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Individual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics of the global population and the increase in lifestylerelated morbidities, it is interesting to invent a new biological age model to be used for health promotion.
This study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging.
Carefully selected physiological variables from a healthy study population of 100 women and men were used as biomarkers to establish an estimate of biological age. Principal component analysis was applied to the biomarkers and the first principal component was used to define the algorithm estimating biological age.
The first principal component accounted for 31% in women and 25% in men of the total variance in the biological age model combining mean arterial pressure, glycated hemoglobin, waist circumference, forced expiratory volume in 1 second, maximal oxygen consumption, adiponectin, highdensity lipoprotein, total cholesterol, and soluble urokinasetype plasminogen activator receptor. The correlation between the corrected biological age and chronological age was
Estimating biological age from these 9 biomarkers of aging can be used to assess general health compared with the healthy aging trajectory. This may be useful to evaluate health interventions and as an aid to enhance awareness of individual health risks and behavior when deviating from this trajectory.
ClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768
RR210.2196/19209
Biological age (BA) is a measure that quantifies where an individual is on the aging trajectory, assessed by the physiological profile, in comparison with the average person of that given chronological age (CA) within the population from which the equation was generated [
Increasing life expectancy and low fertility rates will have a profound impact on future resources and health care needs [
We included 100 healthy Danish individuals, 51 women and 49 men, between 18 and 65 years of age, to participate in an extensive health examination and the data collection of candidate biomarkers for the BA model. We recruited an equal number of women and men in each 5year age category (
Flow chart of the allocation of enrolled participants in age categories. W: women; M: men.
The study was approved by the Regional Ethics Committee, Denmark (H18031350), recorded as a Clinical Trial (Clinical Trial number: NCT03680768), and performed in accordance with the Helsinki declaration. Participants were informed orally and in writing about the study protocol and the potential risks before obtaining written consent.
On the day of the health examination, participants came to the laboratory following an overnight fast and having avoided exercise activities and alcohol consumption for 24 hours and restrained from smoking for at least 4 hours. Information on the participants’ previous and current health status included weekly alcohol consumption, smoking habits, present medications, past medical history, and selfadministered questionnaires on physical activity level (Physical Activity Scale 2.1) [
Candidate biomarkers measured in the study participants (n=100) showing means with SDs and outcome units per year increase (regression slope with 95% CI).
Biomarkers^{a}  Mean (SD)  Slope (CI)  





Weight (kg)  75.7 (13.1)  0.03 (–0.2 to 0.2)  

Waist circumference (cm)  83.4 (9.8)  0.2 (0.05 to 0.3)  

Hip circumference (cm)  101.4 (7.1)  –0.001 (–0.1 to 0.1)  

Waist/hip ratio  0.8 (0.07)  0.002 (0.001 to 0.003)  

Fat mass (%)  26.8 (8.3)  0.09 (–0.03 to 0.2)  

Muscle mass (kg)  52.8 (10.9)  –0.05 (–0.2 to 0.1)  





Fasting blood glucose (mmol/l)  5.1 (0.4)  0.01 (0.004 to 0.015)  

HbA_{1c}^{b} (mmol/mol)  32.8 (3.2)  0.12 (0.08 to 0.16)  

AGEs^{c} (AU)  1.8 (0.5)  0.027 (0.022 to 0.031)  

Insulin (pmol/l)  44.4 (25.3)  0.05 (–0.32 to 0.42)  

Triglycerides (mmol/l)  0.9 (0.4)  0.002 (–0.004 to 0.008)  

Free fatty acids (μmol/l)  440 (212)  2.36 (–0.72 to 5.46)  

Leptin (pg/ml)  8411 (9472)  –60.0 (–199.8 to 79.9)  

Adiponectin (mg/ml)  11515 (6490)  106.6 (13.4 to 199.8)  

HDL^{d} (mmol/l)  1.5 (0.4)  0.01 (0.006 to 0.017)  

LDL^{e} (mmol/l)  2.8 (0.8)  0.02 (0.01 to 0.03)  

TC^{f} (mmol/l)  4.5 (0.9)  0.03 (0.02 to 0.04)  

TC/HDL ratio  3.1 (0.9)  0.003 (–0.01 to 0.02)  





CRP^{g} (mg/l)  1.6 (3.4)  –0.04 (–0.09 to 0.01)  

suPAR^{h} (ng/ml)  2.09 (0.5)  0.01 (0.003 to 0.017)  





Hemoglobin (mmol/l)  8.7 (0.8)  0.004 (–0.01 to 0.02)  

Hematocrit (%)  41.6 (3.8)  0.03 (–0.03 to 0.09)  





Diastolic BP^{i} (mmHg)  78.0 (10.1)  0.4 (0.3 to 0.5)  

Systolic BP (mmHg)  124.2 (16.7)  0.6 (0.3 to 0.8)  

FEV_{1}^{j} (L)  3.9 (0.9)  –0.02 (–0.04 to –0.01)  

FVC^{k} (L)  4.9 (1.0)  –0.02 (–0.04 to –0.01)  

FEV_{1}/FVC ratio (%)  77.8 (11.6)  –0.13 (–0.20 to –0.05)  





VO_{2max}^{l} (ml/minute/kg)  39.3 (8.11)  –0.18 (–0.28 to –0.06)  

STS^{m} (stands)  23.4 (5.2)  –0.07 (–0.14 to 0.01)  

Handgrip strength (kg)  36.0 (9.4)  –0.8 (–0.2 to 0.1)  

Biceps strength (kg)  35.0 (11.5)  –0.1 (–0.3 to 0.03)  

Quadriceps strength (Nm)  152.4 (51.3)  –0.7 (–1.4 to 0.1) 
^{a}Missing values were present in leptin (n=99), CRP (n=87), hematocrit (n=97), hemoglobin (n=99) and bicep’s strength (n=98).
^{b}HbA_{1c}: glycated hemoglobin type A_{1c}.
^{c}AGE: advanced glycation end product.
^{d}HDL: highdensity lipoprotein.
^{e}LDL: lowdensity lipoprotein.
^{f}TC: total cholesterol.
^{g}CRP: Creactive protein.
^{h}suPAR: soluble urokinase plasminogen activator receptor.
^{i}BP: blood pressure.
^{j}FEV_{1}: forced expiratory volume in 1 second.
^{k}FVC: forced vital capacity.
^{l}VO_{2max}: maximal oxygen consumption.
^{m}STS: 30second sittostand chair rise.
Variables of
To observe the trajectory of normal healthy aging, we excluded participants diagnosed with or having a previous history of T2D, CVD, cancer, and thyroid dysfunction and who were free of the use of medication to lower cholesterol levels, glucose concentration, and BP [
The actual selection between the remaining 31 candidate biomarkers followed a systematic stepwise method in alignment with previous studies [
PCA is a factor analysis that reduces dimensions but preserves most of the information in the original data set. PCA is a linear transformation that applies orthogonal rotation to find factors/principal components that capture the largest amount of information in the data [
To do so, included biomarkers were normalized to a mean of 0 and unit SD, which gives them equal weight in the PCA. The subsequent estimation of BA was performed in 3 steps. First, based on the PCA loading scores, a standardized individual BA score (BAS) was modeled:
where
and the constant
where
Second, we transform the BA score into BA in units of years by application of the Tscale method [
where
where BAc is the corrected biological age,
We present candidate biomarkers as means with SDs and by linear regression to describe the direction and change of the candidate biomarkers per year. We assessed normal distribution using qq plots and histograms, and checked variance of homogeneity and assessment of linearity by plotting residuals versus predicted values. Paired
Pearson correlation coefficient was calculated for each of the 31 candidate biomarkers as a function of CA (
Top: Scatterplots and Pearson’s correlations of: waist circumference (A), high density lipoprotein (B), forced expiratory volume in 1. sec (C), maximal oxygen uptake (D). Bottom: Pearson’s correlation coefficients of the 15 biomarkers significantly correlated with age and their intercorrelations. CA: chronological age; W/H: waist to hip ratio; FBG: fasting blood glucose; HbA_{1c}: glycated hemoglobin type A_{1c}; HDL: High density lipoprotein; LDL: Low density lipoprotein; CHOL: total cholesterol; suPAR: soluble urokinase plasminogen activator receptor; DBP: Diastolic blood pressure; SBP: Systolic blood pressure; FEV_{1}: Forced expiratory volume in 1. sec; VO_{2max}: maximal oxygen uptake.
We observed high intercorrelations for some of the variables (
We observed a high intercorrelation between waist circumference and waist/hip ratio, the latter having the highest correlation with CA. Despite this, waist circumference was selected due to its strong association with visceral adipose tissue [
Following the normalization of the data set comprising the 9 biomarkers, we applied PCA for women and men separately, with and without the inclusion of CA. By including and excluding CA, we could assess if the direction of the 1PC was similar in both cases, thus assuming that the 1PC can be seen as a general aging factor. The analysis showed high loading scores for CA on the 1PC for both women and men (0.473 and 0.515, respectively), confirming the close relationship between age and 1PC (
To clarify how the variables contribute to the estimation of the BA model, we calculated the percentage contribution of each variable using the following equation:
where a^{2}_{n} is the given loading score and
The linear combination of normalized variables for the 1PC by gender (chronological age included).
Principal component analysis variables  Loading scores for 1PC^{a}  

Women  Men  
Chronological age  0.473  0.515  
Mean arterial blood pressure^{b}  0.392  0.294  
Glycated hemoglobin  0.348  0.352  
Waist circumference  0.144  0.378  
Forced expiratory volume in 1 second  –0.164  –0.340  
Maximal oxygen consumption  –0.287  –0.321  
Adiponectin  0.199  0.078  
Highdensity lipoprotein  0.346  0.127  
Total cholesterol  0.405  0.337  
suPAR^{c}  0.220  0.167  
Eigenvalue^{d}  3.50  2.90  
Explained variance %^{e}  35.04  28.96 
^{a}1PC: first principal component comprising the best fit line with the largest sum of squares distances.
^{b}Mean arterial blood pressure = (1/3SBP + 2/3DBP), where SBP is systolic blood pressure and DBP is diastolic blood pressure.
^{c}suPAR: soluble urokinase plasminogen activator receptor.
^{d}Eigenvalue: the sum of squared distances for 1PC.
^{e}Explained variance %: how many percent does the 1PC explain of the total variance in the data set.
The linear combination of normalized variables for the 1PC^{a} by gender (chronological age excluded) and the relative contribution of each physiological variable to BA^{b} estimation.

Women  Men  

Loading scores  Contribution (%)  Loading scores  Contribution (%)  
Mean arterial blood pressure^{c}  0.435  18.9  0.349  12.2  
Glycated hemoglobin  0.408  16.7  0.324  10.5  
Waist circumference  0.173  3.0  0.491  24.1  
Forced expiratory volume in 1 second  –0.138  1.9  –0.309  9.5  
Maximal oxygen consumption  –0.341  11.6  –0.475  22.6  
Adiponectin  0.228  5.2  –0.046  0.2  
Highdensity lipoprotein  0.390  15.2  –0.020  0.04  
Total cholesterol  0.467  21.8  0.3804  14.5  
suPAR^{d}  0.238  5.7  0.254  6.4  
Eigenvalue^{e}  2.79  N/A^{f}  2.25  N/A  
Explained variance %^{g}  30.96  N/A  25.04  N/A 
^{a}1PC: first principal component comprising the best fit line with the largest sum of squares distances.
^{b}BA: biological age.
^{c}Mean arterial blood pressure = (1/3SBP + 2/3DBP), where SBP is systolic blood pressure and DBP is diastolic blood pressure.
^{d}suPAR: soluble urokinase plasminogen activator receptor.
^{e}Eigenvalue: the sum of squared distances for 1PC.
^{f}N/A: Not applicable.
^{g}Explained variance %: how many percent does the 1PC explain of the total variance in the data set.
By applying Equation 1, the loading scores from the PCA were used to construct individual standardized BAS as a function of the 9 biomarkers as shown in the following equations:
Subsequently, the BAS was scaled by applying Equation 4.
Scaling the score into units of years makes it more feasible to use when applying it to health promotion in the general population. Introducing this relationship between CA and BA has been shown to create some bias at the regression ends. Thus, following the previously mentioned correction model of Dubina et al [
The corrections are visualized in
Regression lines before (BA) and after (BAc) correction for women and men, respectively.
The BAc regression lines for women and men, respectively with 95% Confidence interval (shaded area), 95% Prediction intervals (black dotted lines) and line of identity (red dotted line). Slope (b), correlation coefficient (r) and coefficient of determination (R2).
Bland Altman plot for women and men, respectively with BIAS (red dotted line), upper and lower limits of agreement (black dotted lines).
In this study, we aimed to develop a BA model, able to measure healthy aging trajectory, using simple, clinically relevant biomarkers that would respond to changes in health behavior. We selected 9 biomarkers listed in
Sex differences were also observed in the relative contribution of each biomarker to the BA estimate. This indicates that some biomarkers of aging are influenced by sexual dimorphism [
The BA model is based on a healthy reference adult subsample of the population. However, in 8% (4/51) of the women and 16% (n=8/49) of the men, the age difference (CA – BAc) was more than +10 years (
In our data set, the highest correlated biomarker with CA was MAP (
To estimate BA, we used the 1PC as a general aging factor. In the field of BA prediction models, PCA is considered an improvement compared with multiple linear regression [
This is a firstgeneration model which is why this work should be used to initiate further research to understand the interpretation of the model fully. Larger sample size is necessary to do a proper sensitivity analysis on how changes in each biomarker affect the BA estimate. In addition, a larger sample size would improve the validity of the selected biomarkers. In this study, the biomarkers were selected based on their significant correlation with CA in a crosssectional analysis. Using crosssectional data provides information on the age difference in the biomarkers at a specific point in time. To improve the statistical validity of the measures selected as biomarkers, a significant longitudinal correlation with CA should be investigated. This way the age difference in the biomarkers can be assessed over time [
Applying the BA model to longitudinal data is an important future investigation, to see if a relatively high BA is a predictor of poor health outcomes such as T2D, CVD, and mortality. Furthermore, investigating the BA model in healthrelated interventions will provide evidence as to whether the model can be used as a valid clinical tool for measuring disease risks. Our study has strength in its reproducibility—a key element for BA applicability. The majority of the 9 biomarkers are common measurements in the clinic and in science, where standard quantitative techniques are used. Thus, quantifying BA by the combination of these 9 biomarkers has the advantage of being less susceptible to artifactual variations related to the method of measurement and being accessible from stored plasma samples and databases in national health registers. That being said, the feasibility of measuring suPAR and adiponectin in regular clinical routine is low. Thus, future studies should investigate how the exclusion of suPAR and adiponectin affects the ability of the BA model to identify highrisk individuals and to assess the effect of healthenhancing interventions.
The 9 physiological variables identified in this study as aging biomarkers are highly relevant to assess agerelated changes affecting the risk of disease and physical capacity. The BA model has potential for clinical use, due to low technical difficulty and minimally invasive techniques. Estimation of BA has potential as an outcome measure in healthpromoting interventions and as a pedagogical aid. Future research is required to investigate how the model will work in populations deviating from the healthy aging spectrum (eg, in individuals with T2D, CVD, or low cardiorespiratory fitness). We expect that the indicator of being biologically old is easy to understand, as a risk of disease and premature mortality, which explains why this indicator might drive individual motivation toward a healthier lifestyle. However, work remains to be performed to improve the model’s validity as a clinical tool and its predictive abilities including, but not restricted to, its reanalysis in a much larger sample size, testretest reliability, and assessment of the longitudinal stability of the biomarkers.
Correlation coefficient with chronological age for the nine measurements included as biomarkers in the BA model. (A) Waist circumference (cm), (B) High Density Lipoprotein (HDL) (mmol/L), (C) Forced Expiratory Volume in the first second (FEV_{1}) (L), (D) Maximal oxygen consumption (VO_{2max}) (ml/min/kg), (E) Total cholesterol concentration (mmol/L), (F) Mean Arterial Pressure (MAP) (mmHg), (G) Glycated hemoglobin (HbA_{1c}) (mmol/mol), (H) Adiponectin (mg/ml), (I) soluble urokinase plasminogen activator receptor (suPAR) (ng/ml).
Candidate biomarkers measured in the study participants (n=100) and their correlation with age.
first principal component
biological age
corrected biological age
biological age score
blood pressure
chronological age
diastolic blood pressure
fasting blood glucose
forced expiratory volume in 1 second
forced vital capacity
glycated hemoglobin
highdensity lipoprotein
Klemera and Doubal model
lowdensity lipoprotein.
mean arterial pressure
principal component analysis
systolic blood pressure
12item Short Form
soluble urokinase plasminogen activator receptor
type 2 diabetes mellitus
total cholesterol
maximal oxygen consumption
This work was supported by the Copenhagen Center for Health Technology, the Center for Healthy Aging, and the University College Copenhagen. The sponsors had no involvement in the study design, writing of the manuscript, or choice of publication.
KLSH and JWH conceptualized the study and in collaboration with ABK, KÅH, HBDS, and JCBJ designed the study. KLSH, MF, PH, and AB performed the data collection. KÅH and ABK did the formal analysis. KH wrote the first draft, and ABK, KÅH, JCBJ, HBDS, FD, and JWH revised and edited the manuscript.
None declared.