Cited by 47Dementia and Cognitive Impairment ResearchHealth disparities and outcomesAlzheimer's disease research and treatments

Socioeconomic Deprivation, Genetic Risk, and Incident Dementia

Open access
Klee, Matthias, Leist, Anja K., Veldsman, Michele, Ranson, Janice M., Llewellyn, David J.·2023·American Journal of Preventive Medicine

Abstract

INTRODUCTION: Socioeconomic factors and genetic predisposition are established risk factors for dementia. It remains unclear whether associations of socioeconomic deprivation with dementia incidence are modified by genetic risk. METHODS: Participants in the UK Biobank aged ≥60 years and of European ancestry without dementia at baseline (2006-2010) were eligible for the analysis, with the main exposures area-level deprivation based on the Townsend Deprivation Index and individual-level socioeconomic deprivation based on car and home ownership, housing type and income, and polygenic risk of dementia. Dementia was ascertained in hospital and death records. Analysis was conducted in 2021. RESULTS: In this cohort study, 196,368 participants (mean [SD] age=64.1 [2.9] years, 52.7% female) were followed up for 1,545,316 person-years (median [IQR] follow-up=8.0 [7.4-8.6] years). In high genetic risk and high area-level deprivation, 1.71% (95% CI=1.44, 2.01) developed dementia compared with 0.56% (95% CI=0.48, 0.65) in low genetic risk and low-to-moderate area-level deprivation (hazard ratio=2.31; 95% CI=1.84, 2.91). In high genetic risk and high individual-level deprivation, 1.78% (95% CI=1.50, 2.09) developed dementia compared with 0.31% (95% CI=0.20, 0.45) in low genetic risk and low individual-level deprivation (hazard ratio=4.06; 95% CI=2.63, 6.26). There was no significant interaction between genetic risk and area-level (p=0.77) or individual-level (p=0.07) deprivation. An imaging substudy including 11,083 participants found a greater burden of white matter hyperintensities associated with higher socioeconomic deprivation. CONCLUSIONS: Individual-level and area-level socioeconomic deprivation were associated with increased dementia risk. Dementia prevention interventions may be particularly effective if targeted to households and areas with fewer socioeconomic resources, regardless of genetic vulnerability.

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.amepre.2023.01.012

Cites

8 of 8 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?

  • Covariates were selected based on their known associations with Alzheimer Disease in the UK Biobank cohort.

    “Covariates were selected based on their known associations with Alzheimer Disease in the UKB cohort.”

    From TET2-mutant myeloid cells mitigate Alzheimer’s disease progression via CNS infiltration and enhanced phagocytosis in mice (2025) · cites this paper as [78]

    The Methods > Measures section lists covariates used in models of the UK Biobank cohort. It does not say they were selected because of known associations with Alzheimer disease; the study’s modeled outcome was incident all-cause dementia. Page numbers are not available in our copy. (LLM, conf=0.84)

    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

1 retracted

Flagged first, then by citations

Research works printed but not linked: 38

  1. [2]Lambert JC Ibrahim-Verbaas CA Harold D Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease Nat Genet 45 12 2013 1452 1458 10.1038/ng.2802 24162737 PMC3896259 · DOI · Paper page
  2. [3]Jansen IE Savage JE Watanabe K Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk [published correction appears in Nat Genet. 2020;52(3):354] Nat Genet 51 3 2019 404 413 10.1038/s41588-018-0311-9 30617256 PMC6836675 · DOI · Paper page
  3. [4]Lourida I Hannon E Littlejohns TJ Association of lifestyle and genetic risk with incidence of dementia JAMA 322 5 2019 430 437 10.1001/jama.2019.9879 31302669 PMC6628594 · DOI · Paper page
  4. [5]Licher S Ahmad S Karamujić-Čomić H Genetic predisposition, modifiable-risk-factor profile and long-term dementia risk in the general population Nat Med 25 9 2019 1364 1369 10.1038/s41591-019-0547-7 31451782 PMC6739225 · DOI
  5. [6]Ajnakina O Cadar D Steptoe A. Interplay between socioeconomic markers and polygenic predisposition on timing of dementia diagnosis J Am Geriatr Soc 68 7 2020 1529 1536 10.1111/jgs.16406 32187654 PMC7363562 · DOI
  6. [8]Powell WR Buckingham WR Larson JL Association of neighborhood-level disadvantage with Alzheimer disease neuropathology JAMA Netw Open 3 6 2020 e207559 10.1001/jamanetworkopen.2020.7559 PMC7290421 32525547 · DOI
  7. [9]Townsend P. Deprivation J Soc Policy 16 2 1987 125 146 10.1017/S0047279400020341 · DOI · Paper page
  8. [10]Weiss J Puterman E Prather AA Ware EB Rehkopf DH. A data-driven prospective study of dementia among older adults in the United States PLoS One 15 10 2020 e0239994 10.1371/journal.pone.0239994 PMC7540891 33027275 · DOI
  9. [11]Clarke PJ Ailshire JA House JS Cognitive function in the community setting: the neighbourhood as a source of “cognitive reserve”? J Epidemiol Community Health 66 8 2012 730 736 10.1136/jech.2010.128116 21515547 PMC3387518 · DOI
  10. [12]Clarke PJ Weuve J Barnes L Evans DA Mendes de Leon CF. Cognitive decline and the neighborhood environment Ann Epidemiol 25 11 2015 849 854 10.1016/j.annepidem.2015.07.001 26253697 PMC4609590 · DOI
  11. [13]de Keijzer C Bauwelinck M Dadvand P. Long-term exposure to residential greenspace and healthy ageing: a systematic review Curr Environ Health Rep 7 1 2020 65 88 10.1007/s40572-020-00264-7 31981136 · DOI
  12. [14]Peters R Ee N Peters J Booth A Mudway I Anstey KJ. Air pollution and dementia: a systematic review J Alzheimers Dis 70 s1 2019 S145 S163 10.3233/JAD-180631 30775976 PMC6700631 · DOI · Paper page
  13. [15]de Lange AMG Kaufmann T Quintana DS Prominent health problems, socioeconomic deprivation, and higher brain age in lonely and isolated individuals: a population-based study Behav Brain Res 414 2021 113510 10.1016/j.bbr.2021.113510 34358570 · DOI
  14. [16]Hunt JFV Vogt NM Jonaitis EM Association of neighborhood context, cognitive decline, and cortical change in an unimpaired cohort Neurology 96 20 2021 e2500 e2512 10.1212/WNL.0000000000011918 33853894 PMC8205478 · DOI
  15. [17]Tosto G Zimmerman ME Carmichael OT Brickman AM Alzheimer's Disease Neuroimaging Initiative. Predicting aggressive decline in mild cognitive impairment: the importance of white matter hyperintensities JAMA Neurol 71 7 2014 872 877 10.1001/jamaneurol.2014.667 24821476 PMC4107926 · DOI
  16. [18]Zhang N Ranson JM Zheng ZJ Interaction between genetic predisposition, smoking, and dementia risk: a population-based cohort study Sci Rep 11 1 2021 12953 10.1038/s41598-021-92304-x 34155245 PMC8217565 · DOI
  17. [19]Pan KY Xu W Mangialasche F Grande G Fratiglioni L Wang HX. The role of apolipoprotein E epsilon4 in the association between psychosocial working conditions and dementia Aging (Albany NY) 12 4 2020 3730 3746 10.18632/aging.102843 32081835 PMC7066897 · DOI
  18. [20]Zhang Y Jin X Lutz MW Interaction between APOE ε4 and dietary protein intake on cognitive decline: a longitudinal cohort study Clin Nutr 40 5 2021 2716 2725 10.1016/j.clnu.2021.03.004 33933737 PMC10106247 · DOI
  19. [22]Wilkinson T Schnier C Bush K Identifying dementia outcomes in UK Biobank: a validation study of primary care, hospital admissions and mortality data Eur J Epidemiol 34 6 2019 557 565 10.1007/s10654-019-00499-1 30806901 PMC6497624 · DOI · Paper page
  20. [23]Sudlow C Gallacher J Allen N UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age PLoS Med 12 3 2015 e1001779 10.1371/journal.pmed.1001779 PMC4380465 25826379 · DOI · Paper page
  21. [25]Littlejohns TJ Holliday J Gibson LM The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions Nat Commun 11 1 2020 2624 10.1038/s41467-020-15948-9 32457287 PMC7250878 · DOI · Paper page
  22. [26]Kim S Egerter S Cubbin C Takahashi ER Braveman P. Potential implications of missing income data in population-based surveys: an example from a postpartum survey in California Public Health Rep 122 6 2007 753 763 10.1177/003335490712200607 18051668 PMC1997243 · DOI
  23. [29]Lyall DM Cox SR Lyall LM Association between APOE e4 and white matter hyperintensity volume, but not total brain volume or white matter integrity Brain Imaging Behav 14 5 2020 1468 1476 10.1007/s11682-019-00069-9 30903549 PMC7572345 · DOI · Paper page
  24. [30]Cox SR Lyall DM Ritchie SJ Associations between vascular risk factors and brain MRI indices in UK Biobank Eur Heart J 40 28 2019 2290 2300 10.1093/eurheartj/ehz100 30854560 PMC6642726 · DOI
  25. [32]Alfaro-Almagro F Jenkinson M Bangerter NK Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank Neuroimage 166 2018 400 424 10.1016/j.neuroimage.2017.10.034 29079522 PMC5770339 · DOI
  26. [33]Wardlaw JM Valdés Hernández MC Muñoz-Maniega S What are white matter hyperintensities made of? Relevance to vascular cognitive impairment [published correction appears in J Am Heart Assoc. 2016;5(1):e002006] J Am Heart Assoc 4 6 2015 e001140 10.1161/JAHA.114.001140 PMC4599520 26104658 · DOI
  27. [34]Nobis L Manohar SG Smith SM Hippocampal volume across age: nomograms derived from over 19,700 people in UK Biobank Neuroimage Clin 23 2019 101904 10.1016/j.nicl.2019.101904 PMC6603440 31254939 · DOI
  28. [36]VanderWeele TJ. Causal mediation analysis with survival data Epidemiology 22 4 2011 582 585 10.1097/EDE.0b013e31821db37e 21642779 PMC3109321 · DOI
  29. [37]Donders ART van der Heijden GJMG Stijnen T Moons KGM. Review: a gentle introduction to imputation of missing values J Clin Epidemiol 59 10 2006 1087 1091 10.1016/j.jclinepi.2006.01.014 16980149 · DOI · Paper page
  30. [38]Doove LL Van Buuren S Dusseldorp E. Recursive partitioning for missing data imputation in the presence of interaction effects Comput Stat Data Anal 72 2014 92 104 10.1016/j.csda.2013.10.025 · DOI
  31. [40]Bradley V Nichols TE. Addressing selection bias in the UK Biobank neurological imaging cohort MedRxiv 2022 10.1101/2022.01.13.22269266 Preprint. Online January 24 · DOI
  32. [41]Alfaro-Almagro F McCarthy P Afyouni S Confound modelling in UK Biobank brain imaging Neuroimage 224 2021 117002 10.1016/j.neuroimage.2020.117002 PMC7610719 32502668 · DOI · Paper page
  33. [42]Cole SR Hernán MA. Constructing inverse probability weights for marginal structural models Am J Epidemiol 168 6 2008 656 664 10.1093/aje/kwn164 18682488 PMC2732954 · DOI · Paper page
  34. [43]van Buuren S Groothuis-Oudshoorn K. mice: multivariate imputation by chained equations in R J Stat Softw 45 3 2011 1 67 10.18637/jss.v045.i03 · DOI · Paper page
  35. [46]Leonenko G Baker E Stevenson-Hoare J Identifying individuals with high risk of Alzheimer's disease using polygenic risk scores Nat Commun 12 1 2021 4506 10.1038/s41467-021-24082-z 34301930 PMC8302739 · DOI
  36. [47]Debette S Schilling S Duperron MG Larsson SC Markus HS. Clinical significance of magnetic resonance imaging markers of vascular brain injury: a systematic review and meta-analysis JAMA Neurol 76 1 2019 81 94 10.1001/jamaneurol.2018.3122 30422209 PMC6439887 · DOI
  37. [51]Weuve J Proust-Lima C Power MC Guidelines for reporting methodological challenges and evaluating potential bias in dementia research Alzheimers Dement 11 9 2015 1098 1109 10.1016/j.jalz.2015.06.1885 26397878 PMC4655106 · DOI
  38. [52]Rojas-Saunero LP Young JG Didelez V Ikram MA Swanson SA. Choosing questions before methods in dementia research with competing events and causal goals MedRxiv 2021 10.1101/2021.06.01.21258142 Preprint. Online June 3 PMC10403306 37139580 · DOI

Cited by

Sorted by citations

Authors
Klee, M., Leist, A. K., Veldsman, M., Ranson, J. M., & Llewellyn, D. J.
Affiliations
  • University of Luxembourg (LU)
  • University of Oxford (GB)
  • University of Exeter (GB)
Corresponding author
Leist, Anja K.
Venue
American Journal of Preventive Medicine
Publisher
Elsevier BV
Published
2023-05
Volume
64
Issue
5
Pages
621-630
ISSN
0749-3797 (ISSN-L)
OpenAlex
W4323317127
Cited by
47
References
8
Citations by year
2026: 11 · 2025: 20 · 2024: 13 · 2023: 2 · 2014: 1
FWCI
9.37
Type
journal article
Language
en
Open access
Locations
  • American Journal of Preventive Medicine (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • Open Repository and Bibliography (University of Luxembourg) (submittedVersion, other-oa)
Funders
  • Wellcome Trust
  • National Institute for Health and Care Research
  • National Institute on Aging (RF1AG055654)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Social Sciences › Health › Health disparities and outcomes
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Keywords
dementia, socioeconomic deprivation, genetic risk, polygenic risk, area deprivation, Townsend deprivation index, white matter hyperintensities, dementia incidence, dementia prevention
MeSH terms
Dementia, Female, Humans, Income, Male, Risk Factors, Socioeconomic Factors, Cohort Studies

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