Subjective Cognitive Impairment (SCI) and Future Dementia Risk in the National Health and Aging Trends Study (NHATS) During 2012

Abstract

Background

Subjective cognitive impairment (SCI), assessed in national surveys, offers potential for dementia monitoring and early detection. However, its causal link to dementia risk remains unclear.

Objective

To evaluate whether SCI causally affects dementia risk in U.S. older adults (≥65 years), considering mortality as a competing risk.

Methods

Using data from 1622 dementia-free older adults in the National Health and Aging Trends Study (NHATS) during 2011–2019, we estimated total, direct, and separable effects of SCI on dementia and mortality risks.

Results

SCI was reported by 7.6% at baseline and associated with a twofold increased dementia risk over 8 years (RR: 1.95, 95% CI: 1.07–3.07) and lower mortality risk (RR: 0.31, 95% CI: 0.10–0.67). Direct effect analysis indicated a potential direct causal link between SCI and dementia.

Conclusions

SCI predicts dementia onset and inversely affects mortality, highlighting the importance of early detection and precise analytic approaches.

Keywords

subjective cognitive impairment probable dementia Alzheimer’s disease and related dementias competing risks NHATS

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References

Adams

M. L.

Grandpre

Katz

D. L.

Shenson

(2020). Cognitive impairment and cardiovascular disease: A comparison of risk factors, disability, quality of life, and access to health care. Public Health Reports, 135(1), 132–140. https://doi.org/10.1177/0033354919893030

Alzheimer's Disease Association . (2023). 2023 Alzheimer's disease facts and figures. Alzheimer's and Dementia, 19(4), 1598–1695. https://doi.org/10.1002/alz.13016

Andersen

P. K.

Abildstrom

S. Z.

Rosthøj

(2002). Competing risks as a multi-state model. Statistical Methods in Medical Research, 11(2), 203–215. https://doi.org/10.1191/0962280202sm281ra

Andersen

P. K.

Geskus

R. B.

de Witte

Putter

(2012). Competing risks in epidemiology: Possibilities and pitfalls. International Journal of Epidemiology, 41(3), 861–870. https://doi.org/10.1093/ije/dyr213

Austin

P. C.

Lee

D. S.

Fine

J. P.

(2016). Introduction to the analysis of survival data in the presence of competing risks. Circulation, 133(6), 601–609. https://doi.org/10.1161/circulationaha.115.017719

Chapman

Rentería

M. A.

Dworkin

J. D.

Garriga

S. M.

Barker

M. S.

Avila-Rieger

Gonzalez

Joyce

J. L.

Vonk

J. M. J.

Soto

Manly

J. J.

Brickman

A. M.

Mayeux

R. P.

Cosentino

S. A.

(2023). Association of subjective cognitive decline with progression to dementia in a cognitively unimpaired multiracial community sample. Neurology, 100(10), e1020–e1027. https://doi.org/10.1212/wnl.0000000000201658

Chiang

C. L.

(1961). A stochastic study of the life table and its applications. III. The follow-up study with the consideration of competing risks. Biometrics, 17(1), 57–78. https://doi.org/10.2307/2527496

Chyr

L. C.

Wolff

J. L.

Zissimopoulos

J. M.

Drabo

E. F.

(2024). Analysis of agreement between measures of subjective cognitive impairment and probable dementia in the National Health and Aging Trends Study. Alzheimer's and Dementia, 20(4), 2817–2829. https://doi.org/10.1002/alz.13758

Cole

S. R.

Hernán

M. A.

(2008). Constructing inverse probability weights for marginal structural models. American Journal of Epidemiology, 168(6), 656–664. https://doi.org/10.1093/aje/kwn164

10.

Crimmins

E. M.

Kim

J. K.

Langa

K. M.

Weir

D. R.

(2011). Assessment of cognition using surveys and neuropsychological assessment: The health and retirement study and the aging, demographics, and memory study. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 66(Suppl 1), i162–i171. https://doi.org/10.1093/geronb/gbr048

11.

D'Elia

Whitfield

Schlosser

Lutz

Barnhofer

Chételat

Marchant

N. L.

Gonneaud

Klimecki

(2024). Impact of mindfulness-based and health self-management interventions on mindfulness, self-compassion, and physical activity in older adults with subjective cognitive decline: A secondary analysis of the SCD-well randomized controlled trial. Alzheimer's and Dementia, 16(1), Article e12558. https://doi.org/10.1002/dad2.12558

12.

Efron

(1967). The two sample problem with censored data. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 5(1), 831–853. https://doi.org/10.1525/9780520313903

13.

Fine

J. P.

Gray

R. J.

(1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509. https://doi.org/10.2307/2670170

14.

Fine

J. P.

Jiang

Chappell

(2001). On semi-competing risks data. Biometrika, 88(4), 907–919. https://www.jstor.org/stable/2673691

15.

Freedman

V. A.

Kasper

J. D.

(2019). Cohort profile: The national health and aging trends study (NHATS). International Journal of Epidemiology, 48(4), 1044–1045. https://doi.org/10.1093/ije/dyz109

16.

Geskus

R. B.

(2016). Data analysis with competing risks and intermediate states (1st ed.). Chapman & Hall/CRC; Taylor & Francis.

17.

Hernán

M. A.

(2010). The hazards of hazard ratios. Epidemiology, 21(1), 13–15. https://doi.org/10.1097/EDE.0b013e3181c1ea43

18.

Hernán

M. A.

Robins

J. M.

(2023). Causal inference: What if (1st ed.). : Chapman & Hall/CRC; Taylor & Francis.

19.

Howe

C. J.

Cole

S. R.

Lau

Napravnik

Joseph

J. E.

(2016). Selection bias due to loss to follow up in cohort studies. Epidemiology, 27(1), 91–97. https://www.jstor.org/stable/26511888

20.

Iso-Markku

Aaltonen

Kujala

U. M.

Halme

H.-L.

Phipps

Knittle

Vuoksimaa

Waller

(2024). Physical activity and cognitive decline among older adults: A systematic review and meta-analysis. JAMA Network Open, 7(2), e2354285. https://doi.org/10.1001/jamanetworkopen.2023.54285

21.

Jack

C. R.

Jr. Andrews

J. S.

Beach

T. G.

Buracchio

Dunn

Graf

Hansson

Jagust

McDade

Molinuevo

J. L.

Okonkwo

O. C.

Pani

Rafii

M. S.

Scheltens

Siemers

Snyder

H. M.

Sperling

Teunissen

C. E.

Carrillo

M. C.

(2024). Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup. Alzheimer's and Dementia, 20(8), 5143–5169. https://doi.org/10.1002/alz.13859

22.

Jacobson

Thunell

Zissimopoulos

(2020). Cognitive assessment at Medicare's annual wellness visit in fee-for-service and Medicare advantage plans. Health Affairs, 39(11), 1935–1942. https://doi.org/10.1377/hlthaff.2019.01795

23.

Jessen

Amariglio

R. E.

van Boxtel

Breteler

Ceccaldi

Chételat

Dubois

Dufouil

Ellis

K. A.

van der Flier

W. M.

Glodzik

van Harten

A. C.

de Leon

M. J.

McHugh

Mielke

M. M.

Molinuevo

J. L.

Mosconi

Osorio

R. S.

Perrotin

Wagner

(2014). A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimer's and Dementia, 10(6), 844–852. https://doi.org/10.1016/j.jalz.2014.01.001

24.

Kang

Mahajan

Spat-Lemus

Durape

Chen

Gurnani

A. S.

Devine

Auerbach

S. H.

Ang

T. F. A.

Sherva

Qiu

W. Q.

Lunetta

K. L.

Farrer

L. A.

Mez

(2024). Subjective cognitive decline plus and longitudinal assessment and risk for cognitive impairment. JAMA Psychiatry.

25.

Kasper

J. D.

Freedman

V. A.

(2014). Findings from the 1st round of the national health and aging trends study (NHATS): Introduction to a special issue. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 69(Suppl_1), S1–S7. https://doi.org/10.1093/geronb/gbu125

26.

Kasper

J. D.

Freedman

V. A.

(2020). National health and aging trends study user guide: Rounds 1-9 final release. Johns Hopkins University School of Public Health.

27.

Kasper

J. D.

Freedman

V. A.

Spillman

(2013) Classification of persons by dementia status in the National Health and Aging Trends Study (5, pp. 1–4). Johns Hopkins University School of Public Health. Technical paper .

28.

Langa

K. M.

Plassman

B. L.

Wallace

R. B.

Herzog

A. R.

Heeringa

S. G.

Ofstedal

M. B.

Burke

J. R.

Fisher

G. G.

Fultz

N. H.

Hurd

M. D.

Potter

G. G.

Rodgers

W. L.

Steffens

D. C.

Weir

D. R.

Willis

R. J.

(2005). The aging, demographics, and memory study: Study design and methods. Neuroepidemiology, 25(4), 181–191. https://doi.org/10.1159/000087448

29.

Palmqvist

Tideman

Mattsson-Carlgren

Schindler

S. E.

Smith

Ossenkoppele

Calling

West

Monane

Verghese

P. B.

Braunstein

J. B.

Blennow

Janelidze

Stomrud

Salvadó

Hansson

(2024). Blood biomarkers to detect Alzheimer disease in primary care and secondary care. JAMA.

30.

Pearl

(1995). Causal diagrams for empirical research. Biometrika, 82(4), 669–688. https://doi.org/10.2307/2337329

31.

Pepe

M. S.

Mori

(1993). Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? Statistics in Medicine, 12(8), 737–751. https://doi.org/10.1002/sim.4780120803

32.

Petersen

R. C.

Wiste

H. J.

Weigand

S. D.

Fields

J. A.

Geda

Y. E.

Graff-Radford

Knopman

D. S.

Kremers

W. K.

Lowe

Machulda

M. M.

Mielke

M. M.

Stricker

N. H.

Therneau

T. M.

Vemuri

Jack

C. R.

Jr. (2021). NIA-AA Alzheimer's disease framework: Clinical characterization of stages. Annals of Neurology, 89(6), 1145–1156. https://doi.org/10.1002/ana.26071

33.

Pike

K. E.

Cavuoto

M. G.

Wright

B. J.

Kinsella

G. J.

(2022). Subjective cognitive decline: Level of risk for future dementia and mild cognitive impairment, a meta-analysis of longitudinal studies. Neuropsychology Review, 32(4), 703–735. https://doi.org/10.1007/s11065-021-09522-3

34.

Reisberg

Shulman

M. B.

Torossian

Leng

Zhu

(2010). Outcome over seven years of healthy adults with and without subjective cognitive impairment. Alzheimer's and Dementia, 6(1), 11–24. https://doi.org/10.1016/j.jalz.2009.10.002

35.

Ritchie

Touchon

(2000). Mild cognitive impairment: Conceptual basis and current nosological status. Lancet, 355(9199), 225–228. https://doi.org/10.1016/s0140-6736(99)06155-3

36.

Robins

J. M.

Hernán

M. A.

Brumback

(2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550–560. https://doi.org/10.1097/00001648-200009000-00011

37.

Roehr

Luck

Heser

Fuchs

Ernst

Wiese

Werle

Bickel

Brettschneider

Koppara

Pentzek

Lange

Prokein

Weyerer

Mösch

König

H. H.

Maier

Scherer

Jessen

Riedel-Heller

S. G.

(2016). Incident subjective cognitive decline does not predict mortality in the elderly—results from the longitudinal German study on ageing, cognition, and dementia (AgeCoDe). PLoS One, 11(1), e0147050. https://doi.org/10.1371/journal.pone.0147050

38.

Rojas-Saunero

L. P.

Young

J. G.

Didelez

Ikram

M. A.

Swanson

S. A.

(2023). Considering questions before methods in dementia research with competing events and causal goals. American Journal of Epidemiology, 192(8), 1415–1423. https://doi.org/10.1093/aje/kwad090

39.

Rutherford

M. J.

Andersson

T. M.

Myklebust

Møller

Lambert

P. C.

(2022). Non-parametric estimation of reference adjusted, standardised probabilities of all-cause death and death due to cancer for population group comparisons. BMC Medical Research Methodology, 22(1), 2. https://doi.org/10.1186/s12874-021-01465-w

40.

Schober

Vetter

T. R.

(2018). Survival analysis and interpretation of time-to-event data: The tortoise and the hare. Anesthesia & Analgesia, 127(3), 792–798. https://doi.org/10.1213/ane.0000000000003653

41.

Schretlen

Testa

Pealson

(2010). Clock-drawing test scoring approach from the calibrated neuropsychological normative system. Psychological Assessment Resources.

42.

Schwarz

Lange

Benson

G. S.

Horn

Wurdack

Lukas

Buchert

Wirth

Flöel

(2021). Severity of subjective cognitive complaints and worries in older adults are associated with cerebral amyloid-β load. Frontiers in Aging Neuroscience, 13(1), 675583. https://doi.org/10.3389/fnagi.2021.675583

43.

Stensrud

M. J.

Hernán

M. A.

Tchetgen Tchetgen

E. J.

Robins

J. M.

Didelez

Young

J. G.

(2021). A generalized theory of separable effects in competing event settings. Lifetime Data Analysis, 27(4), 588–631. https://doi.org/10.1007/s10985-021-09530-8

44.

Stensrud

M. J.

Young

J. G.

Didelez

Robins

J. M.

Hernán

M. A.

(2022). Separable effects for causal inference in the presence of competing events. Journal of the American Statistical Association, 117(537), 175–183. https://doi.org/10.1080/01621459.2020.1765783

45.

Taylor

C. A.

Bouldin

E. D.

McGuire

L. C.

(2018). Subjective cognitive decline among adults aged ≥45 years – United States, 2015-2016. Morbidity and Mortality Weekly Report, 67(27), 753–757. https://doi.org/10.15585/mmwr.mm6727a1

46.

Tsiatis

(1975). A nonidentifiability aspect of the problem of competing risks. Proceedings of the National Academy of Sciences, 72(1), 20–22.

47.

Turner

J. R.

Hill

N. L.

Brautigam

Bhargava

Mogle

(2023). How does exposure to dementia relate to subjective cognition? A systematic review. Innovation in Aging, 7(6), 2. https://doi.org/10.1093/geroni/igad056

48.

Xiao

Moodie

E. E. M.

Abrahamowicz

(2013). Comparison of approaches to weight truncation for marginal structural Cox models. Epidemiologic Methods, 2(1), 1–20. https://doi.org/10.1515/em-2012-0006

49.

Young

J. G.

Stensrud

M. J.

Tchetgen Tchetgen

E. J.

Hernán

M. A.

(2020). A causal framework for classical statistical estimands in failure-time settings with competing events. Statistics in Medicine, 39(8), 1199–1236. https://doi.org/10.1002/sim.8471

Subjective Cognitive Impairment (SCI) and Future Dementia Risk in the National Health and Aging Trends Study (NHATS) During 2012–2019

Abstract

Background

Objective

Methods

Results

Conclusions

Keywords

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