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Age Estimation From Blood Test Results Using a Random Forest Model.

Satomi Kodera1, Osamu Yokoi2, Masaki Kaneko1,3

  • 1KYB Medical Service Co., LTD, Tokyo, Japan.

Journal of Clinical Laboratory Analysis
|June 12, 2025
PubMed
Summary

Estimating biological age from screening data using a random forest model provides a precise measure of physical aging. This "blood age" can help identify aging-related health issues like metabolic syndrome.

Keywords:
age predictionbiological ageblood agegender differencepostmenopausal womenrandom forest method

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Area of Science:

  • Preventive Medicine
  • Gerontology
  • Biomarkers

Background:

  • Screening data plays a crucial role in understanding aging and health.
  • The number of data items required for accurate health assessments needs clarification.

Purpose of the Study:

  • To estimate chronological age from screening data.
  • To determine the minimum number of data items needed for reliable age estimation.
  • To explore the utility of estimated age in preventive medicine.

Main Methods:

  • A random forest model was employed.
  • Data from 11,554 individuals (aged 0-95 years) undergoing screening tests were analyzed.
  • Analyses included blood, urine, and saliva tests.

Main Results:

  • High accuracy (R² = 0.7010) was achieved using 71 data items.
  • Accuracy remained high (R² = 0.6937) with 15 items.
  • Accuracy decreased significantly with fewer than 800 datasets or 7 data items.

Conclusions:

  • Age estimation from blood data (blood age) is a precise indicator of physical aging.
  • Blood age and other biological age estimates show promise for studying aging-related conditions.
  • This approach can aid in exploring issues like metabolic and frail syndromes.