2024,
BMC Medical Research Methodology.
DOI:
10.1186/s12874-024-02181-x
Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data
Marije H. Sluiskes, Jelle J. Goeman, Marian Beekman, P. Eline Slagboom, Hein Putter, Mar Rodríguez-Girondo
Abstract:
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.
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2024-03-31 16:59:00
#paper Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data. BMC Medical Research Methodology. 2024. https://doi.org/10.1186/s12874-024-02181-x. 生物年龄代表了个体真实的生理状态，其与实际年龄可能会有差异（个体可能比实际年龄更年轻/更老）。生物年龄与实际年龄之间的偏离（aging divergence）激发了广泛的研究兴趣，通常认为当生物年龄大于实际年龄时，个体会有更低的预期寿命以及更高的死亡或者疾病风险。常见的生物年龄通常由生化或者分子特征预测得知，而实际应用中这类数据往往属于横截面数据（cross-section data, 指在某一个时间点收集的数据，与时序数据相区别）。 这篇文章指出，当使用的是横截面数据时候，研究 aging divergence是否与某一些性状相关往往有一个隐含假设（identical-association-assumption），即与年龄最有关的形状也必然与aging divergence最有联系。该假设是否成立直接影响分析结果是否有生物学意义。可惜的是从横截面数据中我们无法测试这种假设是否成立或者不成立（untestable）。这篇文章的主要贡献是通过模拟和真实数据显示地揭示了这个经常被忽视的隐含假设，对衰老的研究和衰老机理的解释有一些警醒作用。