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Biological Age: Difference between revisions
→Estimation Methods
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Comparisons among MLR, PCA, Hochschild’s method, and KDM | Comparisons among MLR, PCA, Hochschild’s method, and KDM | ||
{| class="wikitable" | {| class="wikitable" | ||
! | ! Method | ||
! | ! Proposer | ||
! | ! Year | ||
! | ! Core concept | ||
! | ! Advantage | ||
! | ! Disadvantage | ||
! | ! Main researchers | ||
|- | |- | ||
| MLR | |||
| | |||
| More than 50 years ago | |||
| Aging biomarkers are determined by the correlation with CA using MLR model | |||
| MLR is the preliminary method and is easy to operate | |||
| (1) The standards of aging biomarkers lead to the paradox of CA | |||
(2) MLR also distorts the BA at the regression edge and ignores discontinuity in the aging rate{{pmid|6873212}}{{pmid|3226152}}{{pmid|950448}} | (2) MLR also distorts the BA at the regression edge and ignores discontinuity in the aging rate{{pmid|6873212}}{{pmid|3226152}}{{pmid|950448}} | ||
| Hollingsworth et al{{pmid|5841151}} and Kroll and Saxtrup{{pmid|11708217}} | |||
|- | |- | ||
| PCA | |||
| Nakamura | |||
| 1985 | |||
| PCA uses fewer uncorrelated variables to explain the main variance | |||
| | |||
# Biomarkers are uncorrelated variables{{pmid|16318865}} | # Biomarkers are uncorrelated variables{{pmid|16318865}} | ||
# PCA avoids the influence of regression edge in MLR{{pmid|3226152}} | # PCA avoids the influence of regression edge in MLR{{pmid|3226152}} | ||
| PCA cannot avoid the paradox of CA and some statistical deficiencies of MLR{{pmid|16318865}} | |||
| Nakamura et al,{{pmid|2737197}}{{pmid|2282902}} Nakamura and Miyao,{{pmid|12634284}} Nakamura et al,{{pmid|8026568}}{{pmid|9762521}} Nakamura and Miyao,{{pmid|17921421}} Nakamura et al,{{pmid|3226152}} Nakamura,<ref>75. Nakamura E. The assessment of physiological age based upon a principal component analysis of various physiological variables. J Kyoto Pref Univ Med. 1985;94:757–769. [Google Scholar]</ref> Nakamura and Miyao,{{pmid|18840798}} Nakamura et al,{{pmid|8803500}} Park et al,{{pmid|18597867}} Bai et al,{{pmid|19940465}} and Zhang{{pmid|25470806}}–{{pmid|24659482}} | |||
|- | |- | ||
| Hochschild’s method | |||
| Hochschild | |||
| 1989 | |||
| Hochschild’s method aims to select aging biomarkers according to their effects on life expectancy{{pmid|2684676}} | |||
| (1) Hochschild’s method solves the paradox of CA | |||
(2) Hochschild’s method avoids statistical problems of MLR | (2) Hochschild’s method avoids statistical problems of MLR | ||
| (1) Hochschild’s method is nonstandard and relatively complicated | |||
(2) Hochschild’s method is not based on the definition of BA | (2) Hochschild’s method is not based on the definition of BA | ||
(3) A large number of subjects are required when this approach is adopted for another system{{pmid|20005245}} | (3) A large number of subjects are required when this approach is adopted for another system{{pmid|20005245}} | ||
| Hochschild{{pmid|2684676}}{{pmid|2583248}}<ref>76. Hochschild R. Validating Biomarkers of Aging-Mathematical Approaches and Results of a 2462-Person Study. Boca Raton: CRC Press; 1994. [Google Scholar]</ref> | |||
|- | |- | ||
| KDM | |||
| Klemera and Doubal | |||
| 2006 | |||
| KDM is based on minimizing the distance between ''m'' regression lines and ''m'' biomarker points in an ''m''-dimensional space of all biomarkers{{pmid|16318865}} | |||
| (1) KDM performed better than CA{{pmid|23213031}} | |||
(2) KDM is precise when compared with other methods{{pmid|23213031}}{{pmid|20005245}}{{pmid|28110151}} | (2) KDM is precise when compared with other methods{{pmid|23213031}}{{pmid|20005245}}{{pmid|28110151}} | ||
(3) KDM solves the paradox of CA{{pmid|23213031}}{{pmid|20005245}} | (3) KDM solves the paradox of CA{{pmid|23213031}}{{pmid|20005245}} | ||
| The calculation of KDM is complicated{{pmid|20005245}} | |||
| Klemera and Doubal,{{pmid|16318865}} Levine,{{pmid|23213031}} Levine and Crimmins,{{pmid|25088793}} Cho et al{{pmid|20005245}} and Jee and Park{{pmid|28110151}} | |||
|} | |} | ||