Driver or Passenger? A Bradford Hill Framework for Aging-Associated lncRNAs.
Abstract
Transcriptomic surveys of aging tissues routinely report hundreds to thousands of differentially expressed long non-coding RNAs (lncRNAs), and the field has no agreed way of separating causal drivers from passengers that merely track the aging phenotype. I translate Sir Austin Bradford Hill's nine criteria for causal inference, formulated in 1965, into operational tests for aging-associated lncRNAs. Each criterion is recorded as 0, 1 or 2, and the per-criterion profile is the result: no sum or band decides a category. Hill offered viewpoints, not independent quantities to be added; here half of the cited studies support two or more criteria, so a total counts some evidence twice. Two axes are reported separately - molecular causal evidence, and organism-level validation (criterion 8), recorded as supportive, not evaluated, or in vivo at a non-aging endpoint - so that an animal experiment on development, reproduction, cancer or injury is not read as a negative test of aging. Applied to ten worked examples chosen to illustrate rather than to sample the field - NEAT1, MEG3, lincRNA-p21, MALAT1, MIR31HG, SALNR, TERRA, PANDAR, HOTAIR, TUG1 - the profiles separate candidates awaiting an animal experiment from those whose animal experiments addressed something else. MALAT1 is instructive: of six in-vivo perturbation studies, one assays a senescence endpoint. No candidate has an aging experiment that returned a negative result, and none reaches the top of criterion 8, which requires conditional deletion or a titrated antisense regimen with rescue. Organism-level evidence, not literature volume, is the structural bottleneck.
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Research summary
The paper proposes a standardized framework using Bradford Hill criteria to distinguish causal drivers of aging from passenger lncRNAs, identifying organism-level validation as the primary bottleneck. This is relevant because it addresses the fundamental challenge of identifying true causal mechanisms of aging in transcriptomic data, which is essential for developing effective longevity interventions rather than targeting non-causal biomarkers.
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