Exploration of Medicine

Table 3. Principal component loadings of the eleven social network features.

Social network features	PC1	PC2
Someone shows love	–0.454	0.093
How many relatives	–0.538	0.005
Someone gives emotional support	–0.344	0.14
Participate in any groups	–0.491	0.177
How many friends see a month	–0.15	–0.741
How often do you go to religious meetings	–0.17	–0.602
As much contact as you like	–0.175	0.055
How many friends	–0.174	0.009
How many relatives see a month	–0.077	0.143
Someone to give you advice	–0.095	0.064
Someone to listen to you	–0.134	0.033

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The supplementary materials for this article are available at: https://www.explorationpub.com/uploads/Article/file/1001436_sup_1.pdf.

Declarations

Acknowledgments

During the preparation of this work, the authors used ChatGPT-5.6 to improve language and grammar. After using the tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Author contributions

TL: Data curation, Formal analysis, Methodology, Visualization, Writing—original draft, Writing—review & editing. HD: Conceptualization, Methodology, Writing—review & editing. PHH: Conceptualization, Methodology, Writing—review & editing, Supervision, Investigation. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

The study protocol was approved by the Boston University Medical Campus Institutional Review Board (IRB; approval number H-43332). The study was conducted in accordance with the Declaration of Helsinki.

Consent to participate

Written informed consent was obtained from all participants prior to enrollment in FHS.

Consent to publication

Informed consent to publication was obtained from relevant participants.

Availability of data and materials

The datasets analyzed in this study were obtained from the Framingham Heart Study and are not publicly available due to data use agreements and participant privacy protections. Requests for access to these datasets should be directed to the Framingham Heart Study through https://www.framinghamheartstudy.org/fhs-for-researchers/.

Funding

Support for this work came from the Alzheimer’s Association (grant number [23AARF-1019048]) and from the National Institute on Aging (grant number [U19AG068753]), as well as the Framingham Heart Study of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. This project has been funded in whole or in part with Federal funds from the National Heart Lung and Blood Institute, Department of Health and Human Services, under Contract No. [75N92019D00031]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.

References

Hou Y, Dan X, Babbar M, Wei Y, Hasselbalch SG, Croteau DL, et al. Ageing as a risk factor for neurodegenerative disease. Nat Rev Neurol. 2019;15:565–81. [DOI] [PubMed]

Lee J, Kim HJ. Normal Aging Induces Changes in the Brain and Neurodegeneration Progress: Review of the Structural, Biochemical, Metabolic, Cellular, and Molecular Changes. Front Aging Neurosci. 2022;14:931536. [DOI] [PubMed] [PMC]

McMurran CE, Wang Y, Mak JKL, Karlsson IK, Tang B, Ploner A, et al. Advanced biological ageing predicts future risk for neurological diagnoses and clinical examination findings. Brain. 2023;146:4891–902. [DOI] [PubMed] [PMC]

2025 Alzheimer’s disease facts and figures. Alzheimers Dement. 2025;21:e70235. [DOI] [PMC]

Stern Y, Barnes CA, Grady C, Jones RN, Raz N. Brain reserve, cognitive reserve, compensation, and maintenance: operationalization, validity, and mechanisms of cognitive resilience. Neurobiol Aging. 2019;83:124–9. [DOI] [PubMed] [PMC]

Stern Y, Arenaza-Urquijo EM, Bartrés-Faz D, Belleville S, Cantilon M, Chetelat G, et al. Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimers Dement. 2020;16:1305–11. [DOI] [PubMed] [PMC]

Jia B, Xu Y, Zhu X. Cognitive resilience in Alzheimer’s disease: Mechanism and potential clinical intervention. Ageing Res Rev. 2025;106:102711. [DOI] [PubMed]

Arenaza-Urquijo EM, Boyle R, Casaletto K, Anstey KJ, Vila-Castelar C, Colverson A, et al. Sex and gender differences in cognitive resilience to aging and Alzheimer’s disease. Alzheimers Dement. 2024;20:5695–719. [DOI] [PubMed] [PMC]

Ahangari N, Fischer CE, Schweizer TA, Munoz DG. Cognitive resilience and severe Alzheimer’s disease neuropathology. Aging Brain. 2023;3:100065. [DOI] [PubMed] [PMC]

10.

Joshi MS, Galvin JE. Cognitive Resilience in Brain Health and Dementia Research. J Alzheimers Dis. 2022;90:461–73. [DOI] [PubMed] [PMC]

11.

Mathys H, Peng Z, Boix CA, Victor MB, Leary N, Babu S, et al. Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer’s disease pathology. Cell. 2023;186:4365–85.e27. [DOI] [PubMed] [PMC]

12.

Penninkilampi R, Casey AN, Singh MF, Brodaty H. The Association between Social Engagement, Loneliness, and Risk of Dementia: A Systematic Review and Meta-Analysis. J Alzheimers Dis. 2018;66:1619–33. [DOI] [PubMed]

13.

Stoykova R, Matharan F, Dartigues JF, Amieva H. Impact of social network on cognitive performances and age-related cognitive decline across a 20-year follow-up. Int Psychogeriatr. 2011;23:1405–12. [DOI] [PubMed]

14.

Samtani S, Stafford J, Szcześniak D, Brodaty H, Davis D, Ikram MA, et al. How does social health impact cognitive function and brain reserve? Findings from the SHARED Consortium. Ageing Res Rev. 2026;118:103118. [DOI] [PubMed]

15.

Liu Y, Lu G, Liu L, He Y, Gong W. Cognitive reserve over the life course and risk of dementia: a systematic review and meta-analysis. Front Aging Neurosci. 2024;16:1358992. [DOI] [PubMed] [PMC]

16.

Laininger L, Dietzel N, Graessel E, Prokosch HU, Kolominsky-Rabas PL. Impact of social network composition on cognitive decline: Digital Dementia Registry Bavaria (digiDEM Bayern). PLOS ONE. 2024;19:e0306447. [DOI] [PubMed] [PMC]

17.

Hu YS, Xu P, Pigino G, Brady ST, Larson J, Lazarov O. Complex environment experience rescues impaired neurogenesis, enhances synaptic plasticity, and attenuates neuropathology in familial Alzheimer’s disease-linked APPswe/PS1ΔE9 mice. FASEB J. 2010;24:1667–81. [DOI] [PubMed] [PMC]

18.

Lazarov O, Robinson J, Tang YP, Hairston IS, Korade-Mirnics Z, Lee VM, et al. Environmental Enrichment Reduces Aβ Levels and Amyloid Deposition in Transgenic Mice. Cell. 2005;120:701–13. [DOI] [PubMed]

19.

Perry BL, Roth AR, Peng S, Risacher SL, Saykin AJ, Apostolova LG. Social Networks and Cognitive Reserve: Network Structure Moderates the Association Between Amygdalar Volume and Cognitive Outcomes. J Gerontol: B. 2021;77:1490–500. [DOI] [PubMed] [PMC]

20.

Noonan MP, Mars RB, Sallet J, Dunbar RIM, Fellows LK. The structural and functional brain networks that support human social networks. Behav Brain Res. 2018;355:12–23. [DOI] [PubMed] [PMC]

21.

Hirabayashi N, Honda T, Hata J, Furuta Y, Shibata M, Ohara T, et al. Association Between Frequency of Social Contact and Brain Atrophy in Community-Dwelling Older People Without Dementia. Neurology. 2023;101:e1108–17. [DOI] [PubMed]

22.

Duffner LA, DeJong NR, Jansen JFA, Backes WH, de Vugt M, Deckers K, et al. Associations between social health factors, cognitive activity and neurostructural markers for brain health – A systematic literature review and meta-analysis. Ageing Res Rev. 2023;89:101986. [DOI] [PubMed]

23.

Berkman LF. Social Support, Social Networks, Social Cohesion and Health. Soc Work Health Care. 2000;31:3–14. [DOI] [PubMed]

24.

Siette J, Pomare C, Dodds L, Jorgensen M, Harrigan N, Georgiou A. A comprehensive overview of social network measures for older adults: A systematic review. Arch Gerontol Geriatr. 2021;97:104525. [DOI] [PubMed]

25.

Xia Y, Wang J, Yang P, Yu S, Tang Q, Yu M, et al. The Impact of Social Support and Resilience on Cognitive Function in Older Adults With Mild Cognitive Impairment: A Cross-Sectional Study. Public Health Nurs. 2025;42:1722–31. [DOI] [PubMed]

26.

Zhang Y, Wu Y, Li Y. Sex differences in the mediating effect of resilience on social support and cognitive function in older adults. Geriatr Nurs. 2023;53:50–6. [DOI] [PubMed]

27.

Jackson KL, Luo J, Willroth EC, Ong AD, James BD, Bennett DA, et al. Associations Between Loneliness and Cognitive Resilience to Neuropathology in Older Adults. J Gerontol: B. 2023;78:939–47. [DOI] [PubMed] [PMC]

28.

Cohn-Schwartz E, Levinsky M, Litwin H. Social network type and subsequent cognitive health among older Europeans. Int Psychogeriatr. 2021;33:495–504. [DOI] [PubMed] [PMC]

29.

Kim YB, Lee SH. Social network types and cognitive decline among older Korean adults: A longitudinal population-based study. Int J Geriatr Psychiatry. 2019;34:1845–54. [DOI] [PubMed]

30.

Oh SS, Kang B, Hong D, Kim JI, Jeong H, Song J, et al. A Multivariable Prediction Model for Mild Cognitive Impairment and Dementia: Algorithm Development and Validation. JMIR Med Inform. 2024;12:e59396. [DOI] [PubMed] [PMC]

31.

Ahmadzadeh M, Cosco TD, Best JR, Christie GJ, DiPaola S. Predictors of the rate of cognitive decline in older adults using machine learning. PLOS ONE. 2023;18:e0280029. [DOI] [PubMed] [PMC]

32.

Berkman LF, Syme SL. Social networks, host resistance, and mortality: a nine-year follow-up study of Alameda County residents. In: Steptoe A, Wardle J, editors. Psychosocial Processes and Health: A Reader. Cambridge: Cambridge University Press; 1994. pp. 43–67. [DOI]

33.

Mahmood SS, Levy D, Vasan RS, Wang TJ. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet (Lond Engl). 2014;383:999–1008. [DOI] [PubMed] [PMC]

34.

Feinleib M, Kannel WB, Garrison RJ, McNamara PM, Castelli WP. The Framingham Offspring Study. Design and preliminary data. Prev med. 1975;4:518–25. [PubMed]

35.

United Nations. World population ageing 2013. New York: United Nations; 2014. [DOI]

36.

Kravitz E, Schmeidler J, Beeri MS. Cognitive Decline and Dementia in the Oldest-Old. Rambam Maimonides Med J. 2012;3:e0026. [DOI] [PubMed] [PMC]

37.

D’Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General Cardiovascular Risk Profile for Use in Primary Care. Circulation. 2008;117:743–53. [DOI] [PubMed]

38.

Aslan E, Özüpak Y. Comparison of machine learning algorithms for automatic prediction of Alzheimer disease. J Chin Med Assoc. 2024;88:98–107. [DOI] [PubMed] [PMC]

39.

Peng S, Roth AR, Apostolova LG, Saykin AJ, Perry BL. Cognitively stimulating environments and cognitive reserve: the case of personal social networks. Neurobiol Aging. 2022;112:197–203. [DOI] [PubMed] [PMC]

40.

Sun H, Perry B. Factors related to cognitive reserve: a comparative analysis of education, occupation, lifestyle, and social network factors based on brain measures. J Gerontol B: Psychol Sci Soc Sci. 2026;81:gbag059. [DOI] [PubMed] [PMC]

41.

Morganti F, Negri I. How Cognitive Reserve Could Protect from Dementia? An Analysis of Everyday Activities and Social Behaviors During Lifespan. Brain Sci. 2025;15:652. [DOI] [PubMed] [PMC]

42.

Marseglia A, Kalpouzos G, Laukka EJ, Maddock J, Patalay P, Wang HX, et al. Social Health and Cognitive Change in Old Age: Role of Brain Reserve. Ann Neurol. 2023;93:844–55. [DOI] [PubMed]

43.

Dekhtyar S, Marseglia A, Xu W, Darin-Mattsson A, Wang HX, Fratiglioni L. Genetic risk of dementia mitigated by cognitive reserve: A cohort study. Ann Neurol. 2019;86:68–78. [DOI] [PubMed] [PMC]

44.

Kelly ME, Duff H, Kelly S, McHugh Power JE, Brennan S, Lawlor BA, et al. The impact of social activities, social networks, social support and social relationships on the cognitive functioning of healthy older adults: a systematic review. Syst Rev. 2017;6:259. [DOI] [PubMed] [PMC]

45.

Yu K, Van Bogart K, Kang JE, Taylor HO, Harrington KD, Silbert LC, et al. How loneliness and social isolation are linked to cognitive decline among older adults? A systematic review of underlying physiological and psychobehavioral mechanisms. Alzheimers Dement. 2026;22:e71585. [DOI] [PubMed] [PMC]

46.

Cacioppo JT, Cacioppo S, Capitanio JP, Cole SW. The Neuroendocrinology of Social Isolation. Annu Rev Psychol. 2015;66:733–67. [DOI] [PubMed] [PMC]

47.

Aslan E, Özüpak Y. A hybrid SE-ResNet50 deep learning framework for high-accuracy and explainable cotton leaf disease classification. BMC Plant Biol. 2026;26:1244. [DOI] [PubMed] [PMC]

48.

Alpsalaz SD, Aslan E, Özüpak Y, Alpsalaz F, Uzel H, Zaitsev I. Explainability-Aligned Reliability-Weighted Fuzzy Ensemble for Automated Cervical Cancer Classification. Int J Intell Syst. 2026;2026:2931556. [DOI]

49.

Aslan E. Prediction and comparative analysis of emissions from gas turbines using random search optimization and different machine learning-based algorithms. Bull Pol Acad Sci Tech Sci. 2024;72:e151956. [DOI]

50.

James C, Ranson JM, Everson R, Llewellyn DJ. Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients. JAMA Netw Open. 2021;4:e2136553. [DOI] [PubMed] [PMC]

51.

Javeed A, Dallora AL, Berglund JS, Ali A, Ali L, Anderberg P. Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. J Med Syst. 2023;47:17. [DOI] [PubMed] [PMC]

52.

Aslan E, Demirtas Alpsalaz S, Özüpak Y, Alpsalaz F, Uzel H. Alzheimer’s Classification with a MaxViT-Based Deep Learning Model Using Magnetic Resonance Imaging. J Appl Sci Technol Trends. 2025;6:316–27. [DOI]

53.

Sohn M, Yang J, Lee JH, Choi D. Predictive factors for dementia among older adults in South Korea: an interpretable machine learning analysis. Alzheimers Res Ther. 2025;17:246. [DOI] [PubMed] [PMC]

54.

Alam A, Rabbani MG, Prybutok VR. Artificial Intelligence–Enabled Dementia Risk Prediction for Smart and Sustainable Healthcare: An Interpretable Machine Learning Study Using NHATS. Appl Sci. 2026;16:2180. [DOI]

55.

Windsor TD, Rioseco P, Fiori KL, Curtis RG, Booth H. Structural and functional social network attributes moderate the association of self-rated health with mental health in midlife and older adults. Int Psychogeriatr. 2016;28:49–61. [DOI] [PubMed]

56.

Umberson D, Montez JK. Social Relationships and Health: A Flashpoint for Health Policy. J Health Soc Behav. 2010;51:S54–66. [DOI] [PubMed] [PMC]

57.

Meister LM, Zahodne LB. Associations between social network components and cognitive domains in older adults. Psychol Aging. 2022;37:591–603. [DOI] [PubMed] [PMC]

58.

Calandri IL, Ardohain CA, Elgani SA, Seminara E, Hernandez MA, Ossenkoppele R, et al. Personal social network strengthens adherence to lifestyle changes in individuals with subjective cognitive decline. Alzheimers Dement (Amst). 2024;16:e12538. [DOI] [PubMed] [PMC]

59.

Marino FR, Lyu C, Li Y, Liu T, Au R, Hwang PH. Physical Activity Over the Adult Life Course and Risk of Dementia in the Framingham Heart Study. JAMA Netw Open. 2025;8:e2544439. [DOI] [PubMed] [PMC]