I've often wondered how my Garmin 955 estimates biological age. Recently, I did a search on Google Scholar on "biological age estimation." One hit that interested me is:

Linpei Jia, Weiguang Zhang & Xiangmei Chen (2017) Common methods of biological age estimation, Clinical Interventions in Aging, 12:, 759-772, DOI: 10.2147/CIA.S134921

 


 Aging Biomarkers Standards Count
Grip Strength Show significant changes with age
Not highly correlated with another biomarker
Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Have high reproducibility in cross-species comparisons
Reflect physiological function
Change at a rate reflecting the rate of aging
Significant differences among individuals
8
Systolic Blood Pressure Show significant changes with age
Not highly correlated with another biomarker
Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Have high reproducibility in cross-species comparisons
Reflect physiological function
Change at a rate reflecting the rate of aging
Highly reproducible
8
Forced Expiratory Volume In 1 Second Show significant changes with age
Not highly correlated with another biomarker
Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Have high reproducibility in cross-species comparisons
Reflect physiological function
Change at a rate reflecting the rate of aging
7
Digital Symbol Test Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Display changes over a relatively short period
Measurable during a relatively short time interval
Highly reproducible
Significant differences among individuals
6
D-Dimer Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Display changes over a relatively short period
Measurable during a relatively short time interval
Highly reproducible
Significant differences among individuals
6
Cystatin C Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Display changes over a relatively short period
Measurable during a relatively short time interval
Highly reproducible
Significant differences among individuals
6
Intima-Media Thickness Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Display changes over a relatively short period
Measurable during a relatively short time interval
Highly reproducible
Significant differences among individuals
6
Mitral Annulus Peak E Anterior Wall Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Display changes over a relatively short period
Measurable during a relatively short time interval
Highly reproducible
Significant differences among individuals
6
Serum Albumin Show significant changes with age
Not highly correlated with another biomarker
Monitor a basic mechanism of the aging process and not an effect of disease
Have high reproducibility in cross-species comparisons
Reflect physiological function
Highly reproducible
6
Erythrocyte Sedimentation Rate Show significant changes with age
Not highly correlated with another biomarker
Monitor a basic mechanism of the aging process and not an effect of disease
Reflect physiological function
Reflect physiological function
5
Vo2 Max Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Have high reproducibility in cross-species comparisons
Reflect physiological function
Change at a rate reflecting the rate of aging
5
Serum Globulin Show significant changes with age
Not highly correlated with another biomarker
Have high reproducibility in cross-species comparisons
Reflect physiological function
Highly reproducible
5
Alkaline Phosphatase Show significant changes with age
Not highly correlated with another biomarker
Have high reproducibility in cross-species comparisons
Reflect physiological function
Highly reproducible
5
Serum Urine Nitrogen Show significant changes with age
Not highly correlated with another biomarker
Have high reproducibility in cross-species comparisons
Reflect physiological function
Highly reproducible
5
Lateral Stance Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Have high reproducibility in cross-species comparisons
Change at a rate reflecting the rate of aging
4
Whole-Body Reaction Time Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Have high reproducibility in cross-species comparisons
Change at a rate reflecting the rate of aging
4
Sit-And-Reach Test Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Have high reproducibility in cross-species comparisons
Change at a rate reflecting the rate of aging
4
Vertical Jump Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Have high reproducibility in cross-species comparisons
Change at a rate reflecting the rate of aging
4
Soft Lean Mass Monitor a basic mechanism of the aging process and not an effect of disease
Noninvasive or minimally invasive
Have high reproducibility in cross-species comparisons
Change at a rate reflecting the rate of aging
4
One Leg Stand With Eyes Open Show significant changes with age
Have high reproducibility in cross-species comparisons
Significant differences among individuals
3
Functional Reach Show significant changes with age
Have high reproducibility in cross-species comparisons
Significant differences among individuals
3
10-Minute Walk Show significant changes with age
Have high reproducibility in cross-species comparisons
Significant differences among individuals
3
Skin Elasticity Show significant changes with age
Not highly correlated with another biomarker
Reflect physiological function
3
Hematocrit Show significant changes with age
Have high reproducibility in cross-species comparisons
Highly reproducible
3
Serum Glutamic Oxalacetic Transaminase Show significant changes with age
Have high reproducibility in cross-species comparisons
Highly reproducible
3
Vital Capacity Show significant changes with age
Not highly correlated with another biomarker
Reflect physiological function
3
Vibrometer Show significant changes with age
Not highly correlated with another biomarker
Reflect physiological function
3
Light Extinction Test Show significant changes with age
Not highly correlated with another biomarker
Reflect physiological function
3
Phosphates Show significant changes with age
Have high reproducibility in cross-species comparisons
Highly reproducible
3
Visual Acuity Show significant changes with age
Not highly correlated with another biomarker
Reflect physiological function
3
Total Protein Show significant changes with age
Have high reproducibility in cross-species comparisons
Highly reproducible
3
Auditory Function Show significant changes with age
Not highly correlated with another biomarker
Reflect physiological function
3
Serum Cholesterol Show significant changes with age
Not highly correlated with another biomarker
Reflect physiological function
3
Triglycerides Show significant changes with age
Not highly correlated with another biomarker
Reflect physiological function
3
Blood Glucose Show significant changes with age
Not highly correlated with another biomarker
Reflect physiological function
3
Calcium Show significant changes with age
Highly reproducible
2
Glycosylated Hemoglobin Monitor a basic mechanism of the aging process and not an effect of disease
Reflect physiological function
2
Ratio Of Albumin To Globulin Show significant changes with age
Not highly correlated with another biomarker
2
Low-Density Cholesterol Monitor a basic mechanism of the aging process and not an effect of disease
Reflect physiological function
2
Waist Circumference Monitor a basic mechanism of the aging process and not an effect of disease
Reflect physiological function
2
Percent Body Fat Monitor a basic mechanism of the aging process and not an effect of disease
Reflect physiological function
2
Lymphocytes Show significant changes with age
Highly reproducible
2
Serum Creatinine Show significant changes with age
Highly reproducible
2
Mean Corpuscular Hemoglobin Show significant changes with age
Not highly correlated with another biomarker
2
Hearing Threshold Monitor a basic mechanism of the aging process and not an effect of disease
Reflect physiological function
2
Blood Urea Nitrogen Monitor a basic mechanism of the aging process and not an effect of disease 1
;

 

Northwestern University distinguishes between biological and chronological age as follows:

"Chronological age is how long you have existed. Biological age is how old your cells are.

Sometimes these two numbers are the same for people, but everyone ages at different rates.

Your healthspan is the period of life where you are free of any aging-related disease. Dr. Vaughan and the Potocsnak Longevity Institute are aiming to increase the human healthspan by slowing down the aging process to push back the onset of aging-related diseases."

 I've written elsewhere about the relationship between aging and lifestyle. What struck me as new was how characteristics that we can modify about ourselves show up in many estimation models for biological age. Linpei Jia, et al., implicitly allude to this in the paper:

"Since the BA model is a potential estimation of aging for clinical use, such as predicting onset and prognosis of diseases, improving the elderly’s living qualities, and realizing successful aging, here we summarize previous BA studies, illustrate the basic statistical steps, and thoroughly discuss the comparisons among the four common BA estimation methods."

"Genetic factors play an important role in the aging process. Approximately 20%–50% of the biological variations are caused by genetic factors."

Put in other words, 50%-80% of the differences in the aging process between people of similar chronological age are due to lifestyle variations.

Linpei Jia, et al., review the prevalent formulations for biological age computation along with the individual biological measurands that go into the formulas. They summarize the biomarkers in Table 1 of the article. The table has a list of clinical standards or characteristics of each set of biomarkers. For each set, it lists the relevant biomarkers. I was interested in a slightly different view of the data. To the left, I created a table of biomarkers sorted by how many clinical standards sets they appear in. Also, for each biomarker, I list the standards.  The code I wrote to create this table is below.

 Grip strength, systolic blood pressure, and forced expiratory volume appear at the top of the list. The first is easily modifiable with strength training. The second two are highly affected by cardiovascular training, both aerobic and anaerobic. 

Further down the list, is VO2 max, defined by the American Heart Association (AHA), as:

"a measure of the body's maximal ability to use oxygen to perform physical work, relies on interconnected functioning of the cardiovascular system, lungs, and skeletal musculature. A large body of work, spanning 3 decades, has shown that CRF is a potent predictor of key health outcomes, including incident cardiovascular disease (CVD) and mortality."

VO2 max is such a powerful predictor of health that the AHA considered adding it to its list of vital signs at one point. 

 Norwegian University of Science and Technology's Fitness Age Calculator

FirstBeat's What’s Your Fitness Age? VO2max Reveals It

 

 

Incidentally, the code I used to create the table above is here:

# -*- coding: utf-8 -*-
"""
Created on Mon Jan 1 06:16:33 2024

@author: davev
"""

import pandas as pd
import re as re
from collections import Counter

fn = 'T0001-10.2147_CIA.S134921.csv'

table = pd.read_csv(fn, usecols=['Standards', 'Aging biomarkers'])
table.dropna(inplace=True)

biomarker_lst = []
standards_lst = []

biomarker_dict = dict()

for row in table.iterrows():
biomarkers = row[1].loc['Aging biomarkers']
biomarkers = biomarkers.split(',')
for item in biomarkers:
temp = re.sub(r'\d\d ', '', item)
temp = re.sub(r'\d\d$', '', temp)
temp = temp.removeprefix('and ')
temp = temp.title()
if temp != '' and temp != ' ':
biomarker_lst.append(temp)
if temp in biomarker_dict:
biomarker_dict[temp] = biomarker_dict[temp] + '\n' + row[1].loc['Standards']
else:
biomarker_dict[temp] = row[1].loc['Standards']


counted = Counter(biomarker_lst)
counted_dict = dict(counted)
counted_df = pd.DataFrame(counted_dict, index=['Count']).transpose()


biomarker_df = pd.DataFrame(biomarker_dict, index=['Standards']).transpose()

data = pd.concat([biomarker_df, counted_df], axis=1).sort_values(by='Count', ascending=False)
data_html = data.to_html()