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A meta-analysis of loneliness and risk of dementia using longitudinal data from >600,000 individuals

Nature Mental Health volume 2, pages 1350–1361 (2024)Cite this article

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Abstract

Loneliness is one critical risk factor for cognitive health. Here we combined data from ongoing aging studies and the published literature and provide the largest meta-analysis on the association between loneliness and dementia (k = 21 samples, N = 608,561) and cognitive impairment (k = 16, N = 103,387). Loneliness increased the risk for all-cause dementia (hazard ratio (HR) 1.306, 95% confidence interval (CI) 1.197–1.426), Alzheimer’s disease (HR 1.393, 95% CI 1.290–1.504; k = 5), vascular dementia (HR 1.735, 95% CI 1.483–2.029; k = 3) and cognitive impairment (HR 1.150, 95% CI 1.113–1.189). The associations persisted when models controlled for depression, social isolation and/or other modifiable risk factors for dementia. The large heterogeneity across studies was partly due to differences in loneliness measures and ascertainment of cognitive status. The results underscore the importance to further examine the type or sources of loneliness and cognitive symptoms to develop effective interventions that reduce the risk of dementia.

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Fig. 1: Flow diagram for identifying eligible studies.
Fig. 2: Forest plot for all-cause dementia.
Fig. 3: Forest plot for AD and VaD.
Fig. 4: Forest plot for cognitive impairment.

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Data availability

The present study includes a coordinated analysis of data from eight public cohort studies: HRS, https://hrs.isr.umich.edu/data-products; ELSA, https://www.elsa-project.ac.uk/accessing-elsa-data; SHARE, https://share-eric.eu/data/data-access; TILDA, https://www.icpsr.umich.edu/web/ICPSR/series/726; MHAS, https://www.mhasweb.org/DataProducts/Home.aspx; KLoSA, https://survey.keis.or.kr/eng/myinfo/login.jsp; CHARLS, https://charls.charlsdata.com/pages/data/111/en.html; and HILDA, https://melbourneinstitute.unimelb.edu.au/hilda/for-data-users. Our access to the data does not allow for data redistribution. Individual researchers can access data from each of these studies after registration at each study data portal; we described each cohort in detail in the Supplementary Information. Data for the meta-analysis are available in the Open Science Framework repository for social sciences (https://doi.org/10.17605/OSF.IO/TFPHS).

Code availability

The R script that supports meta-analytic results is available via the Open Science Framework repository for social sciences at https://doi.org/10.17605/OSF.IO/TFPHS.

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Acknowledgements

The work reported in this publication was supported by the National Institute on Aging of the National Institutes of Health (grant nos. R01AG074573 and RF1AG053297 to A.R.S. and R01AG068093 to A.T.). The funders had no role in the study design, analysis, decision to publish or preparation of the manuscript. We further thank all participants, national and international agencies that support each cohort study analyzed in this work: HRS sponsored by the US National Institute on Aging (grant number NIA U01AG009740) and coordinated by the University of Michigan; ELSA sponsored by the US National Institute on Aging (grant number NIA R01AG017644) and the UK Government Departments coordinated by the National Institute for Health and Care Research; SHARE funded by the European Commission and Horizon 2020, and supported by the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the US National Institute on Aging and various national funding sources (www.share-project.org); TILDA based in Trinity College Dublin, for which data are hosted by the Irish Social Science Data Archive and the Inter-university Consortium for Political and Social Research based in the University of Michigan; MHAS supported by the US National Institute on Aging (grant no. NIA R01AG018016) with the collaborative effort from the University of Texas Medical Branch, the Instituto Nacional de Estadística y Geografía (Mexico), the University of Wisconsin, the Instituto Nacional de Geriatría (Mexico) and the Instituto Nacional de Salud Pública (Mexico); KLoSA, for which data are cured by the Korea Employment Information Service; CHARLS supported by the US National Institute on Aging (grant number NIA R01AG037031), the Natural Science Foundation of China, the World Bank and Peking University; HILDA, funded by Australian Government Department of Social Services and managed by the Melbourne Institute of Applied Economic and Social Research. For each study, data were collected with the informed consent of participants. All procedures, materials and participant compensations were approved by the institutional review boards of their respective institutions.

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Authors and Affiliations

  1. Department of Behavioral Sciences and Social Medicine, Florida State University College of Medicine, Tallahassee, FL, USA

    Martina Luchetti, Amanda A. Sesker & Angelina R. Sutin

  2. Department of Geriatrics, Florida State University College of Medicine, Tallahassee, FL, USA

    Damaris Aschwanden & Antonio Terracciano

  3. Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Geneva, Switzerland

    Damaris Aschwanden

  4. Department of Psychology, School of Mental Health, Institute of Aging, Key Laboratory of Alzheimer’s Disease of Zhejiang Province, and Zhejiang Provincial Clinical Research Center for Mental Disorders, The Affiliated Kangning Hospital, Wenzhou Medical University, Wenzhou, China

    Xianghe Zhu

  5. Zhejiang Lab for Regenerative Medicine Vision and Brain Health, Oujiang Laboratory, Wenzhou, China

    Xianghe Zhu

  6. Department of Psychology, University of Limerick, Limerick, Ireland

    Páraic S. O’Súilleabháin

  7. Health Research Institute, University of Limerick, Limerick, Ireland

    Páraic S. O’Súilleabháin

  8. Euromov, University of Montpellier, Montpellier, France

    Yannick Stephan

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M.L. and A.R.S. conceived the project. M.L. and D.A. carried out the literature review and selection of published articles with supervision from A.R.S. M.L. performed all analyses with input from D.A., A.T. and A.R.S. M.L. drafted the first version of the manuscript. D.A., A.A.S., X.Z., P.S.O., Y.S., A.T. and A.R.S. provided inputs on the interpretation of results and critical revisions to the manuscript. All authors approved the final version of the manuscript.

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Correspondence to Martina Luchetti.

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Nature Mental Health thanks Patrick Lao, Eduardo Zimmer and the other, anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Forest plot of fully adjusted models.

The figure shows supplementary analyses with studies that accounted for depression, social isolation, and/or modifiable clinical factors for dementia, panel a (18 samples, 559,890 participants) and cognitive impairment, panel b (16 samples, 81,709 participants). Effect sizes for the individual studies and average random-effects (RE) model are displayed in hazard ratios (HR) with 95% confidence intervals (95% CI). Circle sizes and lines represent the weight of each study and 95% CI limits. Upper-level 95% CIs that exceeded 4.0 are not shown. Study abbreviations: H70 Study = Gothenberg H70 Birth Cohort Study; LEILA = Leipzig Longitudinal Study of the Aged; LRGSTUA = Neuroprotective Model for Healthy Longevity among Malaysian Older Adults Towards Using Ageing; RS = Rotterdam Study; SNAC-K = Swedish National Study on Aging and Care in Kungsholmen; Zhou (2019) included results for male and female (M / F) individuals. Selected cohorts: HRS = Health and Retirement Study; ELSA = English Longitudinal Study of Ageing; SHARE = Study of Health, Ageing and Retirement in Europe; TILDA = The Irish Longitudinal Study on Ageing; MHAS = Mexican Health and Aging Study; KLoSA = Korean Longitudinal Study of Aging; CHARLS = China Health and Retirement Longitudinal Study; and HILDA = Household, Income and Labour Dynamics in Australia. Data Extraction File includes the complete list of control variables within each study: https://osf.io/tfphs/.

Extended Data Fig. 2 Funnel plots to evaluate publication bias.

The figure shows funnel plots for the meta-analysis concerning risk of dementia (panel a) and cognitive impairment (panel b). It represents log-transformed hazard ratios and standard errors used in R metafor to run the analysis. The close dots indicate the observed studies and the open dots indicate the missing studies imputed by the trill-and-fill method.

Extended Data Table 1 Supplemental analyses from the individual cohort studies
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Extended Data Table 2 Breakdown of the literature searches
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Extended Data Table 3 Summary of the selected articles
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Extended Data Table 4 Summary of the selected articles (continued)
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Extended Data Table 5 Leave-one-out sensitivity analyses
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Extended Data Table 6 Quality Assessment
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Supplementary information

Supplementary Information

Supplementary Table 1. Preregistration deviations. Supplementary Note 1. Description of the individual cohort studies. Supplementary Table 2. Loneliness measures across individual cohort studies. Supplementary Table 3. Classification of cognitive status across individual cohort studies. Supplementary Table 4. Selected control variables across individual cohort studies. Supplementary Table 5. MOOSE checklist for meta-analyses of observational studies. Supplementary Table 6. Final decision and reasons for exclusion of the screened full-text articles. Supplementary Note 2. Supplementary analysis to evaluate publication bias.

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Luchetti, M., Aschwanden, D., Sesker, A.A. et al. A meta-analysis of loneliness and risk of dementia using longitudinal data from >600,000 individuals. Nat. Mental Health 2, 1350–1361 (2024). https://doi.org/10.1038/s44220-024-00328-9

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