Literature DB >> 18597867

Developing a biological age assessment equation using principal component analysis and clinical biomarkers of aging in Korean men.

JinHo Park1, BeLong Cho, HyukTae Kwon, CheolMin Lee.   

Abstract

The purpose of the present study is to find clinically useful candidate biomarkers of aging, and using these to develop an equation measuring biological age (BA) in Korean men, then to validate the clinical usefulness of it. Among 4288 men who received medical health examinations, we selected 1588 men who met the normality criteria of each variable. We assumed that chronological ages (CA) of healthy persons represent the BA of them. Variables showing significant correlations with CA were selected. Redundant variables were excluded. We selected 11 variables: VO(2)max, percent body fat (%BF), waist circumference (WC), forced expiratory volume in 1 s (FEV1), systolic blood pressure (SBP), low density cholesterol (LDLCH), blood urea nitrogen (BUN), serum albumin (SA), erythrocyte sedimentation rate(ESR) hearing threshold (HT), and glycosylated hemoglobin (HBA1C). These 11 variables were then submitted into principal component analysis (PCA) and standardized BA scores were obtained. Using them and T-scale idea, the following equation to assess BA was developed: BA=-28.7+0.83(%BF)+0.48(WC)+0.13(SBP)-0.27(VO(2)max)+0.19(HT)-3.1(FEV1)+0.32(BUN)+0.06(LDLCH)-3.0(SA)+0.34(ESR)+4.6(HBA1C). We compared the BA of 3122 men by their fasting glucose and age level. The BA of the higher glucose level group was significantly higher than that of others at all CA levels. The selected 11 biomarkers encompassed known clinically important factors of adult diseases and functional disabilities. This BA assessment equation can be used in the general Korean male population and it proved to be clinically useful.

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Year:  2008        PMID: 18597867     DOI: 10.1016/j.archger.2008.04.003

Source DB:  PubMed          Journal:  Arch Gerontol Geriatr        ISSN: 0167-4943            Impact factor:   3.250


  17 in total

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Review 6.  Construction Formula of Biological Age Using the Principal Component Analysis.

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10.  Deep biomarkers of human aging: Application of deep neural networks to biomarker development.

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