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Cited by 65Genetics, Aging, and Longevity in Model OrganismsGenetic Associations and EpidemiologyAging and Gerontology Research

Biomarkers for Aging Identified in Cross-sectional Studies Tend to Be Non-causative

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Nelson, Paul G, Promislow, Daniel E L, Masel, Joanna·2020·The Journals of Gerontology: Series A
DOI 10.1093/gerona/glz174PMC PMC7457180

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Abstract

Biomarkers are important tools for diagnosis, prognosis, and identification of the causal factors of physiological conditions. Biomarkers are typically identified by correlating biological measurements with the status of a condition in a sample of subjects. Cross-sectional studies sample subjects at a single timepoint, whereas longitudinal studies follow a cohort through time. Identifying biomarkers of aging is subject to unique challenges. Individuals who age faster have intrinsically higher mortality rates and so are preferentially lost over time, in a phenomenon known as cohort selection. In this article, we use simulations to show that cohort selection biases cross-sectional analysis away from identifying causal loci of aging, to the point where cross-sectional studies are less likely to identify loci that cause aging than if loci had been chosen at random. We go on to show this bias can be corrected by incorporating correlates of mortality identified from longitudinal studies, allowing cross-sectional studies to effectively identify the causal factors of aging.

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Crossref · 10.1093/gerona/glz174

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  • Methods that incorporate additional biological information on mortality, as in the PhenoAge clock approach, are able to correct for this effect and select causal loci with much greater frequency.

    “However, methods that incorporate additional biological information on mortality, as in the PhenoAge clock approach, are able to correct for this effect and select causal loci with much greater frequency.”

    From Measuring biological age using omics data (2022) · cites this paper as [168]

  • Causal ageing features tend to become less represented with increased age as unhealthy agers become less likely to be included in studies due to death or health challenges that preclude participation in research.

    “Further, they show that causal ageing features tend to become less represented with increased age as unhealthy agers become less likely to be included in studies due to death or health challenges that preclude participation in research.”

    From Measuring biological age using omics data (2022) · cites this paper as [168]

  • A recent simulation study by Nelson et al. showed that first-generation methylation ageing clocks do worse than random chance at identifying causal ageing loci due to cohort selection effects in cross-sectional ageing cohorts.

    “A recent simulation study by Nelson et al.168 bolsters the rationale for training these composite models, showing that first-generation methylation ageing clocks do worse than random chance at identifying causal ageing loci due to cohort selection effects that occur in cross-sectional ageing cohorts169,170.”

    From Measuring biological age using omics data (2022) · cites this paper as [168]

  • Nelson et al. demonstrated important limitations of ageing-clock models that are built with cross-sectional cohort studies.

    “This study demonstrated important limitations of ageing-clock models that are built with cross-sectional cohort studies”

    From Measuring biological age using omics data (2022) · cites this paper as [168]

  • Nelson et al. used simulation studies to show that the use of more complex second-generation ageing score training objectives overcomes some of these limitations.

    “and used simulation studies to show that the use of more complex second-generation ageing score training objectives overcomes some of these limitations.”

    From Measuring biological age using omics data (2022) · cites this paper as [168]

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Research works printed but not linked: 2

  1. Baily, Esq. FRS &c. Philos Trans R Soc Lond, Ser B: Biol Sci. 1825;115:513-583.
  2. Meer KM, Nelson PG, Xiong K, Masel J. Transcriptional Error Rates Vary by Gene Expression Level in E. coli but not S. cerevisiae. bioRxiv. 2019:554329.

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Authors
Nelson, P. G., Promislow, D. E. L., & Masel, J.
Affiliations
  • University of Arizona (US)
  • University of Washington (US)
  • Department of Ecology and Evolutionary Biology, University of Arizona, Tucson
Corresponding author
Nelson, Paul G
ORCID
Venue
The Journals of Gerontology: Series A
Publisher
Oxford University Press (OUP)
Published
2019-07-29
Volume
75
Issue
3
Pages
466-472
ISSN
  • 1079-5006 (ISSN-L)
  • 1758-535X
DOI
10.1093/gerona/glz174
PMC
PMC7457180
OpenAlex
W2966563903
Cited by
65
References
57
Citations by year
2026: 9 · 2025: 11 · 2024: 16 · 2023: 5 · 2022: 11 · 2021: 9 · 2020: 4
FWCI
2.18
Type
journal article
Language
en
Open access
Licence
Locations
  • The Journals of Gerontology Series A (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (K12GM000708)
  • John Templeton Foundation (60814DMS1561814)
  • National Science Foundation (AG049494)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Aging and Gerontology Research
Keywords
aging biomarkers, cross-sectional study, causal inference, mortality, longitudinal study, simulation study
MeSH terms
Aging, Computer Simulation, Cross-Sectional Studies, Humans, Cohort Studies, Bias, Patient Selection, Epigenesis, Genetic

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