Cited by 29Alzheimer's disease research and treatmentsDementia and Cognitive Impairment ResearchGenetic Associations and Epidemiology

Age stratification corrects bias in estimated hazard of APOE genotype for Alzheimer's disease

Open access
Liu, Li, Caselli, Richard J.·2018·Alzheimer's & Dementia: Translational Research & Clinical Interventions

Abstract

Introduction: The apolipoprotein E (APOE) e4 allele is a major genetic risk factor of late‐onset Alzheimer's disease. However, its interaction with two other canonical risk factors, age and sex, is not clear. Previous studies have reported conflicting results on its differential effects in men and women, its association with young‐onset AD before the age of 65 years, and its significance in genetic admixture populations. In these studies, the hazard of the e4 allele was assumed to be constant during aging. However, this hypothesis has not been tested and its violation may lead to significant biases and contribute to such discrepant findings. Methods: In a prospective cohort of 4727 subjects, we performed Cox regression analysis of the association of the e4 allele with AD age of onset. We then performed diagnostics on the resulting model and tested if the hazard of the e4 allele violated the assumption of proportionality during aging. We examined whether incorporating age stratifications and time‐dependent coefficients could restore the proportionality. We then validated our findings in four independent cohorts. Results: Hazard of the e4 allele for AD was nonproportional. It took a stepwise decline around the age of 80 years for men and around the age of 75 years for women. By stratifying subjects into a younger group and an older group, we detected more consistent effects of the e4 allele across multiple independent cohorts. We also found that the e4 allele was a significant risk factor for young‐onset AD with age of onset before 65 years. Discussion: Age compositions of study cohorts can significantly bias the estimated effect of the APOE genotype. Studies of AD should consider hidden age structures among subjects and routinely employ appropriate age and sex stratification strategies or nonparametric modeling in experimental designs and data analysis. Finally, we argue that the e4 allele is a risk factor not only for late‐onset AD but also for young‐onset AD.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.trci.2018.09.006

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The effects of APOE genotype on AD risk are significantly diminished in older individuals (>75 years, LOAD).

    “This is also consistent with recent meta-analyses of AD that have shown that the effects of APOE genotype on AD risk are significantly diminished in older individuals (>75 years, LOAD) (41).”

    From Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets (2021) · cites this paper as [41]

    The Results report a statistically significant decline in the APOE ε4 hazard with age, supporting the central claim. But the transition was approximately 75 years for women and 80 years for men, not uniformly >75 years; the paper also presents analyses of multiple cohorts rather than a meta-analysis. The cited passages have no page numbers in our copy. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. [21]Jara A. Hanson T.E. Quintana F.A. Muller P. Rosner G.L. DPpackage: Bayesian Non- and Semi-parametric Modelling in R J Stat Softw 40 2011 1 30 PMC3142948 21796263

Cited by

Sorted by citations

Authors
Liu, L., & Caselli, R. J.
Affiliations
Mayo Clinic in Arizona (US)
Corresponding author
  • Liu, Li
  • Caselli, Richard J.
Venue
Alzheimer's & Dementia: Translational Research & Clinical Interventions
Publisher
Wiley
Published
2018-01
Volume
4
Issue
1
Pages
602-608
ISSN
2352-8737 (ISSN-L)
OpenAlex
W2898796531
Cited by
29
References
34
Citations by year
2026: 3 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 3 · 2021: 7 · 2020: 3 · 2019: 7
FWCI
2.92
Type
journal article
Language
en
Open access
Locations
  • Alzheimer s & Dementia Translational Research & Clinical Interventions (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
National Institute on Aging
Topics
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
APOE ε4, Alzheimer's disease, age stratification, proportional hazards assumption, age of onset, early-onset Alzheimer's disease, sex differences, genetic risk factors, time-dependent coefficients

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review