Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data
Abstract Background There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual’s true global physiological state than chronological age. Biological age predictors are often generated based...
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | https://doi.org/10.1186/s12874-024-02181-x |
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| author | Marije H. Sluiskes Jelle J. Goeman Marian Beekman P. Eline Slagboom Hein Putter Mar Rodríguez-Girondo |
| author_facet | Marije H. Sluiskes Jelle J. Goeman Marian Beekman P. Eline Slagboom Hein Putter Mar Rodríguez-Girondo |
| author_sort | Marije H. Sluiskes |
| collection | DOAJ |
| description | Abstract Background There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual’s true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one’s chronological age-independent aging divergence ∆. Methods We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold. Results The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging’s association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random. Conclusions Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors. |
| first_indexed | 2024-04-25T01:04:58Z |
| format | Article |
| id | doaj.art-786492533f114367a8d0041a7311830a |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-04-25T01:04:58Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-786492533f114367a8d0041a7311830a2024-03-10T12:15:41ZengBMCBMC Medical Research Methodology1471-22882024-03-0124111510.1186/s12874-024-02181-xClarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real dataMarije H. Sluiskes0Jelle J. Goeman1Marian Beekman2P. Eline Slagboom3Hein Putter4Mar Rodríguez-Girondo5Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical CenterMedical Statistics, Department of Biomedical Data Sciences, Leiden University Medical CenterMolecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical CenterMolecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical CenterMedical Statistics, Department of Biomedical Data Sciences, Leiden University Medical CenterMedical Statistics, Department of Biomedical Data Sciences, Leiden University Medical CenterAbstract Background There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual’s true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one’s chronological age-independent aging divergence ∆. Methods We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold. Results The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging’s association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random. Conclusions Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.https://doi.org/10.1186/s12874-024-02181-xAgingBiological ageAging divergenceAging rateAging clocksCross-sectional biological age predictors |
| spellingShingle | Marije H. Sluiskes Jelle J. Goeman Marian Beekman P. Eline Slagboom Hein Putter Mar Rodríguez-Girondo Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data BMC Medical Research Methodology Aging Biological age Aging divergence Aging rate Aging clocks Cross-sectional biological age predictors |
| title | Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data |
| title_full | Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data |
| title_fullStr | Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data |
| title_full_unstemmed | Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data |
| title_short | Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data |
| title_sort | clarifying the biological and statistical assumptions of cross sectional biological age predictors an elaborate illustration using synthetic and real data |
| topic | Aging Biological age Aging divergence Aging rate Aging clocks Cross-sectional biological age predictors |
| url | https://doi.org/10.1186/s12874-024-02181-x |
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