ARTÍCULO DE REVISIÓN

Measurement of biological age with biomarkers: A scoping review

Medición de la edad biológica con biomarcadores: una revisión de alcance

Diego Alejandro Espíndola-Fernández1, Ana María Posada-Cano2, Dagnóvar Aristizábal-Ocampo2, Jaime Gallo-Villegas1,2

1. School of Medicine, University of Antioquia

   
   

2. SICOR Clinical and Research Center for Comprehensive Solutions in Cardiovascular Risk

Recibido:

1. de marzo de 2024

   Aceptado:

2. de marzo de 2024

Correspondencia:

diego.espindola@udea.edu.co

DOI: 10.56050/01205498.2339

Abstract

Introduction: Studies related to the mechanisms of senescence have allowed the identification and quantification of biomarkers of biological aging. However, the available information is scattered, and there is no uniform definition of the concept. We aimed to overview the current approaches to measuring biological age using biomarkers and summarize the evidence. Méthods: We use the five-phase methodology developed by Arksey and O’Malley. A comprehensive search was performed in five databases up to March 27, 2023. Original articles that described methods for measuring biological age using ≥2 biomarkers in individuals aged ≥18 years were included, with no restrictions on language or publication date. Results: We included 90 articles from 15 countries that were published as early as 1974. Most were observational, utilizing biomarkers ranging from 3 to 900. Eleven methods for calculating composite biological age were described: principal component analysis, Klemera-Doubal, and multiple linear regression analysis being the most used. The outcomes focused on specific risk factors, comorbidities, physical or cognitive functionality, and mortality. The prognostic validity of biological age was as- sessed by examining its association with mortality risk, highlighting its superior predictive power compared to chronological age or traditional age-related disease biomarkers. Conclusion: We conclude that scientific literature exhibits considerable heterogeneity in number and type of biomarkers employed, as well as the methods used to estimate biological age. Currently, there is no universally accepted standard for assessing biological age, and there is lack of research examining its reproducibility and prognostic validity. 

Keywords: Biological age; Chronic noncommunicable diseases; Aging; Biomarker; Systematic review

Resumen

Introducción: Los estudios relacionados con los mecanismos del envejecimiento han permitido la identificación y cuantificación de biomarcadores del envejecimiento biológico. Sin embargo, la información disponible está dispersa y no existe una definición uniforme del concepto. Nuestro objetivo fue revisar los enfoques actuales para medir la edad biológica utilizando biomarcadores y resumir la evidencia. Métodos: Utilizamos la metodología de cinco fases desarrollada por Arksey y O’Malley. Se realizó una búsqueda exhaustiva en cinco bases de datos hasta el 27 de marzo de 2023. Se incluyeron artículos originales que describían métodos para medir la edad biológica utilizando ≥2 biomarcadores en individuos mayores de ≥18 años, sin restricciones de idioma o fecha de publicación. Resultados: Se incluyeron 90 artículos de 15 países, publicándose el primero en 1974. La mayoría fueron observacionales, utilizando desde 3 hasta 900 biomarcadores. Se describieron once métodos para calcular la edad biológica compuesta: el análisis de componentes principales, Klemera-Doubal y el análisis de regresión lineal múltiple, fueron los más utilizados. Los resultados se centraron en factores de riesgo específicos, comorbilidades, funcionalidad física o cognitiva y mortalidad. La validez pronóstica de la edad biológica se evaluó examinando su asociación con el riesgo de mortalidad, destacando su poder predictivo superior en comparación con la edad cronológica o los biomarcadores de enfermedades tradicionales relacionadas con la edad. Conclusión: Concluimos que la literatura científica exhibe una considerable heterogeneidad en el número y tipo de biomarcadores empleados, así como en los métodos utilizados para estimar la edad biológica. Actualmente, no existe un estándar universalmente aceptado para evaluar la edad biológica, y falta investigación que examine su reproducibilidad y validez pronóstica.

Palabras clave: Edad biológica; Enfermedades crónicas; Longevidad; Biomarcadores; Revisión sistemática, envejecimiento.

Introduction

Aging is a multidimensional phenomenon, and chronological age is just one of its relevant dimensions (1). Conventional measures of aging tend to overlook other important aspects, such as health and physiological function (2). With the improvement of health services, medical advancements, and technological progress, the population pyramid is being inverted, with adults over 65 years of age now comprising 10% of the global population (3). This shift can be attributed to increased life expectancy, which has consequently resulted in a higher prevalence of frailty, disability, and chronic noncommunicable diseases associated with age-related physiological decline (4). Consequently, there has been a growing need for research focused on understanding and quantifying the ageing process (5).

Studies exploring the mechanisms of ageing have identified and measured early markers of biological decay, offering valuable insights for the development of preventive and therapeutic strategies (6). These markers encompass various indicators of advancing age, including: i) external physical manifestations of aging (7); ii) morphological and physiological modifications of different organs (8); iii) neuropsychological changes (9); iv) physical work capacity (10); and v) biochemical and clinical markers (11). It is worth noting that there are additional measurements, such as frailty indices, deficit accumulation scores, multimorbidity scores, multifactorial risk indices, and prognostic indices, which are beyond the scope of this work (11). These biomarkers serve as composite measures of the senescence process, which often occurs asynchronously with chronological age.

In the scientific literature, different methods of quantifying biological age have been described. These methods range from composite estimates that include multiple biomarkers pointing at measurable and quantifiable biological parameters, serving as indices for assessing health and physiology (12). However, the information on this topic is dispersed throughout the literature (13), lacking a unified definition of biological age (14) and a consensus on the optimal biomarkers to be included (15). Moreover, methodologies for assessing biological age have evolved over time (16), yet there is a surprising lack of validation studies supporting their use (17). Therefore, the objective of this scoping review is to provide a comprehensive characterization of the measurement of biological age using multiple biomarkers in the general population, including the methods employed, the selection of biomarkers, the types of research designs utilized, and the validation of the estimates. By identifying gaps in the existing literature, this review also offers suggestions for future research priorities in this field.

Methods

Study design

This scoping review was conducted following the five-phase methodology developed by Arksey and O’Malley (18). Unlike a systematic review, a scoping review is employed to explore relatively new or emerging concepts, aiming to identify the breadth of evidence, key concepts, and core factors related to the topic, while also highlighting knowledge gaps and suggesting future research directions (19). The findings of this study are presented in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, specifically the extension for scoping reviews (PRISMA-ScR), which includes a checklist and explanation (20). It should be noted that this systematic review did not have a pre-registered protocol.

Eligibility criteria

The research question for this study was formulated using the Population, Intervention, Comparison, and Outcome (PICO) framework, aiming to identify composite biomarker predictors of biological age in the general population. Thus, the research question of this study was: “What is the existing knowledge on the measurement of biological age using multiple biomarkers in the general population?” This scoping review included observational studies, clinical trials, sys- tematic reviews, and meta-analyses involving individuals aged 18 years and older, without restrictions on previous medical history or associated conditions. However, studies specifically focused on a particular medical condition were excluded.

Information sources

A systematic three-step search strategy was implemented to identify relevant literature published up to March 27, 2023. Firstly, the PubMed database was searched using key terms and MeSH terms such as “biological age,” “biological aging,” “measure,” and “biomarker.” Secondly, five databases including PubMed, CINAHL, Embase, SCOPUS, and Cochrane Central were searched for relevant published articles. There were no restrictions on publication date or language. The search terms used in this step included “biological age,” “biological aging,” “measure,” “quantification,” “evaluation,” “assessment,” “estimation,” and “biomarker.”

Search

For each database, specific search parameters were applied, including keywords and specifiers for time, language, and types of study. The search strategies used for each database are as follows:

PubMed: ((biological age[Title/Abstract] OR biological aging[Title/Abstract]) AND ((measure[Title/Abstract] OR quantification[Title/ Abstract] OR evaluation[Title/Abstract] OR as- sessment[Title/Abstract]) OR (biomarker[Title/ Abstract]))).

CINAHL: ((AB(biological age OR biological aging)) AND (AB((measure OR quantification OR evaluation OR assessment OR estimation) OR (biomarker)))).

Embase: (‘biological age’:ab,ti OR ‘biological aging’:ab,ti) AND (measure:ab,ti OR quantification:ab,ti OR evaluation:ab,ti OR assessment:ab,- ti OR estimation:ab,ti OR biomarker:ab,ti) AND ([systematic review]/lim OR [meta analysis]/lim OR [randomized controlled trial]/lim OR [controlled clinical trial]/lim) AND [1900-2021]/py.

SCOPUS: ((TITLE(biological AND age) OR TITLE (biological AND aging)) AND ((TITLE-ABS (measure) OR TITLE-ABS (quantification) OR TITLE-ABS (evaluation) OR TITLE-ABS (assessment) OR TITLE-ABS (estimation) OR (TITLE-ABS (biomarker))))) AND (LIMIT-TO (DOCTYPE, “ar”)).

Cochrane Library: ((biological age): ti OR (biological aging): ti) AND (((measure): ti, ab, kw OR (quantification): ti, ab, kw OR (evaluation): ti, ab, kw OR (assessment): ti, ab, kw) OR (estimation): ti, ab, kw) OR (biomarker): ti, ab, kw).

Furthermore, additional articles were identified by manually searching the reference lists of the included studies. Please refer to Figure 1 for more details.

Selection of sources of evidence

Studies meeting the following criteria were included in the review: i) described the measurement of biological age using more than two clinical or paraclinical variables; ii) involved subjects older than 18 years, with or without previous medical history; iii) classified as observational studies, clinical trials, systematic reviews, or meta-analyses. Studies were excluded if they: i) described measurements not related to biological age or described a single marker; ii) did not refer to the direct assessment of biological age; iii) referred only to organ measurements instead of general measurements; and iv) were specifically related to the prognosis of a single condition.

Data recording process

To record the data, electronic databases from the Google system were accessed and the search results were reviewed. One researcher (DAEF) filled out the data extraction forms based on the predefined inclusion criteria. The extracted information was then reviewed by a second researcher (AMPC) to identify any inconsistencies. Any discrepancies or disagreements were resolved through discussion and consensus between the two researchers.

Extracted and evaluation of sources of evidence

The following information was collected from each of the included articles: i) article title; ii) Digital Object Identifier (DOI); iii) year of publication; iv) name of the main author; v) language of publication; vi) country and affiliation of the main author; vii) type of methodological design viii); description of data collection; ix) stated main objective; x) mathematical or standardized method used to estimate biological age; xi) number of patients included; xii) general characteristics of the population; xiii) average age of participants; xiv) percentage of women included; xv) average biological age; xvi) for clinical trials, the type of intervention performed and its respective comparison; xvii) validity criteria of the estimation method; xviii) evaluation of reproducibility xix); number and type of biomarkers used to estimate composite biological age; xx) expected outcome; xxi) main findings reported; and xxii) relevant comments raised by the researcher.

Figure 1. A PRISMA flow chart of study selection process in the scoping review.

Summary of results

The studies were categorized based on the search engine and year of publication to extract the relevant information. They were further grouped according to the type of study design and the mathematical or standardized method employed to estimate biological age. Information from each article was then extracted and recorded in the designated database, following the predefined characteristics.

Results

A total of 15 nationalities were represented in the articles found, spanning from 1974 to the present. The first article originated from Finland (n=1; 1%) (21), while others were from Germany (n=4; 5%) (22–25), Australia (n=1; 1%) (26), Canada (n=3; 3%) (27–29), China (n=13; 15%) (30–42), Korea (n=10; 11%) (43–52), United States (n=24; 26%) (53–75), England (n=3; 3%) (76–78), Japan (n=8; 9%) (79–86), Poland (n=1; 1%) (87), Czech Republic (n=2; 2%) (88,89), Russia (n=11; 12%) (88,90–99), Singapore (n=2; 2%) (100,101) Switzerland (n=1; 1%) (102), Ukraine (n=1, 1%) (103), Italy (n=3; 3%) (104–106), Taiwan (n=1; 1%) (107), Denmark (n=1; 1%) (108), Israel (n=1; 1%) (109), and Mexico (n=1; 1%) (110).

Authors with multiple publications included Nakamura, E. (n=6) 59–62,64,65), Belsky, D.W. (n=4) (41,44,45,57), Jee, H. (n=3) (26,29,31), Zhang, W.G. (n=3) (13–15), Pyrkov, T.V. (n=2) (73,74), Bae, C. (n=3) (23,27,32), Liu, Z. (n=2) (17,50), and Graf, G.H. (n=2) (52,55) Most articles were published in English (n=84; 93%) (21,25–59,61–83, 85–89, 93, 94, 96, 98–101, 103–115), while a few were in Russian (n=5; 6%) (90–92, 95, 97), German (n=1; 1%) (116), and Japanese (n=1; 1%) (84). In terms of study type, one nonrandomized clinical trial was found (n=1; 1%) (102), along with 12 prospective cohort observational studies (n=12; 13%) (29, 37, 38, 55, 70, 73, 75, 76, 78, 89, 103, 107), 45 observational retrospective cohort studies (n=46; 50%) (25, 27,  28, 31, 36, 40–45, 49, 51–54, 56–59, 61–69, 71, 72, 74, 77, 80–82, 93, 94, 100, 101, 106, 109–112), and 33 cross-sectional observational studies (n=33; 37%) (21, 26, 32–35, 39, 46–48, 50, 79, 83–88, 90–92, 95–99, 104, 105, 108, 113–116). The most frequently used methods to calculate biological age were principal component analysis (PCA) (n=25; 28%) (26, 28, 34, 35, 39, 44–52, 56, 79–82, 85, 86, 93, 94, 101, 108, 113), the Klemera-Doubal method (KDM) (117) (n=32; 36%) (25, 36, 37, 39, 42, 46, 48, 49, 54–57, 59, 61, 62, 64, 65, 67–69, 72–75, 77, 78, 88, 89, 100, 101, 109, 118), multiple linear regression analysis (MLRA) (n=22; 25%) (21, 23, 24, 30–32, 39, 46–49, 51, 76, 83, 84, 87, 94, 101, 103, 104, 111, 115), the Levine method (119) (n=6, 7%) (53,66–69,77), the formula of Voitenko, V. (120) (n=4; 4%) (90,92,97,99), the BioAge scale (121) (n=4; 4%) (29,92,98,107), the formula of Belozerova, L. (122) (n=2; 2%) (90,96), functional biological age (fBioAge) (123) (n=1; 1%) (63), Hochschild method (115) (n=1; 1%) (48), Podkolzin, A. (124) (n=1; 1%) (95), machine learning (n=2; 2%) (36,40), deep neural network (n=2; 2%) (105,106), and novel methods (n=13; 15%) with specific recommendations without specifying the mathematical procedure performed (31, 33, 38, 43, 51, 58, 66, 75, 87, 94, 101–103, 110, 112).

The highest number of biomarkers applied in an article was 900, including genomic, proteomic, and metabolomic measures (56), while the lowest number was 3, encompassing strength, memory, and vibrotactile sensitivity (103). On average, 28 features were included, with a median of 9 and a mode of 9 biomarkers.

The developed methods incorporated a wide range of biomarkers based on the expected outcome, and most studies demonstrated significant variation in aging estimates. The variables included in these methods encompassed various individual characteristics, such as the presence of gray hair, baldness, and cutaneous elasticity (76), personal history of smoking or chronic noncommunicable diseases (94,100), anthropometric measures such as weight, height, and body mass index (21,33,3 8,42,52,87,90,96,101,109,110,113), vital signs like blood pressure and heart rate, hematological, metabolic, renal, inflammatory, and infectious parameters (23, 25, 27–29, 32, 34–36, 39, 41, 43, 45–47, 49–51, 53–55, 57–59, 61, 62, 64–67, 69, 72–75, 77–85, 88, 90–92, 96, 98, 100, 101, 105–107, 120), molecular tests such as telomere length and methylation (33, 71), neuromuscular capacity including grip strength, gait speed, and monopodal balance (23, 26, 28, 37, 48, 51, 78, 86, 100, 101, 103, 104, 112, 118), musculoskeletal characteristics like muscle mass and bone mineral density, lung capacity assessed through forced expiratory volume, forced vital capacity, and maximum oxygen consumption (26, 38, 39, 42, 46, 47, 49–51, 57–59, 61–63, 65, 73, 74, 78, 80–82, 88, 89, 101, 108, 115, 118), cardiovascular and arterial characteristics such as pulse pressure, intima media thickness, and ankle-brachial index (27, 32–35, 43, 92), basic neuropsychological tests including minimental, verbal memory, and semantic fluency (23, 48, 100, 101, 103, 115, 118), basic neurological sensitivity assessed with visual and auditory acuity (26, 28, 83, 85, 98, 112, 114), and self-reports such as perceived health and perception of vision or hearing (54, 63, 90, 98). These variables collectively contributed to estimating biological age in different studies.

The studies included a considerable number of participants, with an average of 173,467 patients. The minimum number of participants in a study was 15 (104), while the maximum number reached 6,518,532 (43). On average, the percentage of women included in the studies was 45%. The participants consisted of healthy adults, volunteers, or frequent visitors to community health centers where the research was conducted. The reported mean age varied across studies, with an average reported mean age of 48 years. The minimum age reported was 18 (34, 55, 68, 92–94, 113), while the maximum age reached up to the tenth decade of life (67). Participants were often described as healthy adult volunteers or individuals from retrospective and prospective cohorts of longitudinal studies.

The research objectives of the included studies were diverse but primarily focused on examining the association between biological age and measured biomarkers (30, 32, 47, 54, 60, 78, 82, 88, 90), risk factors (40, 43, 51, 81, 100, 101, 107), comorbidities (44, 52, 53, 56, 57, 86, 93, 118), physical functionality (43, 54, 61, 65, 72, 79, 87), and mortality (25, 31, 36–38, 41, 42, 44, 45, 53, 54, 67, 70–72, 74, 76, 93, 94, 109, 110).

In most articles, biological age was not specified as an absolute value or compared with chronological age. Instead, it was analyzed in relation to different outcomes based on the authors’ preferences, although the reasons for their choices were not explicitly stated.

Prognostic validity was assessed in 21 studies that considered mortality as an outcome. These studies found that a higher biological age was associated with an increased risk of mortality, particularly for all-cause mortality (25, 31, 36–38, 41, 42, 44, 45, 53, 54, 67, 70–72, 74, 76, 93, 94, 109, 110).

This association remained significant even after adjusting for sex and chronological age (31). The use of biological age showed a stronger association with mortality compared to chronological age or traditional biomarkers of age-related diseases (25, 31, 36–38, 41, 42, 44, 45, 53, 54, 67, 70–72, 74, 76, 93, 94, 109, 110).

Regarding internal validity, only one study out of the 89 included articles specifically analyzed it (57). This study compared a biological age algorithm using nine versus ten biomarkers and found similar results for risk ratios and model performance (57). Reproducibility analysis was not reported in any of the studies.

Some specific findings from the included studies were as follows: biological age was found to increase with tobacco or alcohol consumption habits (43,107). It also increased with sedentary lifestyle or low fruit intake (100), while greater physical activity (79) or participation in wellness programs were associated with decreased biological age (56). Biological age was related to impaired cognitive ability (53), fragility (101), all-cause morbidity

(25, 37, 38, 52, 74), dental health (111), and the incidence of chronic diseases such as metabolic syndrome, cerebrovascular accidents, cancer, or diabetes mellitus (25, 37, 38, 44, 52, 74). It was also associated with disability (36), emergency room visits, hospitalization (118), and survival (45). Higher biological age was linked to lower self-reports of health status and older physical appearances (58). Metabolites such as amino acids,

fatty acids, acylcarnitine, sphingolipids, and nucleotides were frequently associated with the rate of biological aging (55). Furthermore, 481 genes were identified to be significantly associated with biological age (59). Measures of biological age were found to predict physical, cognitive, and mortality outcomes, although the performance of different methods varied depending on the specific study (54, 101). Among the interventions described, double filtration plasmapheresis was found to reduce biomarkers of aging (30).

It is important to note that these findings are based on the available literature and should be interpreted in the context of each study’s design and methodology.

Discussion

In this scoping review, the focus was on the measurement of biological age using composite biomarkers. A total of 90 relevant original articles were identified, and various methods were employed for estimating biological age, with PCA being the most frequently applied method, followed by KDM and MLRA. The median number of biomarkers to estimate biological age was 9. These biomarkers encompassed a wide range of variables, including personal traits, anthropometric measurements, laboratory tests, neuromuscular, musculoskeletal, neuropsychological, pulmonary, cardiovascular characteristics, as well as tissue, cell, or molecular features (Figure 2).

Figure 2. Range of biomarker employed in composite measures of biological age. Features are grouped in six categories and the list is non exhaustive but illustrative, indicating areas of biological functioning where these biomarkers are indicative of physiological reserve or evidence damage to cellular functions. TNF-α: Tumor Necrosis Factor-α; IL-8: Interleukin 8; mtDNAcn: mitochondrial DNA copy number; hs-CRP: high-sensitivity C-reactive protein; NT-proBNP: N-terminal (NT)-pro hormone BNP; WBC: White blood cells; IFN-γ: Interferon gamma; VO2maximum: maximum oxygen consumption.

The concept of biological age has gained recognition in aging research as a means to capture the heterogeneous and progressive decline in biological functions associated with chronological age (125). It is understood that individuals age at different rates, and biological age has been proposed as a crucial factor in explaining the variations in longevity among people (14). The findings of this scoping review highlight the importance of assessing biological age as part of the analysis of population longevity to predict adverse health outcomes (16). It has been observed that a higher biological age, compared to chronological age or traditional biomarkers of age-related diseases, is associated with an increased risk of mortality (25, 31, 36–38, 41, 42, 44, 45, 53, 54, 67, 70–72, 74, 76, 93, 94, 109, 110). Moreover, a faster rate of biological aging is linked to a worse health prognosis, characterized by greater morbidity and mortality (31, 44, 45, 53, 54, 67, 76, 93, 94).

These findings underscore the significance of considering biological age in understanding the aging process and its implications for health. By incorporating composite biomarkers and employing various predictive methods, researchers can gain insights into individual differences in aging and identify individuals at higher risk for adverse health outcomes (16).

In the management of chronic noncommunicable diseases, the use of biological age can help identify individuals who require greater healthcare interventions or who have a fragile health condition (15). This point is well supported by evidence, as observed in the results of the scoping review (Table 1). However, there are still important questions that need to be addressed regarding the assessment of biological age, including the number of biomarkers needed and the best method for its estimation. In our review, we found that the number of biomarkers considered varied widely, ranging from 3 to 900 in different studies (56,103).

These findings highlight three central questions in this field: i) What is the optimal number of biomarkers required to reliably identify a higher biological age? ii) Which composite biomarkers provide greater accuracy and precision in estimating biological age? iii) Which method yields greater validity in assessing biological age?

Among the studies included in our review, commonly employed biomarkers encompassed hematological, inflammatory, renal, metabolic, pulmonary, and cardiovascular assessments. The number of biomarkers considered varied depending on the method used and the desired outcomes. It appears that different measurements of biological age capture heterogeneous aspects of the aging process, contributing to the heterogeneity of results. Therefore, the performance of a specific calculation method cannot be automatically extrapolated to another method that employs a different set of biomarkers or outcomes.

Blood-related biomarkers frequently exhibit changes with chronological age (23, 27, 28, 32, 34, 35, 43, 45–47, 49–51, 53–55, 57–59, 61, 62, 64–67, 69, 77, 79–85, 88, 90–92, 96, 98, 100, 101, 120). However, it is important to note that these variations are often non-linear over time (126). Consequently, in the studies evaluated, it becomes challenging to separate the biochemical changes associated with age from the results obtained through cross-sectional methods used for estimating biological age.

In summary, while biological age shows promise in identifying individuals with greater healthcare needs and fragility, there is a need for further research to determine the optimal number of biomarkers, identify the most accurate composite biomarkers, and establish the most valid method for assessing biological age. This will contribute to the refinement and standardization of biological age estimation, ultimately enhancing its utility in the management of chronic noncommunicable diseases.

Table 1. Description of the studies included in the scoping review.

C: Cross-sectional; RC: Retrospective cohort; PC: Prospective cohort; CCT: Controlled clinical trial; MLRA: Multiple linear regression analysis; PCA: Principal component analysis; KDM: Kle- mera-Doubal method; LM: Levine method; SEM: Structural equation model.

Recently, Jazwinski and Kim, emphasized the need to incorporate additional elements to improve the interpretation of the model used (127). For instance, including functional measurements that reflect the physiological state at any age could help identify physiological alterations marking the transition from a healthy to a vulnerable condition (128). In longitudinal studies, tracking hematological, renal, inflammatory, metabolic, pulmonary, and cardiovascular biomarkers over time could be beneficial (77). However, reference values or thresholds to determine these transitions have not been proposed yet. The models used to estimate biological age, especially those recently adopted in the aging phenotype to measure the pace of aging, could be considered a good starting point (16). These models incorporate differentbiomarkerswithbiologicalplausibilityand evaluate the occurrence of adverse outcomes over time, which could help define health trajectories, the rate of functional decline, and the transition from a healthy to a vulnerable state.

Mathematical modeling for the estimation of biological age with biomarkers was proposed 50 years ago (21). According to our findings, the most frequently applied methods are PCA, KDM, and MLRA. It is important to highlight that the statistical method used can influence the validity and reproducibility of the estimation of biological age, as each method has its advantages and disadvantages (Table 2) (129). For example, while PCA and MLRA select biomarkers based on their relationship with chronological age, which may produce distortions in the case of extreme values, KDM includes chronological age as an independent variable, overcoming the limitations of the former (117). Nevertheless, in all those methods biological age is modeled as a linear function of chronological age and multiple biomarkers which constrain the functional form of its relation. Structural equation model-based estimators recently proposed transcends the limitations of those methods and allow modeling the biological age as a latent variable expressed in multiple biomarkers and relax assumptions about functional relationship without restrictions on parameters (Table 2) (130). New prospective longitudinal studies are needed to compare current methodologies and determine which produces a satisfactory estimate with potential clinical utility.

Limitations

This review was not previously registered. However, we rigorously followed the methodological design proposed a priori, in accordance with the PRISMA guidelines.

Our search was limited to five databases accessible to the authors. Nonetheless, by cross-referencing the cited literature, we evaluated most publications in the field, except for three inaccessible articles.

In scoping reviews, critical evaluation of research is not a generalized recommendation. However, conducting a critical evaluation could contribute to determining the methodological quality of ongoing research in the field.

Perspectives

Given the heterogeneity and variability of biological age determinants, relying on a single biomarker may not adequately account for its underlying complexity, as observed in our scoping review. Therefore, a panel of carefully selected biomarkers may accelerate progress towards a more reliable determination of biological age. There is a growing trend favoring a group of biomarkers that reflect different interconnected health processes or serve as biological indicators predicting functional capacity at a specific time point, surpassing the effec- tiveness of chronological age. We have identified that composite measures of biological age need to encompass different aspects of the aging process, ranging from developmental programs and DNA replication to tissue and organ function and whole- body physiological capacity. Therefore, as a by- product of the current scoping review and recent analysis of comparative measurement of biological age using various methods (131,132), we propose that future studies should analyze biological age as a composite phenotype (Table 3).

Table 2. Comparison of different methods to estimate biological age

To achieve this, quantitative measurement must encompass key elements, such as defining the composite measure and developing a statistical model that combines the selected biomarkers and physiological measures to estimate biological age. This model can be based on machine learning algorithms or other novel mathematical approaches, such as structural equation model-based estimators (130). These new methods, when applied to biological age estimation, improve empirical performance, and allow for multiple interpretations, serving as indicators of latent physiological deterioration. Additionally, the estimator of biological age should be anchored in the modeling of outcomes of physiological aging, such as morbidity and mortality (130).

Finally, the analysis should include exploring the associations between biological age and relevant outcomes, such as disease risk, mortality, and functional decline. With these provisions, the validity of composite biomarkers for determining biological age will enhance their utility as a clinical tool.

Table 3. A proposed approach to studying biological age as a complex phenotype through six key components.

A combined measure involves taking into account various components that collectively contribute to the quantification of the biological aging process. By breaking it down into distinct components, researchers can focus on specific biomarkers, advanced statistical methods, and physiological systems that are more relevant to biological aging. This approach allows for a more precise and nuanced evaluation of an individual’s aging status, potentially leading to more accurate estimates of biological age and a better understanding of the underlying factors contributing to age-related biological decline.

Conclusion

In recent years, the study of biological age has been a highly active research area due to the increasing longevity of populations worldwide. However, there is significant variability in the number and types of biomarkers used, as well as the methods employed to estimate biological age. Currently, there is no universally accepted gold standard for assessing biological age. Additionally, there is a need for studies to evaluate the reproducibility and prognostic validity of current measurement approaches. Based on our scoping review, we have identified key aspects that should be considered when proposing new research related to the study of biological age as a useful indicator of healthy longevity.

Conflicts of interest

The authors declare no conflicts of interest with the work.

Funding

This research was funded by the Ministry of Science, Technology and Innovation (Minciencias), Colombia. Project 221377758352, contract 763 of 2018.

Acknowledgments

Authors thank the Ministry of Science, Technology, and Innovation from Colombia (Minciencias) and Clinical and Research Center SICOR in Medellin, Colombia.

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