medication. Dietary intervention is particularly applicable to the growing ageing population because of its advantages in terms of reducing care, low cost, wide universality, and positive feedback on the mental and social status of the elderly.

Pharmacological intervention

However, in most situations, behavioural interventions alone are insufficient to treat age-related diseases, possibly due to inconsistent patient compliance and complex interactions with other confounding factors. Targeted pharmacological modulation is a practical approach to further extending lifespan and health against the functional decline of biological ageing. Based on identified biomarkers and pathways of biological ageing, reducing inflammation, suppressing aberrant immune responses, and maintaining cellular homeostasis are current targets of mainstream pharmacological strategies to counteract this process. Preventive drug use for common diseases such as diabetes and cardiovascular disease in high-risk populations is already widely used as an anti-ageing tool, like Metformin, doxazosin, and so forth. The biological age gap can be used to better monitor ageing preclinically, allowing more precise identification of the time point for pharmacological intervention.

Drug discovery

In addition, the discovery and development of novel anti-ageing drugs have emerged as a promising avenue that could significantly alter the future prospects of individuals. The identification of emerging biomarkers of ageing, particularly those at the molecular and cellular levels, has provided researchers with a wealth of targets for anti-ageing therapies. With the aid of advanced multi-omics technology and large-scale molecular databases, researchers have accurately pinpointed the dominant plasma proteomic dataset associated with immunological ageing and have identified targeted immunological drugs relevant to age-related diseases. Nevertheless, the majority of current research has concentrated on the investigation of existing pharmaceuticals. While experimental studies have explored the utility of emerging biomarkers for the identification of new drug targets, their clinical application remains limited.

Regenerative strategies

During biological ageing, the ability of our stem cells to differentiate into tissue cells gradually decreases. In general, most adult stem cells are in a quiescent state rather than an active state as in an embryo. Emerging regenerative medicine focuses on restoring and rebuilding this regenerative potential by inducing quiescent stem cells to proliferate. Transplantation of neural stem cells has been used in the treatment of brain-ageing diseases such as Alzheimer's disease, reducing amyloid deposition and improving cognition in animal models. In addition, injecting the blood of younger animals into older animals resulted in the rejuvenation of their aged stem cells, with restored youthful functional and molecular states. However, it remains unclear whether the effective components are young stem cells or young regulatory factors. Advances in regenerative medicine provide insights for future anti-ageing interventions.

Promotion in geroscience

Surrogate in clinical outcomes

Ageing biomarkers are popular surrogate endpoints in geroscience trials. The use of ageing biomarkers provides a practical solution to the challenges of using traditional primary clinical endpoints, which can occur so infrequently that the statistical power of the trial is compromised. A potential application of biomarkers as surrogate endpoints is measuring the biological age gap in large-scale screening of diseases for preventive purposes. Most screening participants may be asymptomatic, free of age-related morbidities, but still have increased risks of ageing, indexed by high biological age gaps. A positive biological age gap can help us to identify those at high risk of future disease and allow early gero-protective interventions.

Incorporate social policy

Multidisciplinary cooperation with health professionals and society as a whole is needed to tackle the problem of ageing. At present, most social programmes and policies for older people are developed based on chronological age as a criterion for eligibility. However, due to the heterogeneity of ageing, there is still a large number of people who do not meet the criteria yet suffer from accelerated biological ageing and impaired health status. With the emerging application of ageing biomarkers in geroscience, the target population of ageing social policies can be identified with greater precision than by reference to chronological age alone. In line with the public health response of the UN Decade of Healthy Ageing (2021–2030), the use of ageing biomarkers can update our perceptions of ageing, facilitate research into the interactions between social determinants and biological changes, and provide individualised health services based on personal characteristics of biological ageing. The subsequent changes in such biomarkers can provide traceable evidence for long-term care.

CHALLENGES

Advances in ageing biomarkers may uncover the physiological changes driving the ageing process and provide insight on “healthy” ageing. However, many challenges must be addressed before widespread adoption of ageing biomarkers. First, the methods of defining the ground truth have been varying and undetermined; second, the sources of data that best represent biological ageing remain unknown; third, there is unavoidable bias in improving the model accuracy and a paradox may exist between improving the accuracy of age prediction and validity of reflecting ageing rates; fourth, clinical value of different ageing biomarkers remains to be investigated, especially in longitudinal studies.

Ground truth of biological age

Of note, there has been no standard way of defining biological age (see details in Part 2). Generally, various objective and subjective assessments have served as ground truth of biological age, including objective metrics covering chronological age in the healthy population and age-related phenotypic data (e.g., mortality risk), as well as subjective metrics such as perception of ageing. The prediction of the ageing process may have certain characteristics.Chronological age in a healthy population is considered as the most common way of defining biological age. However, consensus around the definition of a “healthy” ageing population is lacking, and noise or dirty data potentially exist despite the strict definition of the healthy population.

Given these limitations of defining biological age from the chronological age, age-related phenotypic data, such as mortality risk, healthy life span, and composite clinical measures of phenotypic age are also used to train the model. Although this avoids using healthy populations and makes the ageing rate determined directly by biologically-related process, it remains unclear which age-related phenotypes best represent the ageing of the body. Some raised that the best way to define biological age may be mortality risk since this metric is an absolute objective estimate of the overall ageing outcome. However, very few models are developed based on mortality risk due to the lack of large-scale longitudinal data. Regarding the subjective assessment of perceived health, the performance of the ageing biomarker may be subject to recall bias introduced by raters.

Data sources for age prediction

This review has classified the data sources of ageing biomarkers into three types: molecular and cellular tests, medical imaging, and other clinical measures. Molecular and cellular biomarkers are derived from human blood or other tissues analyzed either by clinical laboratory tests or multi-omics technology. Imaging-based markers are based on medical imaging for clinical practice such as brain MRI, ECG, and fundus images. Other clinical measures such as physical assessments and functional markers are also commonly evaluated sources for biological age prediction. Composite biomarkers integrating different types of data sources to predict ageing have also been proposed. We also searched patents claiming ageing biomarkers and summarized the relevant patents in Supporting Information: Table .

Given molecular and cellular biomarkers based on molecular levels, the mechanism underlying the biomarkers is easier to interpret than imaging and clinical profiles. However, heterogeneity, access, and reproducibility of methodologies and analytical protocols may lead to batch-to-batch variations. In fact, there is little overlap of the molecules used in the “same” ageing clocks from different studies. Additionally, the biological ageing rate in one tissue can be quite different from that of another and it is not realistic to obtain a combination of age estimates in several tissues.

Imaging-based markers have the advantage of not requiring invasive sample collection and can be readily acquired. Previous age models need image-processing, which was time-consuming and labour-intensive. Recent emergence of artificial intelligence techniques, especially deep learning, could enable age predictions from raw and unprocessed data while achieving high accuracy. However, the reliability of AI algorithms is still limited due to their “black box” nature.

Other clinical measures such as physical assessment or functional tests are readily assessable and lend themselves as decent initial screening tools capable of identifying individuals at risk of unhealthy ageing in a clinical setting. However, physical assessments only reflect the functional status of one organ system, such as spirometry used in the estimation of lung age.

An integration of different types of data in composite biomarkers can reflect the multiple traceable footprints across many different levels and body systems, which improves the quantification of the overall impact of ageing. However, this requires strong computing power and advanced algorithm.

Model performance accuracy

As discussed in Part 2, previous studies have tried to identify a better-performing marker by comparing the accuracy indicators (e.g., MAE) of ageing biomarkers before incorporating measures of biological age into clinical applications. Although various statistical approaches have been proposed to improve the model's accuracy, some concerns need to be noted. One concern is the dilute effect; that is the age of younger subjects is overestimated while the age of older subjects is underestimated because the models are often influenced by the mean. This may cause a negative correlation between age gap and chronological age, leading to spurious associations between age gap and clinical outcomes.

Another concern is that improved accuracy in predicting chronological age may not be beneficial for age gap reflecting rates of biological ageing. There is still a pitfall that the age gap may be just a measurement error after subtraction or regression. Zhang's group reported that as the accuracy of chronological age prediction increases using an epigenetic clock, the association between biological age gap and mortality or other age-related outcomes attenuates. This paradox may remind us that the performance of the model should not be evaluated mainly by accuracy in predicting age but by the value in revealing ageing phenotypes.

Clinical value validation

Currently, the clinical evaluation for ageing rate estimator is based on health or ageing-related diseases associated parameters. While most estimators mentioned in this review showed associations with health parameters, few studies have compared the validity of different ageing biomarkers predicting health outcomes. Such comparative head-to-head studies incorporating different markers in one study are still lacking,- due to the lack of large cohorts with different data types and difficult access to different algorithms for age estimators.

It is important to note that the vast majority of current ageing biomarkers have been validated on cross-sectional data, thus causal insights into ageing biomarkers have not been studied until recently. Emerging studies of longitudinal cohorts have also provided supporting evidence for the predictive value of established ageing biomarkers in predicting poor health outcomes, (e.g., brain age in predicting age-related strength loss, and retinal age in predicting incident neurodegenerative diseases). More longitudinal human studies are still warranted with a larger sample size to address questions on the utility of ageing biomarkers in the prediction of pathological ageing and age-related outcomes.

As estimates of ageing were only captured at a single point in current studies, few studies have investigated the dynamic change in these ageing biomarkers in response to environmental factors or antiageing interventions. Protective and risk factors could be identified by comparing the dynamic changes of the ageing biomarkers. Therefore, tracking and reporting the long-term effects of anti-ageing interventions on the biological ageing rate indexed by biomarkers remain as major focuses of future research.

To effectively tackle the challenges posed by ageing research, it is imperative to concentrate efforts, promote multidisciplinary communication, and encourage collaboration. In recognition of this need, the Biomarkers of Aging Consortium () was recently established by experts in fields such as ageing and longevity, cellular rejuvenation, and multi-omic ageotyping. Given the emerging trends in ageing research, the consortium has emphasized the necessity of systematic validation efforts to identify and establish reliable ageing biomarkers. This requires addressing the challenges posed by the compatibility and generalizability of current biomarkers, as well as the need to evaluate the efficacy of longevity interventions.

OUTLOOK

Research on ageing biomarkers has led to significant advances and applications, but much work is still needed to further explore their potential in the future. Breakthroughs are expected in several areas including exploring potential mechanisms, constructing potential biomarkers by combining different data sources or applying new technologies, and validating the clinical value of existing and emerging biomarkers through extensive collaboration and longitudinal study investigation.

Although clinical and population studies have suggested several biomarkers of ageing, we still need laboratory research to elucidate the biological processes and molecular mechanisms involved. Laboratory work is underway to reveal the remarkable malleability of ageing. For example, to investigate how the alterations in CpG sites affect downstream physiological age-related changes, in vitro work is warranted to identify key relevant molecules (e.g. hypoxia-related factors and key methylation enzymes) during epigenetic reprogramming. Using experimental vertebrate animals, DNAm age estimators across species can advance our understanding of epigenetic ageing and contribute to the future development of anti-ageing interventions. Current emerging genetic methodologies, such as Mendelian randomization, are effective in revealing the deep causality of ageing clock theories, providing support for further interpretation of mechanisms. The Aging Biomarker Consortium conducted a thorough review that comprehensively evaluated age-related pathophysiological, functional, and morphological changes at the cellular and organ levels, which could serve as a significant foundation for future research.

Standardized data collection and combination of multimodality data are needed in the future investigation of constructing potential biomarkers. Since each “level” or type of biomarker conveys different information about biological ageing, to obtain optimal performance and biological relevance, multiple data involving a plethora of modalities and functions may have to be used to track multimodality-specific ageing patterns. Michael Snyder and his colleagues have adopted deep multi-omics data to profile “Ageotype” as a pattern of personal ageing. Based on such a combination of multimodality data, existing ageing research centers should be engaged to develop standardized measures and methods across research teams, allowing the pooling of study populations and results.

Applying new technologies might also help constructing potential biomarkers. Advances in AI, such as machine learning and DL, may provide advocated solutions to untangle the complexity of ageing. AI-based models have unique advantages in handling a large amount of data and show better performance than traditional regression models. With the application of new algorithms such as generative adversarial network (GAN), we could generate medical images of a person at an older age that present age-related changes in a more intuitive way.

To further validate the clinical value of existing and emerging biomarkers, constructing different ageing biomarkers in one cohort should be explored to characterize how these biomarkers interact with each and compare their validity in predicting ageing-related health outcomes. Moreover, changes in the different ageing biomarkers over time should also be recorded for tracking protective or risk factors and identifying effective antiageing interventions. Future research should focus on longitudinal studies with a larger sample size to address challenges on the utility of ageing biomarkers in the prediction of pathological ageing and age-related outcomes. Measures of resilience can also be included since resilience is a key feature of intrinsic ageing while age-related outcomes only document damage accumulation over time.

AUTHOR CONTRIBUTIONS

Outline of the review: Zhu ZT, He MG, Gao YX, Cheng CY, Ge ZY, Clark M. Performed the search: Chen RY, Wang YY, Zhang SR. Drafting of the manuscript: Chen RY, Wang YY, Zhang SR, Bulloch G, Zhang JY. Critical revision of the manuscript: Chen RY, Wang YY, Zhang SR, Bulloch G, Zhang JY, Liao H, Shang XW, Peng QS. Obtained funding: He MG, Zhu ZT. Administrative, technical, or material support: Zhu ZT, He MG. All authors have read and approved the final manuscript.

ACKNOWLEDGMENTS

This work was supported by the National Natural Science Foundation of China (82101173), High-level Talent Flexible Introduction Fund of Guangdong Provincial People's Hospital (KJ012019530), Fundamental Research Funds of the State Key Laboratory of Ophthalmology (303060202400362), NHMRC Investigator Grants (2010072), and Research Accelerator Program of University of Melbourne.

CONFLICTS OF INTEREST STATEMENT

Yuanxu Gao is an Editorial Staff of MedComm-Future Medicine. Author Yuanxu Gao was not involved in the journal's review or decisions related to this manuscript. The other authors declared no conflict of interest.

DATA AVAILABILITY STATEMENT

The authors have nothing to report.

ETHICS STATEMENT

The authors have nothing to report.

References

United Nations, Department of Economic and Social Affairs, Population Division. World population ageing 2017, 2017. Accessed January 10, 2023. https://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2019-Highlights.pdf

Partridge L, Deelen J, Slagboom PE. Facing up to the global challenges of ageing. Nature. 2018;561(7721):45‐56.

Vos T, Flaxman AD, Naghavi M, et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990‐2010: a systematic analysis for the global burden of disease study 2010. The Lancet. 2012;380(9859):2163‐2196.

Tan CC, Lam CSP, Matchar DB, Zee YK, Wong JEL. Singapore's health‐care system: key features, challenges, and shifts. The Lancet. 2021;398(10305):1091‐104.

Yon Y, Mikton CR, Gassoumis ZD, Wilber KH. Elder abuse prevalence in community settings: a systematic review and meta‐analysis. The Lancet Global Health. 2017;5(2):e147‐e156.

Rosen D, Smith ML, Reynolds 3rd CF. The prevalence of mental and physical health disorders among older methadone patients. Am J Geriatr Psychiatry. 2008;16(6):488‐497.

Lowsky DJ, Olshansky SJ, Bhattacharya J, Goldman DP. Heterogeneity in healthy aging. J Gerontol A Biol Sci Med Sci. 2014;69(6):640‐649.

Zhu Z, Hu W, Chen R, et al. Retinal age gap as a predictive biomarker of stroke risk. BMC Med. 2022;20(1):466.

Jones DT, Lee J, Topol EJ. Digitising brain age. The Lancet. 2022;400(10357):988.

Scott LC, Yang Q, Dowling NF, Richardson LC. Predicted heart age among cancer survivors—United States, 2013‐2017. MMWR Morb Mortal Wkly Rep. 2021;70(1):1‐6.

United Nations, Department of Economic and Social Affairs, Population Division. World population ageing 2017, 2017. Accessed January 10, 2023. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health

Poulain M, Herm A, Pes GM. The Blue Zones: Areas of Exceptional Longevity Around the World. Vienna Yearbook of Population Research. 2013;11:87‐108.

Gott A, Andrews C, Larriva Hormigos M, Spencer K, Bateson M, Nettle D. Chronological age, biological age, and individual variation in the stress response in the European starling: a follow‐up study. PeerJ. 2018;6: [eLocator: e5842].

Jackson SHD, Weale MR, Weale RA. Biological age—what is it and can it be measured? Arch Gerontol Geriat. 2003;36(2):103‐115.

Baker 3rd GT, Sprott RL. Biomarkers of aging. Exp Geront. 1988;23(4‐5):223‐239.

Diebel LWM, Rockwood K. Determination of biological age: geriatric assessment vs. biological biomarkers. Curr Oncol Rep. 2021;23(9):104.

Rutledge J, Oh H, Wyss‐Coray T. Measuring biological age using omics data. Nat Rev Genet. 2022;23(12):715‐727.

Butler RN, Sprott R, Warner H, et al. Aging: the reality: biomarkers of aging: from primitive organisms to humans. J Gerontol A Biol Sci Med Sci. 2004;59(6):B560‐B567.

Jylhävä J, Pedersen NL, Hägg S. Biological age predictors. EBioMedicine. 2017;21:29‐36.

Li Y, Huang Z, Dong X, et al. Forensic age estimation for pelvic X‐ray images using deep learning. Eur Radiol. 2019;29(5):2322‐2329.

Higgins‐Chen AT, Thrush KL, Levine ME. Aging biomarkers and the brain. Semin Cell Dev Biol. 2021;116:180‐193.

Xia X, Chen X, Wu G, et al. Three‐dimensional facial‐image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle. Nature Metabolism. 2020;2(9):946‐957.

Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10(4):573‐591.

Cole JH, Ritchie SJ, Bastin ME, et al. Brain age predicts mortality. Mol Psychiatry. 2018;23(5):1385‐1392.

Wood DA, Kafiabadi S, Busaidi AA, et al. Accurate brain‐age models for routine clinical MRI examinations. Neuroimage. 2022;249: [eLocator: 118871].

Cole JH, Poudel RPK, Tsagkrasoulis D, et al. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage. 2017;163:115‐124.

Christman S, Bermudez C, Hao L, et al. Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression. Transl Psychiatry. 2020;10(1):317.

Richard G, Kolskår K, Ulrichsen KM, et al. Brain age prediction in stroke patients: highly reliable but limited sensitivity to cognitive performance and response to cognitive training. Neuroimage Clin. 2020;25: [eLocator: 102159].

Wang J, Knol MJ, Tiulpin A, et al. Gray matter age prediction as a biomarker for risk of dementia. Proc Natl Acad Sci. 2019;116(42):21213‐21218.

Griffith JD, Comeau L, Rosenfield S, et al. Mammalian telomeres end in a large duplex loop. Cell. 1999;97(4):503‐514.

Harley CB, Futcher AB, Greider CW. Telomeres shorten during ageing of human fibroblasts. Nature. 1990;345(6274):458‐460.

Ren F, Li C, Xi H, Wen Y, Huang K. Estimation of human age according to telomere shortening in peripheral blood leukocytes of Tibetan. Am J Foren Med Pathol. 2009;30(3):252‐255.

Hastie ND, Dempster M, Dunlop MG, Thompson AM, Green DK, Allshire RC. Telomere reduction in human colorectal carcinoma and with ageing. Nature. 1990;346(6287):866‐868.

Cherdsukjai P, Buddhachat K, Brown J, et al. Age relationships with telomere length, body weight and body length in wild dugong (Dugong dugon). PeerJ. 2020;8: [eLocator: e10319].

Tsuji A, Ishiko A, Takasaki T, Ikeda N. Estimating age of humans based on telomere shortening. Forensic Sci Int. 2002;126(3):197‐199.

Bountziouka V, Nelson CP, Codd V, et al. Association of shorter leucocyte telomere length with risk of frailty. J Cachexia Sarcopenia Muscle. 2022;13(3):1741‐51.

Nguyen MT, Saffery R, Burgner D, et al. Telomere length and lung function in a population‐based cohort of children and mid‐life adults. Pediatr Pulmonol. 2019;54(12):2044‐2052.

Westbrook A, Zhang R, Shi M, et al. Association between baseline buccal telomere length and progression of kidney function: the health and retirement study. J Gerontol Series A. 2022;77(3):471‐476.

Li Y, Cheang I, Zhang Z, et al. Prognostic association of TERC, TERT gene polymorphism, and leukocyte telomere length in acute heart failure: a prospective study. Front Endocrinol. 2021;12: [eLocator: 650922].

Xiao F, Zheng X, Cui M, et al. Telomere dysfunction‐related serological markers are associated with type 2 diabetes. Diabetes Care. 2011;34(10):2273‐2278.

Zhan Y, Karlsson IK, Karlsson R, et al. Exploring the causal pathway from telomere length to coronary heart disease: a network Mendelian randomization study. Circ Res. 2017;121(3):214‐219.

Sanchez‐Espiridion B, Chen M, Chang JY, et al. Telomere length in peripheral blood leukocytes and lung cancer risk: a large case‐control study in Caucasians. Cancer Res. 2014;74(9):2476‐2486.

Machiela MJ, Lan Q, Slager SL, et al. Genetically predicted longer telomere length is associated with increased risk of B‐cell lymphoma subtypes. Hum Mol Gen. 2016;25(8):1663‐1676.

Rossi D, Lobetti Bodoni C, Genuardi E, et al. Telomere length is an independent predictor of survival, treatment requirement and Richter's syndrome transformation in chronic lymphocytic leukemia. Leukemia. 2009;23(6):1062‐1072.

Needham BL, Rehkopf D, Adler N, et al. Leukocyte telomere length and mortality in The National Health and Nutrition Examination Survey, 1999‐2002. Epidemiology. 2015;26(4):528‐535.

Deelen J, Beekman M, Codd V, et al. Leukocyte telomere length associates with prospective mortality independent of immune‐related parameters and known genetic markers. Int J Epidemiol. 2014;43(3):878‐886.

Bakaysa SL, Mucci LA, Slagboom PE, et al. Telomere length predicts survival independent of genetic influences. Aging Cell. 2007;6(6):769‐774.

Haycock PC, Heydon EE, Kaptoge S, Butterworth AS, Thompson A, Willeit P. Leucocyte telomere length and risk of cardiovascular disease: systematic review and meta‐analysis. BMJ. 2014;349:g4227.

Chen R, Zhan Y. Association between telomere length and Parkinson's disease: a Mendelian randomization study. Neurobiol Aging. 2021;97:144.e9‐144.e11.

Forero DA, González‐Giraldo Y, López‐Quintero C, Castro‐Vega LJ, Barreto GE, Perry G. Telomere length in Parkinson's disease: a meta‐analysis. Exp Geront. 2016;75:53‐55.

D'Mello MJJ, Ross SA, Briel M, Anand SS, Gerstein H, Paré G. Association between shortened leukocyte telomere length and cardiometabolic outcomes: systematic review and meta‐analysis. Circ Cardiovasc Genet. 2015;8(1):82‐90.

Zhu X, Han W, Xue W, et al. The association between telomere length and cancer risk in population studies. Sci Rep. 2016;6: [eLocator: 22243].

Wang Q, Zhan Y, Pedersen NL, Fang F, Hägg S. Telomere length and all‐cause mortality: a meta‐analysis. Ageing Res Rev. 2018;48:11‐20.

Sanders JL, Newman AB. Telomere length in epidemiology: a biomarker of aging, age‐related disease, both, or neither? Epidemiol Rev. 2013;35(1):112‐131.

Hasin Y, Seldin M, Lusis A. Multi‐omics approaches to disease. Genome Biol. 2017;18(1):83.

Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discovery. 2016;15(7):473‐484.

LaFramboise T. Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances. Nucleic Acids Res. 2009;37(13):4181‐4193.

Dor Y, Cedar H. Principles of DNA methylation and their implications for biology and medicine. The Lancet. 2018;392(10149):777‐786.

Horvath S, Raj K. DNA methylation‐based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018;19(6):371‐384.

Reale A, Tagliatesta S, Zardo G, Zampieri M. Counteracting aged DNA methylation states to combat ageing and age‐related diseases. Mech Ageing Dev. 2022;206: [eLocator: 111695].

Noroozi R, Ghafouri‐Fard S, Pisarek A, et al. DNA methylation‐based age clocks: from age prediction to age reversion. Ageing Res Rev. 2021;68: [eLocator: 101314].

Gillespie SL, Hardy LR, Anderson CM. Patterns of DNA methylation as an indicator of biological aging: state of the science and future directions in precision health promotion. Nurs Outlook. 2019;67(4):337‐344.

Seale K, Horvath S, Teschendorff A, Eynon N, Voisin S. Making sense of the ageing methylome. Nat Rev Genet. 2022;23(10):585‐605.

Weidner C, Lin Q, Koch C, et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014;15(2):R24.

Zhang Q, Vallerga CL, Walker RM, et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Med. 2019;11(1):54.

Alisch RS, Barwick BG, Chopra P, et al. Age‐associated DNA methylation in pediatric populations. Genome Res. 2012;22(4):623‐632.

Boks MP, Derks EM, Weisenberger DJ, et al. The relationship of DNA methylation with age, gender and genotype in twins and healthy controls. PLoS One. 2009;4(8): [eLocator: e6767].

Christensen BC, Houseman EA, Marsit CJ, et al. Aging and environmental exposures alter tissue‐specific DNA methylation dependent upon CpG island context. PLoS Genet. 2009;5(8): [eLocator: e1000602].

Bollati V, Schwartz J, Wright R, et al. Decline in genomic DNA methylation through aging in a cohort of elderly subjects. Mech Ageing Dev. 2009;130(4):234‐239.

Esteller M. Epigenetics in cancer. N Engl J Med. 2008;358(11):1148‐1159.

Hannum G, Guinney J, Zhao L, et al. Genome‐wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359‐67.

Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115.

Soriano‐Tárraga C, Giralt‐Steinhauer E, Mola‐Caminal M, et al. Ischemic stroke patients are biologically older than their chronological age. Aging. 2016;8(11):2655‐2666.

Ammous F, Zhao W, Ratliff SM, et al. Epigenetic age acceleration is associated with cardiometabolic risk factors and clinical cardiovascular disease risk scores in African Americans. Clin Epigenetics. 2021;13(1):55.

Sillanpää E, Laakkonen EK, Vaara E, et al. Biological clocks and physical functioning in monozygotic female twins. BMC Geriatr. 2018;18(1):83.

Marioni RE, Shah S, McRae AF, et al. The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936. Int J Epidemiol. 2015;44(4):1388‐1396.

Bressler J, Marioni RE, Walker RM, et al. Epigenetic age acceleration and cognitive function in African American adults in midlife: the atherosclerosis risk in communities study. J Gerontol Series A. 2020;75(3):473‐480.

Wiesman AI, Rezich MT, O'Neill J, et al. Epigenetic markers of aging predict the neural oscillations serving selective attention. Cerebral Cortex. 2020;30(3):1234‐1243.

Breitling LP, Saum KU, Perna L, Schöttker B, Holleczek B, Brenner H. Frailty is associated with the epigenetic clock but not with telomere length in a German cohort. Clin Epigenetics. 2016;8:21.

Liu Z, Leung D, Thrush K, et al. Underlying features of epigenetic aging clocks in vivo and in vitro. Aging Cell. 2020;19(10): [eLocator: e13229].

Zhang Y, Wilson R, Heiss J, et al. DNA methylation signatures in peripheral blood strongly predict all‐cause mortality. Nat Commun. 2017;8: [eLocator: 14617].

Fohr T, Waller K, Viljanen A, et al. Does the epigenetic clock GrimAge predict mortality independent of genetic influences: an 18 year follow‐up study in older female twin pairs. Clin Epigenetics. 2021;13(1):128.

Marioni RE, Shah S, McRae AF, et al. DNA methylation age of blood predicts all‐cause mortality in later life. Genome Biol. 2015;16(1):25.

Perna L, Zhang Y, Mons U, Holleczek B, Saum KU, Brenner H. Epigenetic age acceleration predicts cancer, cardiovascular, and all‐cause mortality in a German case cohort. Clin Epigenetics. 2016;8:64.

Beynon RA, Ingle SM, Langdon R, et al. Epigenetic biomarkers of ageing are predictive of mortality risk in a longitudinal clinical cohort of individuals diagnosed with oropharyngeal cancer. Clin Epigenetics. 2022;14(1):1.

Molinaro AM, Wiencke JK, Warrier G, et al. Interactions of age and blood immune factors and noninvasive prediction of glioma survival. J Natl Cancer Inst. 2022;114(3):446‐457.

Lu X, Zhou Y, Meng J, et al. Epigenetic age acceleration of cervical squamous cell carcinoma converged to human papillomavirus 16/18 expression, immunoactivation, and favourable prognosis. Clin Epigenetics. 2020;12(1):23.

Ren JT, Wang MX, Su Y, Tang LY, Ren ZF. Decelerated DNA methylation age predicts poor prognosis of breast cancer. BMC Cancer. 2018;18(1):989.

Liao P, Ostrom QT, Stetson L, Barnholtz‐Sloan JS. Models of epigenetic age capture patterns of DNA methylation in glioma associated with molecular subtype, survival, and recurrence. Neuro‐Oncology. 2018;20(7):942‐953.

Levine ME, Hosgood HD, Chen B, Absher D, Assimes T, Horvath S. DNA methylation age of blood predicts future onset of lung cancer in the women's health initiative. Aging. 2015;7(9):690‐700.

Roberts JD, Vittinghoff E, Lu AT, et al. Epigenetic age and the risk of incident atrial fibrillation. Circulation. 2021;144(24):1899‐1911.

Roetker NS, Pankow JS, Bressler J, Morrison AC, Boerwinkle E. Prospective study of epigenetic age acceleration and incidence of cardiovascular disease outcomes in the ARIC study (Atherosclerosis Risk in Communities). Circulation: Genomic and Precision Medicine. 2018;11(3): [eLocator: e001937].

Soriano‐Tárraga C, Mola‐Caminal M, Giralt‐Steinhauer E, et al. Biological age is better than chronological as predictor of 3‐month outcome in ischemic stroke. Neurology. 2017;89(8):830‐836.

Degerman S, Josefsson M, Nordin Adolfsson A, et al. Maintained memory in aging is associated with young epigenetic age. Neurobiol Aging. 2017;55:167‐171.

Levine ME, Lu AT, Bennett DA, Horvath S. Epigenetic age of the pre‐frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer's disease‐related cognitive functioning. Aging. 2015;7(12):1198‐1211.

Horvath S, Ritz BR. Increased epigenetic age and granulocyte counts in the blood of Parkinson's disease patients. Aging. 2015;7(12):1130‐1142.

Yang R, Wu GWY, Verhoeven JE, et al. A DNA methylation clock associated with age‐related illnesses and mortality is accelerated in men with combat PTSD. Mol Psychiatry. 2021;26(9):4999‐5009.

Verschoor CP, Lin DTS, Kobor MS, et al. Epigenetic age is associated with baseline and 3‐year change in frailty in the Canadian longitudinal study on aging. Clin Epigenetics. 2021;13(1):163.

Dong Z, Chen Y. Transcriptomics: advances and approaches. Science China Life Sciences. 2013;56(10):960‐967.

Hoagland MB, Stephenson ML, Scott JF, Hecht LI, Zamecnik PC. A soluble ribonucleic acid intermediate in protein synthesis. J Biol Chem. 1958;231(1):241‐257.

Mardis ER. Next‐generation DNA sequencing methods. Annu Rev Genomics Hum Genet. 2008;9:387‐402.

van den Akker EB, Passtoors WM, Jansen R, et al. Meta‐analysis on blood transcriptomic studies identifies consistently coexpressed protein‐protein interaction modules as robust markers of human aging. Aging Cell. 2014;13(2):216‐225.

Glass D, Viñuela A, Davies MN, et al. Gene expression changes with age in skin, adipose tissue, blood and brain. Genome Biol. 2013;14(7):R75.

Kent Jr. JW, Göring HHH, Charlesworth JC, et al. Genotype×age interaction in human transcriptional ageing. Mech Ageing Dev. 2012;133(9‐10):581‐590.

Zhang H, Yang H, Zhang C, et al. Investigation of microRNA expression in human serum during the aging process. J Gerontol Series A. 2015;70(1):102‐109.

Small EM, Olson EN. Pervasive roles of microRNAs in cardiovascular biology. Nature. 2011;469(7330):336‐342.

Lu J, Getz G, Miska EA, et al. MicroRNA expression profiles classify human cancers. Nature. 2005;435(7043):834‐838.

Harries LW, Hernandez D, Henley W, et al. Human aging is characterized by focused changes in gene expression and deregulation of alternative splicing. Aging Cell. 2011;10(5):868‐878.

Fleischer JG, Schulte R, Tsai HH, et al. Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol. 2018;19(1):221.

Peters MJ, Joehanes R, Pilling LC, et al. The transcriptional landscape of age in human peripheral blood. Nat Commun. 2015;6:8570.

Holzscheck N, Falckenhayn C, Söhle J, et al. Modeling transcriptomic age using knowledge‐primed artificial neural networks. NPJ Aging Mech Dis. 2021;7(1):15.

Huan T, Chen G, Liu C, et al. Age‐associated microRNA expression in human peripheral blood is associated with all‐cause mortality and age‐related traits. Aging Cell. 2018;17(1): [eLocator: e12687].

Fang C, Liu X, Zhao J, et al. Age estimation using bloodstain miRNAs based on massive parallel sequencing and machine learning: a pilot study. Forensic Sci Int: Genet. 2020;47: [eLocator: 102300].

Holly AC, Melzer D, Pilling LC, et al. Towards a gene expression biomarker set for human biological age. Aging Cell. 2013;12(2):324‐326.

Zhang Z, Wu S, Stenoien DL, Paša‐Tolić L. High‐throughput proteomics. Annu Rev Anal Chem. 2014;7:427‐454.

Ramazi S, Allahverdi A, Zahiri J. Evaluation of post‐translational modifications in histone proteins: a review on histone modification defects in developmental and neurological disorders. J Biosci. 2020;45:135.

Schwanhäusser B, Busse D, Li N, et al. Global quantification of mammalian gene expression control. Nature. 2011;473(7347):337‐342.

Hennrich ML, Romanov N, Horn P, et al. Cell‐specific proteome analyses of human bone marrow reveal molecular features of age‐dependent functional decline. Nat Commun. 2018;9(1):4004.

Tanaka T, Biancotto A, Moaddel R, et al. Plasma proteomic signature of age in healthy humans. Aging Cell. 2018;17(5): [eLocator: e12799].

Waldera‐Lupa DM, Kalfalah F, Florea AM, et al. Proteome‐wide analysis reveals an age‐associated cellular phenotype of in situ aged human fibroblasts. Aging. 2014;6(10):856‐872.

Menni C, Kiddle SJ, Mangino M, et al. Circulating proteomic signatures of chronological age. J Gerontol Series A. 2015;70(7):809‐816.

Baird GS, Nelson SK, Keeney TR, et al. Age‐dependent changes in the cerebrospinal fluid proteome by slow off‐rate modified aptamer array. Am J Pathol. 2012;180(2):446‐456.

Krištić J, Vučković F, Menni C, et al. Glycans are a novel biomarker of chronological and biological ages. J Gerontol Series A. 2014;69(7):779‐789.

Yu X, Wang Y, Kristic J, et al. Profiling IgG N‐glycans as potential biomarker of chronological and biological ages: A community‐based study in a Han Chinese population. Medicine. 2016;95(28): [eLocator: e4112].

Sathyan S, Ayers E, Gao T, et al. Plasma proteomic profile of age, health span, and all‐cause mortality in older adults. Aging Cell. 2020;19(11): [eLocator: e13250].

Johnson AA, Shokhirev MN, Wyss‐Coray T, Lehallier B. Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age. Ageing Res Rev. 2020;60: [eLocator: 101070].

Lehallier B, Gate D, Schaum N, et al. Undulating changes in human plasma proteome profiles across the lifespan. Nature Med. 2019;25(12):1843‐1850.

Panyard DJ, Yu B, Snyder MP. The metabolomics of human aging: advances, challenges, and opportunities. Sci Adv. 2022;8(42): [eLocator: eadd6155].

Nicholson JK, Foxall PJD, Spraul M, Farrant RD, Lindon JC. 750 MHz 1H and 1H‐13C NMR spectroscopy of human blood plasma. Anal Chem. 1995;67(5):793‐811.

Menni C, Kastenmüller G, Petersen AK, et al. Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int J Epidemiol. 2013;42(4):1111‐1119.

Lawton KA, Berger A, Mitchell M, et al. Analysis of the adult human plasma metabolome. Pharmacogenomics. 2008;9(4):383‐397.

Rist MJ, Roth A, Frommherz L, et al. Metabolite patterns predicting sex and age in participants of the Karlsruhe metabolomics and nutrition (KarMeN) study. PLoS One. 2017;12(8): [eLocator: e0183228].

Vaarhorst AAM, Beekman M, Suchiman EHD, et al. Lipid metabolism in long‐lived families: the Leiden longevity study. Age. 2011;33(2):219‐227.

Kochhar S, Jacobs DM, Ramadan Z, Berruex F, Fuerholz A, Fay LB. Probing gender‐specific metabolism differences in humans by nuclear magnetic resonance‐based metabonomics. Anal Biochem. 2006;352(2):274‐281.

Darst BF, Koscik RL, Hogan KJ, Johnson SC, Engelman CD. Longitudinal plasma metabolomics of aging and sex. Aging. 2019;11(4):1262‐1282.

Yu Z, Zhai G, Singmann P, et al. Human serum metabolic profiles are age dependent. Aging Cell. 2012;11(6):960‐967.

Hertel J, Friedrich N, Wittfeld K, et al. Measuring biological age via metabonomics: the metabolic age score. J Proteome Res. 2016;15(2):400‐410.

Robinson O, Chadeau Hyam M, Karaman I, et al. Determinants of accelerated metabolomic and epigenetic aging in a UK cohort. Aging Cell. 2020;19(6): [eLocator: e13149].

van den Akker EB, Trompet S, Barkey Wolf JJH, et al. Metabolic age based on the BBMRI‐NL (1)H‐NMR metabolomics repository as biomarker of age‐related disease. Circ Genom Precis Med. 2020;13(5):541‐547.

de Vos WM, Tilg H, Van Hul M, Cani PD. Gut microbiome and health: mechanistic insights. Gut. 2022;71(5):1020‐1032.

Biagi E, Candela M, Turroni S, Garagnani P, Franceschi C, Brigidi P. Ageing and gut microbes: perspectives for health maintenance and longevity. Pharmacol Res. 2013;69(1):11‐20.

Cheng J, Palva AM, de Vos WM, Satokari R. Contribution of the intestinal microbiota to human health: from birth to 100 years of age. Curr Top Microbiol Immunol. 2013;358:323‐346.

Collino S, Montoliu I, Martin FPJ, et al. Metabolic signatures of extreme longevity in Northern Italian centenarians reveal a complex remodeling of lipids, amino acids, and gut microbiota metabolism. PLoS One. 2013;8(3): [eLocator: e56564].

Galkin F, Mamoshina P, Aliper A, et al. Human gut microbiome aging clock based on taxonomic profiling and deep learning. iScience. 2020;23(6): [eLocator: 101199].

Gopu V, Cai Y, Krishnan S, et al. An accurate aging clock developed from the largest dataset of microbial and human gene expression reveals molecular mechanisms of aging. bioRxiv. 2020. [DOI: https://dx.doi.org/10.1101/2020.09.17.301887]

Phillip JM, Wu PH, Gilkes DM, et al. Biophysical and biomolecular determination of cellular age in humans. Nat Biomed Eng. 2017;1(7):0093.

Phillip JM, Aifuwa I, Walston J, Wirtz D. The mechanobiology of aging. Annu Rev Biomed Eng. 2015;17(1):113‐141.

Starodubtseva MN. Mechanical properties of cells and ageing. Ageing Res Rev. 2011;10(1):16‐25.

Hu M, Wang J, Zhao H, Dong S, Cai J. Nanostructure and nanomechanics analysis of lymphocyte using AFM: from resting, activated to apoptosis. J Biomech. 2009;42(10):1513‐1519.

Chouinard JA, Grenier G, Khalil A, Vermette P. Oxidized‐LDL induce morphological changes and increase stiffness of endothelial cells. Exp Cell Res. 2008;314(16):3007‐3016.

Lieber SC, Aubry N, Pain J, Diaz G, Kim SJ, Vatner SF. Aging increases stiffness of cardiac myocytes measured by atomic force microscopy nanoindentation. Am J Physiol Heart Circ Physiol. 2004;287(2):H645‐H651.

Nash GB, Wyard SJ. Erythrocyte membrane elasticity during in vivo ageing. Biochim Biophys Acta. 1981;643(2):269‐275.

González‐Bermúdez B, Abarca‐Ortega A, González‐Sánchez M, De la Fuente M, Plaza GR. Possibilities of using T‐cell biophysical biomarkers of ageing. Expert Rev Mol Med. 2022;24: [eLocator: e35].

Blann A. Blood tests and age‐related changes in older people. Nurs Times. 2014;110(7):22‐23.

Kubota K, Shirakura T, Orui T, et al. Change in the blood cell counts with age. Nippon Ronen Igakkai Zasshi. 1991;28(4):509‐514.

Mahlknecht U, Kaiser S. Age‐related changes in peripheral blood counts in humans. Exp Ther Med. 2010;1(6):1019‐1025.

Suwannuruks R, Soisamrong A, Hanchana U, Vanich‐Angkul V, Jopang Y. Hematologic parameters in Thai subjects over 50 years old. J Med Assoc Thai. 1997;80(suppl 1):S76‐S80.

Cieslak KP, Baur O, Verheij J, Bennink RJ, van Gulik TM. Liver function declines with increased age. HPB. 2016;18(8):691‐696.

Videan EN, Fritz J, Murphy J. Effects of aging on hematology and serum clinical chemistry in chimpanzees (Pan troglodytes). Am J Primatol. 2008;70(4):327‐338.

Petroff D, Bätz O, Jedrysiak K, Kramer J, Berg T, Wiegand J. Age dependence of liver enzymes: an analysis of over 1,300,000 consecutive blood samples. Clin Gastroenterol Hepatol. 2022;20(3):641‐650.

Putin E, Mamoshina P, Aliper A, et al. Deep biomarkers of human aging: application of deep neural networks to biomarker development. Aging. 2016;8(5):1021‐1033.

Mamoshina P, Kochetov K, Cortese F, et al. Blood biochemistry analysis to detect smoking status and quantify accelerated aging in smokers. Sci Rep. 2019;9(1):142.

Mamoshina P, Kochetov K, Putin E, et al. Population specific biomarkers of human aging: a big data study using south Korean, Canadian, and Eastern European patient populations. J Gerontol Series A. 2018;73(11):1482‐1490.

Gialluisi A, Di Castelnuovo A, Costanzo S, et al. Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing. Eur J Epidemiol. 2022;37(1):35‐48.

Peretz L, Rappoport N. Deviation of physiological from chronological age is associated with health. Stud Health Technol Inform. 2022;294:224‐228.

Wu JW, Yaqub A, Ma Y, et al. Biological age in healthy elderly predicts aging‐related diseases including dementia. Sci Rep. 2021;11(1): [eLocator: 15929].

Pawelec G. Age and immunity: what is “immunosenescence”? Exp Geront. 2018;105:4‐9.

Yousefzadeh MJ, Flores RR, Zhu Y, et al. An aged immune system drives senescence and ageing of solid organs. Nature. 2021;594(7861):100‐105.

Franceschi C, Bonafè M, Valensin S, et al. Inflamm‐aging. An evolutionary perspective on immunosenescence. Ann NY Acad Sci. 2000;908:244‐254.

Franceschi C, Campisi J. Chronic inflammation (inflammaging) and its potential contribution to age‐associated diseases. J Gerontol A Biol Sci Med Sci. 2014;69(suppl 1):S4‐S9.

Alpert A, Pickman Y, Leipold M, et al. A clinically meaningful metric of immune age derived from high‐dimensional longitudinal monitoring. Nature Med. 2019;25(3):487‐495.

Lambert K, Moo KG, Arnett A, et al. Deep immune phenotyping reveals similarities between aging, Down syndrome, and autoimmunity. Sci Transl Med. 2022;14(627): [eLocator: eabi4888].

Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol. 2018;15(9):505‐522.

Ahmadi‐Abhari S, Luben RN, Wareham NJ, Khaw KT. Distribution and determinants of C‐reactive protein in the older adult population: European prospective investigation into Cancer‐Norfolk study. Eur J Clin Invest. 2013;43(9):899‐911.

Puzianowska‐Kuźnicka M, Owczarz M, Wieczorowska‐Tobis K, et al. Interleukin‐6 and C‐reactive protein, successful aging, and mortality: the PolSenior study. Immun Ageing. 2016;13:21.

Abb J, Abb H, Deinhardt F. Age‐related decline of human interferon alpha and interferon gamma production. Blut. 1984;48(5):285‐289.

Zhao H, Zhang H, Qin X. Age‐related differences in serum MFG‐E8, TGF‐β1 and correlation to the severity of atherosclerosis determined by ultrasound. Mol Med Rep. 2017;16(6):9741‐9748.

Seidler S, Zimmermann HW, Bartneck M, Trautwein C, Tacke F. Age‐dependent alterations of monocyte subsets and monocyte‐related chemokine pathways in healthy adults. BMC Immunol. 2010;11:30.

Liberale L, Badimon L, Montecucco F, Lüscher TF, Libby P, Camici GG. Inflammation, aging, and cardiovascular disease. JACC. 2022;79(8):837‐847.

Yusipov I, Kondakova E, Kalyakulina A, et al. Accelerated epigenetic aging and inflammatory/immunological profile (ipAGE) in patients with chronic kidney disease. GeroScience. 2022;44(2):817‐834.

Sayed N, Huang Y, Nguyen K, et al. An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging. Nat Aging. 2021;1:598‐615.

Shamir L, Long J. Quantitative machine learning analysis of brain MRI morphology throughout aging. Curr Aging Sci. 2016;9(4):310‐317.

Lin L, Jin C, Fu Z, Zhang B, Bin G, Wu S. Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks. Comput Methods Programs Biomed. 2016;125:8‐17.

Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ. A voxel‐based morphometric study of ageing in 465 normal adult human brains. Neuroimage. 2001;14(1 Pt 1):21‐36.

Pfefferbaum A, Mathalon DH, Sullivan EV, Rawles JM, Zipursky RB, Lim KO. A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood. Arch Neurol. 1994;51(9):874‐887.

Franke K, Ziegler G, Klöppel S, Gaser C. Estimating the age of healthy subjects from T1‐weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage. 2010;50(3):883‐892.

Cole JH, Leech R, Sharp DJ. Prediction of brain age suggests accelerated atrophy after traumatic brain injury. Ann Neurol. 2015;77(4):571‐581.

Schnack HG, van Haren NEM, Nieuwenhuis M, Hulshoff Pol HE, Cahn W, Kahn RS. Accelerated brain aging in Schizophrenia: a longitudinal pattern recognition study. Am J Psychiatry. 2016;173(6):607‐616.

He S, Pereira D, David Perez J, et al. Multi‐channel attention‐fusion neural network for brain age estimation: accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan. Med Image Anal. 2021;72: [eLocator: 102091].

Kuo CY, Tai TM, Lee PL, et al. Improving individual brain age prediction using an ensemble deep learning framework. Front Psychiatry. 2021;12: [eLocator: 626677].

Beheshti I, Nugent S, Potvin O, Duchesne S. Bias‐adjustment in neuroimaging‐based brain age frameworks: a robust scheme. Neuroimage Clin. 2019;24: [eLocator: 102063].

Chung Y, Addington J, Bearden CE, et al. Use of machine learning to determine deviance in neuroanatomical maturity associated with future psychosis in youths at clinically high risk. JAMA Psychiatry. 2018;75(9):960‐968.

Xifra‐Porxas A, Ghosh A, Mitsis GD, Boudrias MH. Estimating brain age from structural MRI and MEG data: insights from dimensionality reduction techniques. Neuroimage. 2021;231: [eLocator: 117822].

Rokicki J, Wolfers T, Nordhøy W, et al. Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders. Hum Brain Mapp. 2021;42(6):1714‐1726.

Lewis JD, Evans AC, Tohka J. T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance. Neuroimage. 2018;173:341‐350.

Erus G, Battapady H, Satterthwaite TD, et al. Imaging patterns of brain development and their relationship to cognition. Cerebral Cortex. 2015;25(6):1676‐1684.

Luna A, Bernanke J, Kim K, et al. Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth. Hum Brain Mapp. 2021;42(14):4568‐4579.

Ly M, Yu GZ, Karim HT, et al. Improving brain age prediction models: incorporation of amyloid status in Alzheimer's disease. Neurobiol Aging. 2020;87:44‐48.

Drobinin V, Van Gestel H, Helmick CA, Schmidt MH, Bowen CV, Uher R. The developmental brain age is associated with adversity, depression, and functional outcomes among adolescents. Biol Psychiatry Cogn Neurosci Neuroimaging. 2022;7(4):406‐414.

Han LKM, Schnack HG, Brouwer RM, et al. Contributing factors to advanced brain aging in depression and anxiety disorders. Transl Psychiatry. 2021;11(1):402.

Dunlop K, Victoria LW, Downar J, Gunning FM, Liston C. Accelerated brain aging predicts impulsivity and symptom severity in depression. Neuropsychopharmacology. 2021;46(5):911‐919.

Shahab S, Mulsant BH, Levesque ML, et al. Brain structure, cognition, and brain age in schizophrenia, bipolar disorder, and healthy controls. Neuropsychopharmacology. 2019;44(5):898‐906.

Hwang G, Hermann B, Nair VA, et al. Brain aging in temporal lobe epilepsy: chronological, structural, and functional. Neuroimage Clin. 2020;25: [eLocator: 102183].

Cole JH. Multimodality neuroimaging brain‐age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors. Neurobiol Aging. 2020;92:34‐42.

Elliott ML, Belsky DW, Knodt AR, et al. Brain‐age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort. Mol Psychiatry. 2021;26(8):3829‐3838.

Flammer J, Konieczka K, Bruno RM, Virdis A, Flammer AJ, Taddei S. The eye and the heart. Eur Heart J. 2013;34(17):1270‐1278.

Hoon M, Okawa H, Della Santina L, Wong ROL. Functional architecture of the retina: development and disease. Prog Retinal Eye Res. 2014;42:44‐84.

Nusinovici S, Rim TH, Yu M, et al. Retinal photograph‐based deep learning predicts biological age, and stratifies morbidity and mortality risk. Age Ageing. 2022;51:4.

Koronyo Y, Biggs D, Barron E, et al. Retinal amyloid pathology and proof‐of‐concept imaging trial in Alzheimer's disease. JCI Insight. 2017;2(16): [eLocator: e93621].

Patton N, Aslam T, Macgillivray T, Pattie A, Deary IJ, Dhillon B. Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures. J Anat. 2005;206(4):319‐348.

Liu C, Wang W, Li Z, et al. Biological age estimated from retinal imaging: a novel biomarker of aging. In: Shen D, Liu T, Peters TM, eds. Medical Image Computing and Computer Assisted Intervention—MICCAI 2019; 2019 2019//. Springer International Publishing; 2019:138‐146.

Zhu Z, Shi D, Guankai P, et al. Retinal age gap as a predictive biomarker for mortality risk. Br J Ophthalmol. 2023;107(4):547‐554.

Shigueoka LS, Mariottoni EB, Thompson AC, Jammal AA, Costa VP, Medeiros FA. Predicting age from optical coherence tomography scans with deep learning. Transl Vis Sci Technol. 2021;10(1):12.

Chan VTT, Sun Z, Tang S, et al. Spectral‐domain OCT measurements in Alzheimer's disease. Ophthalmology. 2019;126(4):497‐510.

Hu W, Wang W, Wang Y, et al. Retinal age gap as a predictive biomarker of future risk of Parkinson's disease. Age Ageing. 2022;51(3): [eLocator: afac062].

Zhu Z, Chen Y, Wang W, et al. Association of retinal age gap with arterial stiffness and incident cardiovascular disease. Stroke. 2022;53(11):3320‐3328.

Zhang S, Chen R, Wang Y, et al. Association of retinal age gap and risk of kidney failure: a UK Biobank Study. Am J Kidney Dis. 2023;81(5):537‐544.

Chen W, Qian W, Wu G, et al. Three‐dimensional human facial morphologies as robust aging markers. Cell Res. 2015;25(5):574‐587.

Asakura K, Nishiwaki Y, Milojevic A, et al. Lifestyle factors and visible skin aging in a population of Japanese elders. J Epidemiol. 2009;19(5):251‐259.

Coleman S, Grover R. The anatomy of the aging face: volume loss and changes in 3‐dimensional topography. Aesthet Surg J. 2006;26(1S):S4‐S9.

Zimbler MS, Kokoska MS, Thomas JR. Anatomy and pathophysiology of facial aging. Facial Plast Surg Clin North Am. 2001;9(2):179‐187.

Shaw Jr. RB, Katzel EB, Koltz PF, et al. Aging of the facial skeleton: aesthetic implications and rejuvenation strategies. Plast Reconstr Surg. 2011;127(1):374‐383.

Geng X, Zhou ZH, Smith‐Miles K. Automatic age estimation based on facial aging patterns. IEEE Trans Pattern Anal Mach Intell. 2007;29(12):2234‐2240.

Guodong Guo G, Yun Fu Fu, Dyer CR, Huang TS. Image‐based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans Image Process. 2008;17(7):1178‐1188.

Bobrov E, Georgievskaya A, Kiselev K, et al. PhotoAgeClock: deep learning algorithms for development of non‐invasive visual biomarkers of aging. Aging. 2018;10(11):3249‐3259.

Dykiert D, Bates TC, Gow AJ, Penke L, Starr JM, Deary IJ. Predicting mortality from human faces. Psychosom Med. 2012;74(6):560‐566.

Bachman S, Sparrow D, Smith LK. Effect of aging on the electrocardiogram. Am J Cardiol. 1981;48(3):513‐516.

Simonson E. The effect of age on the electrocardiogram. Am J Cardiol. 1972;29(1):64‐73.

Dai X, Busby‐Whitehead J, Forman DE, Alexander KP. Stable ischemic heart disease in the older adults. J Geriatr Cardiol. 2016;13(2):109‐114.

Molander U, Kumar Dey D, Sundh V, Steen B. ECG abnormalities in the elderly: prevalence, time and generation trends and association with mortality. Aging Clin Exp Res. 2003;15(6):488‐493.

Starc V, Leban MA, Šinigoj P, et al. Can functional cardiac age be predicted from the ECG in a normal healthy population? 2012 Computing in Cardiology, 9‐12 September 2012;2012:101–104.

Ball R, Feiveson A, Schlegel T, Starc V, Dabney A. Predicting “heart age” using electrocardiography. J Pers Med. 2014;4(1):65‐78.

Attia ZI, Friedman PA, Noseworthy PA, et al. Age and sex estimation using artificial intelligence from standard 12‐Lead ECGs. Circul Arrhythm Electrophysiol. 2019;12(9): [eLocator: e007284].

Lima EM, Ribeiro AH, Paixão GMM, et al. Deep neural network‐estimated electrocardiographic age as a mortality predictor. Nat Commun. 2021;12(1):5117.

Le Goallec A, Diai S, Collin S, Prost JB, Vincent T, Patel CJ. Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images. Nat Commun. 2022;13(1):1979.

Raghu VK, Weiss J, Hoffmann U, Aerts HJWL, Lu MT. Deep learning to estimate biological age from chest radiographs. JACC Cardiovasc Imaging. 2021;14(11):2226‐2236.

Asif MK, Nambiar P, Ibrahim N, Al‐Amery SM, Khan IM. Three‐dimensional image analysis of developing mandibular third molars apices for age estimation: a study using CBCT data enhanced with Mimics & 3‐Matics software. Leg Med (Tokyo). 2019;39:9‐14.

Stern D, Payer C, Giuliani N, Urschler M. Automatic age estimation and majority age classification from multi‐factorial MRI data. IEEE J Biomed Health Inform. 2019;23(4):1392‐1403.

D'Ortenzio L, Prowse T, Inskip M, Kahlon B, Brickley M. Age estimation in older adults: use of pulp/tooth ratios calculated from tooth sections. Am J Phys Anthropol. 2018;165(3):594‐603.

Martínez Vera NP, Höller J, Widek T, Neumayer B, Ehammer T, Urschler M. Forensic age estimation by morphometric analysis of the manubrium from 3D MR images. Forensic Sci Int. 2017;277:21‐29.

Garamendi PM, Landa MI, Ballesteros J, Solano MA. Reliability of the methods applied to assess age minority in living subjects around 18 years old. Forensic Sci Int. 2005;154(1):3‐12.

Melo M, Ata‐Ali J. Accuracy of the estimation of dental age in comparison with chronological age in a Spanish sample of 2641 living subjects using the Demirjian and Nolla methods. Forensic Sci Int. 2017;270:276.e1‐276.e7.

Duangto P, Iamaroon A, Prasitwattanaseree S, Mahakkanukrauh P, Janhom A. New models for age estimation and assessment of their accuracy using developing mandibular third molar teeth in a Thai population. Int J Legal Med. 2017;131(2):559‐568.

Tomás LF, Mónico LS, Tomás I, Varela‐Patiño P, Martin‐Biedma B. The accuracy of estimating chronological age from Demirjian and Nolla methods in a Portuguese and Spanish sample. BMC Oral Health. 2014;14:160.

Morris JF, Temple W. Spirometric “lung age” estimation for motivating smoking cessation. Prev Med. 1985;14(5):655‐662.

Ivey MA, Johns DP, Stevenson C, et al. Assessing the performance of two lung age equations on The Australian population: using data from the cross‐sectional BOLD‐Australia study. Nicotine Tob Res. 2014;16(12):1629‐1637.

Newbury W, Newbury J, Briggs N, Crockett A. Exploring the need to update lung age equations. Prim Care Respir J. 2010;19(3):242‐247.

Yamaguchi K, Omori H, Onoue A, et al. Novel regression equations predicting lung age from varied spirometric parameters. Respir Physiol Neurobiol. 2012;183(2):108‐114.

Mitsumune T, Senoh E, Nishikawa H, Adachi M, Kajii E. The effect of obesity and smoking status on lung age in Japanese men. Respirology. 2009;14(5):757‐760.

Hankinson JL, Kinsley KB, Wagner GR. Comparison of spirometric reference values for Caucasian and African American blue‐collar workers. J Occup Environ Med. 1996;38(2):137‐143.

Melo SMD, Melo VA, Melo EV, Menezes Filho RS, Castro VL, Barreto MSP. Envelhecimento pulmonar acelerado em pacientes com obesidade mórbida. Jornal Brasileiro de Pneumologia. 2010;36(6):746‐752.

Harada CN, Natelson Love MC, Triebel KL. Normal cognitive aging. Clin Geriatr Med. 2013;29(4):737‐752.

Gale SA, Acar D, Daffner KR. Dementia. Am J Med. 2018;131(10):1161‐1169.

Petersen RC, Caracciolo B, Brayne C, Gauthier S, Jelic V, Fratiglioni L. Mild cognitive impairment: a concept in evolution. J Intern Med. 2014;275(3):214‐228.

Kambeitz‐Ilankovic L, Haas SS, Meisenzahl E, et al. Neurocognitive and neuroanatomical maturation in the clinical high‐risk states for psychosis: a pattern recognition study. Neuroimage Clin. 2019;21: [eLocator: 101624].

Boyle PA, Wang T, Yu L, et al. The “cognitive clock”: a novel indicator of brain health. Alzheimer's Dementia. 2021;17(12):1923‐1937.

D'Agostino Sr. RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743‐753.

Anderson KM, Odell PM, Wilson PWF, Kannel WB. Cardiovascular disease risk profiles. Am Heart J. 1991;121(1 Pt 2):293‐298.

Cuende JI, Cuende N, Calaveras‐Lagartos J. How to calculate vascular age with the SCORE project scales: a new method of cardiovascular risk evaluation. Eur Heart J. 2010;31(19):2351‐2358.

Junyent M, Zambón D, Gilabert R, Núñez I, Cofán M, Ros E. Carotid atherosclerosis and vascular age in the assessment of coronary heart disease risk beyond the Framingham Risk Score. Atherosclerosis. 2008;196(2):803‐809.

Gepner AD, Keevil JG, Wyman RA, et al. Use of carotid intima‐media thickness and vascular age to modify cardiovascular risk prediction. J Am Soc Echocardiogr. 2006;19(9):1170‐1174.

Villines TC, Taylor AJ. Multi‐ethnic study of atherosclerosis arterial age versus Framingham 10‐year or lifetime cardiovascular risk. Am J Cardiol. 2012;110(11):1627‐1630.

Guaraldi G, Zona S, Alexopoulos N, et al. Coronary aging in HIV‐infected patients. Clin Infect Dis. 2009;49(11):1756‐1762.

Romanens M, Ackermann F, Sudano I, Szucs T, Spence J. Arterial age as a substitute for chronological age in the AGLA risk function could improve coronary risk prediction. Swiss Med Wkly. 2014;144: [eLocator: w13967].

McClelland RL, Nasir K, Budoff M, Blumenthal RS, Kronmal RA. Arterial age as a function of coronary artery calcium (from the Multi‐Ethnic Study of Atherosclerosis [MESA]). Am J Cardiol. 2009;103(1):59‐63.

Shaw LJ, Raggi P, Berman DS, Callister TQ. Coronary artery calcium as a measure of biologic age. Atherosclerosis. 2006;188(1):112‐119.

Mitina M, Young S, Zhavoronkov A. Psychological aging, depression, and well‐being. Aging. 2020;12(18):18765‐18777.

Zannas AS, Arloth J, Carrillo‐Roa T, et al. Lifetime stress accelerates epigenetic aging in an urban, African American cohort: relevance of glucocorticoid signaling. Genome Biol. 2015;16:266.

Thyagarajan B, Shippee N, Parsons H, et al. How does subjective age get “under the skin”? The association between biomarkers and feeling older or younger than one's age: the health and retirement study. Innov Aging. 2019;3(4):igz035.

Stephan Y, Sutin AR, Terracciano A. Subjective age and cystatin C among older adults. J Gerontol Series B. 2019;74(3):382‐388.

Stephan Y, Sutin AR, Terracciano A. Subjective age and adiposity: evidence from five samples. Int J Obes. 2019;43(4):938‐941.

Carstensen LL. The influence of a sense of time on human development. Science. 2006;312(5782):1913‐1915.

Hughes ML, Touron DR. Aging in context: incorporating everyday experiences into the study of subjective age. Front Psychiatry. 2021;12: [eLocator: 633234].

Levy B. Stereotype embodiment: a psychosocial approach to aging. Curr Dir Psychol Sci. 2009;18(6):332‐336.

Zhavoronkov A, Kochetov K, Diamandis P, Mitina M. PsychoAge and SubjAge: development of deep markers of psychological and subjective age using artificial intelligence. Aging. 2020;12(23):23548‐23577.

Stephan Y, Sutin AR, Terracciano A. Subjective age and mortality in three longitudinal samples. Psychosom Med. 2018;80(7):659‐664.

Uotinen V, Rantanen T, Suutama T. Perceived age as a predictor of old age mortality: a 13‐year prospective study. Age Ageing. 2005;34(4):368‐372.

Li Y, Liu M, Miyawaki CE, et al. Bidirectional relationship between subjective age and frailty: a prospective cohort study. BMC Geriatr. 2021;21(1):395.

Rippon I, Steptoe A. Is the relationship between subjective age, depressive symptoms and activities of daily living bidirectional? Soc Sci Med. 2018;214:41‐48.

Mock SE, Eibach RP. Aging attitudes moderate the effect of subjective age on psychological well‐being: evidence from a 10‐year longitudinal study. Psychol Aging. 2011;26(4):979‐986.

Elliott ML, Caspi A, Houts RM, et al. Disparities in the pace of biological aging among midlife adults of the same chronological age have implications for future frailty risk and policy. Nat Aging. 2021;1(3):295‐308.

Belsky DW, Caspi A, Arseneault L, et al. Quantification of the pace of biological aging in humans through a blood test, the DunedinPoAm DNA methylation algorithm. eLife. 2020;9: [eLocator: e54870].

Belsky DW, Caspi A, Houts R, et al. Quantification of biological aging in young adults. Proc Natl Acad Sci. 2015;112(30):E4104‐E4110.

Sathyan S, Ayers E, Gao T, Milman S, Barzilai N, Verghese J. Plasma proteomic profile of frailty. Aging Cell. 2020;19(9): [eLocator: e13193].

Ward MAR, Alenazi A, Delisle M, Logsetty S. The impact of frailty on acute care general surgery patients: a systematic review. J Trauma Acute Care Surg. 2019;86(1):148‐154.

Guler SA, Kwan JM, Winstone TA, et al. Severity and features of frailty in systemic sclerosis‐associated interstitial lung disease. Respir Med. 2017;129:1‐7.

Kim S, Myers L, Wyckoff J, Cherry KE, Jazwinski SM. The frailty index outperforms DNA methylation age and its derivatives as an indicator of biological age. GeroScience. 2017;39(1):83‐92.

Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146‐M157.

Syddall H, Cooper C, Martin F, Briggs R, Aihie Sayer A. Is grip strength a useful single marker of frailty? Age Ageing. 2003;32(6):650‐656.

Woo J, Goggins W, Sham A, Ho SC. Public health significance of the frailty index. Disabil Rehabil. 2006;28(8):515‐521.

Li G, Ioannidis G, Pickard L, et al. Frailty index of deficit accumulation and falls: data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) Hamilton cohort. BMC Musculoskelet Disord. 2014;15:185.

Marengoni A, Zucchelli A, Vetrano DL, et al. Beyond chronological age: frailty and multimorbidity predict in‐hospital mortality in patients with coronavirus disease 2019. J Gerontol Series A. 2021;76(3):e38‐e45.

Li X, Ploner A, Wang Y, et al. Longitudinal trajectories, correlations and mortality associations of nine biological ages across 20‐years follow‐up. eLife. 2020;9: [eLocator: e51507].

Alonso Salinas GL, Sanmartin M, Pascual Izco M, et al. The role of frailty in acute coronary syndromes in the elderly. Gerontology. 2018;64(5):422‐429.

Joseph B, Zangbar B, Pandit V, et al. Emergency general surgery in the elderly: too old or too frail? J Am Coll Surg. 2016;222(5):805‐813.

Demidenko O, Barardo D, Budovskii V, et al. Rejuvant®, a potential life‐extending compound formulation with alpha‐ketoglutarate and vitamins, conferred an average 8‐year reduction in biological aging, after an average of 7 months of use, in the TruAge DNA methylation test. Aging. 2021;13(22):24485‐24499.

Fiorito G, Caini S, Palli D, et al. DNA methylation‐based biomarkers of aging were slowed down in a two‐year diet and physical activity intervention trial: the DAMA study. Aging Cell. 2021;20(10): [eLocator: e13439].

Sanford JA, Nogiec CD, Lindholm ME, et al. Molecular transducers of physical activity consortium (MoTrPAC): mapping the dynamic responses to exercise. Cell. 2020;181(7):1464‐1474.

Duggal NA, Niemiro G, Harridge SDR, Simpson RJ, Lord JM. Can physical activity ameliorate immunosenescence and thereby reduce age‐related multi‐morbidity? Nat Rev Immunol. 2019;19(9):563‐572.

Duggal NA, Pollock RD, Lazarus NR, Harridge S, Lord JM. Major features of immunesenescence, including reduced thymic output, are ameliorated by high levels of physical activity in adulthood. Aging Cell. 2018;17(2): [eLocator: e12750].

Diabetes Prevention Program Research G. Long‐term effects of lifestyle intervention or metformin on diabetes development and microvascular complications over 15‐year follow‐up: the diabetes prevention program outcomes study. Lancet Diabetes Endocrinol. 2015;3(11):866‐875.

Weyh C, Krüger K, Strasser B. Physical activity and diet shape the immune system during aging. Nutrients. 2020;12(3):622.

Benjamin H. Biologic versus chronologic age. J Gerontol. 1947;2(3):217‐227.

Espinoza SE, Musi N, Wang C, et al. Rationale and study design of a randomized clinical trial of metformin to prevent frailty in older adults with prediabetes. J Gerontol Series A. 2020;75(1):102‐109.

Barzilai N, Crandall JP, Kritchevsky SB, Espeland MA. Metformin as a tool to target aging. Cell Metab. 2016;23(6):1060‐1065.

Ettehad D, Emdin CA, Kiran A, et al. Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta‐analysis. The Lancet. 2016;387(10022):957‐967.

Vaiserman A, Lushchak O. Implementation of longevity‐promoting supplements and medications in public health practice: achievements, challenges and future perspectives. J Transl Med. 2017;15(1):160.

Johnson AA, Shokhirev MN, Lehallier B. The protein inputs of an ultra‐predictive aging clock represent viable anti‐aging drug targets. Ageing Res Rev. 2021;70: [eLocator: 101404].

Brunet A, Goodell MA, Rando TA. Ageing and rejuvenation of tissue stem cells and their niches. Nat Rev Mol Cell Biol. 2023;24(1):45‐62.

McGinley LM, Kashlan ON, Bruno ES, et al. Human neural stem cell transplantation improves cognition in a murine model of Alzheimer's disease. Sci Rep. 2018;8(1): [eLocator: 14776].

Kim JA, Ha S, Shin KY, et al. Neural stem cell transplantation at critical period improves learning and memory through restoring synaptic impairment in Alzheimer's disease mouse model. Cell Death Dis. 2015;6(6): [eLocator: e1789].

Katsimpardi L, Litterman NK, Schein PA, et al. Vascular and neurogenic rejuvenation of the aging mouse brain by young systemic factors. Science. 2014;344(6184):630‐634.

Villeda SA, Plambeck KE, Middeldorp J, et al. Young blood reverses age‐related impairments in cognitive function and synaptic plasticity in mice. Nat Med. 2014;20(6):659‐663.

Xia X, Wang Y, Yu Z, Chen J, Han JDJ. Assessing the rate of aging to monitor aging itself. Ageing Res Rev. 2021;69: [eLocator: 101350].

Brinkley TE, Justice JN, Basu S, et al. Research priorities for measuring biologic age: summary and future directions from the Research Centers Collaborative Network Workshop. GeroScience. 2022;44(6):2573‐2583.

Huang CC, Chou KH, Lee WJ, et al. Brain white matter hyperintensities‐predicted age reflects neurovascular health in middle‐to‐old aged subjects. Age Ageing. 2022;51(5): [eLocator: afac106].

Barry LE, O'Neill S, Heaney LG, O'Neill C. Stress‐related health depreciation: using allostatic load to predict self‐rated health. Soc Sci Med. 2021;283: [eLocator: 114170].

Lima‐Costa MF, Cesar CC, Chor D, Proietti FA. Self‐rated health compared with objectively measured health status as a tool for mortality risk screening in older adults: 10‐year follow‐up of the Bambui Cohort Study of aging. Am J Epidemiol. 2012;175(3):228‐235.

Galkin F, Mamoshina P, Aliper A, de Magalhães JP, Gladyshev VN, Zhavoronkov A. Biohorology and biomarkers of aging: current state‐of‐the‐art, challenges and opportunities. Ageing Res Rev. 2020;60: [eLocator: 101050].

The Lancet Respiratory Medicine. Opening the black box of machine learning. Lancet Respir Med. 2018;6(11):801.

Clarke R, Shipley M, Lewington S, et al. Underestimation of risk associations due to regression dilution in long‐term follow‐up of prospective studies. Am J Epidemiol. 1999;150(4):341‐353.

Bashyam VM, Erus G, Doshi J, et al. MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14,468 individuals worldwide. Brain. 2020;143(7):2312‐2324.

Beck D, Lange AMG, Pedersen ML, et al. Cardiometabolic risk factors associated with brain age and accelerate brain ageing. Hum Brain Mapp. 2022;43(2):700‐720.

Osawa Y, Tian Q, An Y, Studenski SA, Resnick SM, Ferrucci L. Longitudinal associations between brain volume and knee extension peak torque. J Gerontol Series A. 2021;76(2):286‐290.

Sebastiani P, Thyagarajan B, Sun F, et al. Biomarker signatures of aging. Aging Cell. 2017;16(2):329‐338.

Lu Y, Brommer B, Tian X, et al. Reprogramming to recover youthful epigenetic information and restore vision. Nature. 2020;588(7836):124‐129.

Matsuyama M, WuWong DJ, Horvath S, Matsuyama S. Epigenetic clock analysis of human fibroblasts in vitro: effects of hypoxia, donor age, and expression of hTERT and SV40 largeT. Aging. 2019;11(10):3012‐3022.

Petkovich DA, Podolskiy DI, Lobanov AV, Lee SG, Miller RA, Gladyshev VN. Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metab. 2017;25(4):954‐960.

Wang T, Tsui B, Kreisberg JF, et al. Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction and rapamycin treatment. Genome Biol. 2017;18(1):57.

Polanowski AM, Robbins J, Chandler D, Jarman SN. Epigenetic estimation of age in humpback whales. Mol Ecol Resour. 2014;14(5):976‐987.

Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA. 2017;318(19):1925‐1926.

Bao H, Cao J, Chen M, et al. Biomarkers of aging. Sci China Life Sci. 2023;66(5):893‐1066.

Ahadi S, Zhou W, Schüssler‐Fiorenza rose SM, et al. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat Med. 2020;26(1):83‐90.

Zhu J, Gao L, Song J, et al. Label‐guided generative adversarial network for realistic image synthesis. IEEE Trans Pattern Anal Mach Intell. 2023;45(3):3311‐3328.

Yi X, Walia E, Babyn P. Generative adversarial network in medical imaging: a review. Med Image Anal. 2019;58: [eLocator: 101552].