Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The cross-sectional and longitudinal relationship of diabetic retinopathy to cognitive impairment: a systematic review and meta-analysis

Abstract

Objectives

To establish a potential relationship between diabetic retinopathy (DR) and different stages of cognitive impairment

Methods

Literature searches were conducted on PubMed and EMBASE, with keywords “diabetic retinopathy” and “cognitive impairment”. Inclusion criteria were original human studies, and English language. Quality of studies was assessed by the Newcastle-Ottawa Quality Assessment (NOSGEN). The register number of this study on the International Prospective Register of Systematic Reviews (PROSPERO) is CRD42021236747. The main outcome measures were odds ratios (OR) and risk ratios (RR) for cross-sectional and longitudinal studies, respectively. Meta-regression was performed to evaluate the effects of potential moderator variables, including, age, onset age of diabetes mellitus (DM), duration of DM, and HbA1c.

Results

Twenty-five studies (17 cross-sectional and 8 longitudinal studies) with a total of 1,963,914 subjects, were included. Among the cross-sectional studies, the pooled ORs of any cognitive impairment, early stage of cognitive impairment and dementia in subjects with DR (95% confidence interval) were 1.48 (1.08–2.02), 1.59 (1.01–2.51), and 1.13 (0.86–1.50), respectively. Among the longitudinal studies, the pooled RRs of any cognitive impairment, early stage of cognitive impairment, and dementia in subjects with DR (95% confidence interval) were 1.35 (1.12–1.65), 1.50 (1.06–2.12), and 1.31 (1.03–1.66), respectively. Meta-regression showed age, onset age of DM, duration of DM, and glycated hemoglobin (HbA1c) were not statistically associated with the outcomes.

Conclusions

The presence of DR in DM patients indicates both higher odds of prevalent cognitive impairment and escalated risks of developing cognitive impairment in the future.

摘要

目的

建立糖尿病视网膜病变(DR)与不同阶段认知障碍之间的潜在关联。

方法

PubMde 和 EMBASE数据库中使用“糖尿病视网膜病变”和“认知障碍”两个关键词进行文献检索。纳入标准为临床研究和英文文献, 我们通过Newcastle-Ottawa质量评估(NOSGEN)方法评估研究质量。本研究在国际前瞻性系统性综述登记研究(PROSPERO)中的登记研究编号为CRD42021236747。比值比 (OR) 和风险比 (RR) 分别是横断面研究和纵向研究的主要结局指标。荟萃-回归分析用以评估潜在调节变量带来的影响, 包括年龄、糖尿病(DM)发病年龄、DM病程和HbA1c。

结果

共纳入25项研究 (17项横断面研究和8项纵向研究), 一共1, 963, 914例受试对象。在横断面研究中, DR受试者中任何认知障碍、早期认知障碍及痴呆的合并OR (95%置信区间) 分别为1.48(1.08–2.02)、1.59(1.01–2.51)和1.13(0.86–1.50)。在纵向研究中, DR受试者中任何认知障碍、早期认知障碍及痴呆的合并RR (95%置信区间) 分别为 1.35 (1.12–1.65), 1.50 (1.06–2.12) and 1.31 (1.03–1.66)。荟萃-回归分析显示, 年龄, DM发病年龄, DM病程和糖化血红蛋白 (HbA1c) 与认知障碍结局无统计学相关性。

结论

并发DR的DM患者发生认知障碍的几率高, 且将来发生认知障碍的风险也高。

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2: Meta-analysis for cross-sectional association between DR and any cognitive impairment.
Fig. 3: Meta-analysis for longitudinal association of DR and any cognitive impairment.
Fig. 4: Meta-analysis for cross-sectional association of DR and early stage of cognitive impairment.
Fig. 5: Meta-analysis for longitudinal association of DR and early stage of cognitive impairment.
Fig. 6: Meta-analysis for cross-sectional association of DR and dementia.
Fig. 7: Meta-analysis for longitudinal association of DR and dementia.
Fig. 8: Meta-analysis for cross-sectional association of PDR and any cognitive impairment.

References

  1. Wong TY, Sabanayagam C. The war on diabetic retinopathy: where are we now? Asia Pac J Ophthalmol. 2019;8:448–56. https://doi.org/10.1097/APO.0000000000000267.

    Article  Google Scholar 

  2. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396:413–46. https://doi.org/10.1016/S0140-6736(20)30367-6.

    Article  Google Scholar 

  3. World Health Organization. Dementia [electronic resource]: a public health priority. Geneva: World Health Organization; 2012.

  4. Livingston GO, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396:413–46. https://doi.org/10.1016/S0140-6736(20)30367-6.

    Article  Google Scholar 

  5. Zhang J, Chen C, Hua S, Liao H, Wang M, Xiong Y, et al. An updated meta-analysis of cohort studies: Diabetes and risk of Alzheimer’s disease. Diabetes Res Clin Pract. 2017;124:41–7. https://doi.org/10.1016/j.diabres.2016.10.024.

    Article  Google Scholar 

  6. Biessels GJ, Nobili F, Teunissen CE, Simó R, Scheltens P. Understanding multifactorial brain changes in type 2 diabetes: a biomarker perspective. Lancet Neurol. 2020;19:699–710. https://doi.org/10.1016/S1474-4422(20)30139-3.

    Article  CAS  Google Scholar 

  7. Amidei CB, Fayosse A, Dumurgier J, Machado-Fragua MD, Tabak AG, van Sloten T, et al. Association between age at diabetes onset and subsequent risk of dementia. JAMA. 2021;325:1640–9. https://doi.org/10.1001/jama.2021.4001.

    Article  Google Scholar 

  8. Cukierman T, Gerstein HC, Williamson JD. Cognitive decline and dementia in diabetes-systematic overview of prospective observational studies. Diabetologia. 2005;48:2460–9.

    Article  CAS  Google Scholar 

  9. Biessels GJ, Staekenborg S, Brunner E, Brayne C, Scheltens P. Risk of dementia in diabetes mellitus: a systematic review. Lancet Neurol. 2006;5:64–74.

    Article  Google Scholar 

  10. van Sloten TT, Sedaghat S, Carnethon MR, Launer LJ, Stehouwer CDA. Cerebral microvascular complications of type 2 diabetes: stroke, cognitive dysfunction, and depression. Lancet Diabetes Endocrinol. 2020;8:325–36. https://doi.org/10.1016/S2213-8587(19)30405-X.

    Article  Google Scholar 

  11. London A, Benhar I, Schwartz M. The retina as a window to the brain-from eye research to CNS disorders. Nat Rev Neurol. 2013;9:44–53. https://doi.org/10.1038/nrneurol.2012.227.

    Article  CAS  Google Scholar 

  12. Trost A, Lange S, Schroedl F, Bruckner D, Motloch KA, Bogner B, et al. Brain and retinal pericytes: origin, function and role. Front Cell Neurosci. 2016;10:20. https://doi.org/10.3389/fncel.2016.00020.

    Article  CAS  Google Scholar 

  13. Vecino E, Rodriguez FD, Ruzafa N, Pereiro X, Sharma SC. Glia-neuron interactions in the mammalian retina. Prog Retin Eye Res. 2016;51. https://doi.org/10.1016/j.preteyeres.2015.06.003.

  14. Cheung CY, Chan VTT, Mok VC, Chen C, Wong TY. Potential retinal biomarkers for dementia: what is new? Curr Opin Neurol. 2019;32:82–91. https://doi.org/10.1097/WCO.0000000000000645.

    Article  CAS  Google Scholar 

  15. Cheung CY-L, Ikram MK, Chen C, Wong TY. Imaging retina to study dementia and stroke. Prog Retin Eye Res. 2017;57:89–107. https://doi.org/10.1016/j.preteyeres.2017.01.001.

    Article  Google Scholar 

  16. van der Flier WM, Skoog I, Schneider JA, Pantoni L, Mok V, Chen CLH, et al. Vascular cognitive impairment. Nat Rev Dis Primers. 2018;4:18003. http://europepmc.org/abstract/MED/2944676910.1038/nrdp.2018.3.

  17. Hendrikx D, Smits A, Lavanga M, De Wel O, Thewissen L, Jansen K, et al. Measurement of Neurovascular Coupling in Neonates. Front Physiol. 2019;10. https://doi.org/10.3389/fphys.2019.00065.

  18. Bruce DG, Davis WA, Starkstein SE, Davis TM. Mid-life predictors of cognitive impairment and dementia in type 2 diabetes mellitus: the Fremantle Diabetes Study. J Alzheimers Dis. 2014;42:S63–70. https://doi.org/10.3233/JAD-132654.

    Article  CAS  Google Scholar 

  19. Jacobson AM, Ryan CM, Braffett BH, Gubitosi-Klug RA, Lorenzi GM, Luchsinger JA, et al. Cognitive performance declines in older adults with type 1 diabetes: results from 32 years of follow-up in the DCCT and EDIC Study. Lancet Diabetes Endocrinol. 2021. https://doi.org/10.1016/S2213-8587(21)00086-3.

  20. Crosby-Nwaobi RR, Sivaprasad S, Amiel S, Forbes A. The relationship between diabetic retinopathy and cognitive impairment. Diabetes Care. 2013;36:3177–86. https://doi.org/10.2337/dc12-2141.

    Article  Google Scholar 

  21. Pedersen HE, Sandvik CH, Subhi Y, Grauslund J, Pedersen FN. Relationship between diabetic retinopathy and systemic neurodegenerative diseases: a systematic review and meta-analysis. Ophthalmol Retina. 2021. https://doi.org/10.1016/j.oret.2021.07.002.

  22. Cheng D, Zhao X, Yang S, Wang G, Ning G. Association between diabetic retinopathy and cognitive impairment: a systematic review and meta-analysis. Front Aging Neurosci. 2021;13. https://doi.org/10.3389/fnagi.2021.692911.

  23. Hill NTM, Mowszowski L, Naismith SL, Chadwick VL, Valenzuela M, Lampit A. Computerized cognitive training in older adults with mild cognitive impairment or dementia: a systematic review and meta-analysis. Am J Psychiatry. 2017;174:329–40. https://doi.org/10.1176/appi.ajp.2016.16030360.

    Article  Google Scholar 

  24. Jacova C, Peters KR, Beattie BL, Wong E, Riddehough A, Foti D, et al. Cognitive impairment no dementia—neuropsychological and neuroimaging characterization of an amnestic subgroup. Dement Geriatr Cogn Disord. 2008;25:238–47. https://doi.org/10.1159/000115848.

    Article  Google Scholar 

  25. Campbell NL, Unverzagt F, LaMantia MA, Khan BA, Boustani MA. Risk factors for the progression of mild cognitive impairment to dementia. Clin Geriatr Med. 2013;29:873–93. https://doi.org/10.1016/j.cger.2013.07.009.

    Article  Google Scholar 

  26. Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:280–92. https://doi.org/10.1016/j.jalz.2011.03.003.

    Article  Google Scholar 

  27. Jack CR, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018;14:535–62. https://doi.org/10.1016/j.jalz.2018.02.018.

    Article  Google Scholar 

  28. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;283:2008–12.

    Article  CAS  Google Scholar 

  29. Wells GA, Shea B, O’Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hospital Research Institute 2014. http://www.ohri.ca/programs/clinical_epidemiology/oxford.htm. Accessed 3 Feb 2020.

  30. Modesti PA, Reboldi G, Cappuccio FP, Agyemang C, Remuzzi G, Rapi S, et al. Panethnic differences in blood pressure in Europe: a systematic review and meta-analysis. PLoS ONE. 2016;11:e0147601. https://doi.org/10.1371/journal.pone.0147601.

    Article  CAS  Google Scholar 

  31. Gorska-Ciebiada M, Saryusz-Wolska M, Borkowska A, Ciebiada M, Loba J. Adiponectin, leptin and IL-1 β in elderly diabetic patients with mild cognitive impairment. Metab Brain Dis. 2016;31:257–66. https://doi.org/10.1007/s11011-015-9739-0.

    Article  CAS  Google Scholar 

  32. Gorska-Ciebiada M, Saryusz-Wolska M, Borkowska A, Ciebiada M, Loba J. C-reactive protein, advanced glycation end products, and their receptor in type 2 diabetic, elderly patients with mild cognitive impairment. Front Aging Neurosci. 2015;7:209. https://doi.org/10.3389/fnagi.2015.00209.

    Article  CAS  Google Scholar 

  33. Gorska-Ciebiada M, Saryusz-Wolska M, Ciebiada M, Loba J. Mild cognitive impairment and depressive symptoms in elderly patients with diabetes: prevalence, risk factors, and comorbidity. J Diabetes Res. 2014;2014:179648. https://doi.org/10.1155/2014/179648.

    Article  Google Scholar 

  34. Cole JB, Florez JC. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol. 2020;16:377–90. https://doi.org/10.1038/s41581-020-0278-5.

    Article  Google Scholar 

  35. Moran EP, Wang Z, Chen J, Sapieha P, Smith LEH, Ma J-X. Neurovascular cross talk in diabetic retinopathy: Pathophysiological roles and therapeutic implications. Am J Physiol Heart Circ Physiol. 2016;311:H738–49. https://doi.org/10.1152/ajpheart.00005.2016.

    Article  Google Scholar 

  36. Gardner T, Davila J. The neurovascular unit and the pathophysiologic basis of diabetic retinopathy. Graefes Arch Clin Exp Ophthalmol. 2017;255. https://doi.org/10.1007/s00417-016-3548-y.

  37. Duh EJ, Sun JK, Stitt AW. Diabetic retinopathy: current understanding, mechanisms, and treatment strategies. JCI Insight. 2017;2:e93751. https://doi.org/10.1172/jci.insight.93751.

    Article  Google Scholar 

  38. Nakahara T, Mori A, Kurauchi Y, Sakamoto K, Ishii K. Neurovascular interactions in the retina: physiological and pathological roles. J Pharmacol Sci. 2013;123:79–84. https://doi.org/10.1254/jphs.13R03CP.

    Article  CAS  Google Scholar 

  39. Nian S, Lo ACY, Mi Y, Ren K, Yang D. Neurovascular unit in diabetic retinopathy: pathophysiological roles and potential therapeutical targets. Eye Vis. 2021;8:15. https://doi.org/10.1186/s40662-021-00239-1.

    Article  Google Scholar 

  40. Tang Z, Chan MY, Leung WY, Wong HY, Ng CM, Chan VTT, et al. Assessment of retinal neurodegeneration with spectral-domain optical coherence tomography: a systematic review and meta-analysis. Eye. 2021;35:1317–25. https://doi.org/10.1038/s41433-020-1020-z.

    Article  Google Scholar 

  41. Zlokovic BV. Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nat Rev Neurosci. 2011;12:723–38. https://doi.org/10.1038/nrn3114.

    Article  CAS  Google Scholar 

  42. Arvanitakis Z, Capuano AW, Leurgans SE, Bennett DA, Schneider JA. Relation of cerebral vessel disease to Alzheimer’s disease dementia and cognitive function in elderly people: a cross-sectional study. Lancet Neurol. 2016;15:934–43. https://doi.org/10.1016/S1474-4422(16)30029-1.

    Article  CAS  Google Scholar 

  43. Iturria-Medina Y, Sotero RC, Toussaint PJ, Mateos-Pérez JM, Evans AC, Weiner MW, et al. Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis. Nat Commun. 2016;7:11934. https://doi.org/10.1038/ncomms11934.

    Article  CAS  Google Scholar 

  44. Nelson AR, Sweeney MD, Sagare AP, Zlokovic BV. Neurovascular dysfunction and neurodegeneration in dementia and Alzheimer’s disease. Biochim Biophys Acta. 2016;1862:887–900. https://doi.org/10.1016/j.bbadis.2015.12.016.

    Article  CAS  Google Scholar 

  45. Alzheimer’s A. 2016 Alzheimer’s disease facts and figures. Alzheimers Dement. 2016;12:459–509. https://doi.org/10.1016/j.jalz.2016.03.001.

    Article  Google Scholar 

  46. Cortes-Canteli M, Iadecola C. Alzheimer’s disease and vascular aging: JACC focus seminar. J Am Coll Cardiol. 2020;75:942–51. https://doi.org/10.1016/j.jacc.2019.10.062.

    Article  CAS  Google Scholar 

  47. Zhao Z, Nelson AR, Betsholtz C, Zlokovic BV. Establishment and dysfunction of the blood-brain barrier. Cell. 2015;163:1064–78. https://doi.org/10.1016/j.cell.2015.10.067.

    Article  CAS  Google Scholar 

  48. Kisler K, Nelson AR, Montagne A, Zlokovic BV. Cerebral blood flow regulation and neurovascular dysfunction in Alzheimer disease. Nat Rev Neurosci. 2017;18:419–34. https://doi.org/10.1038/nrn.2017.48.

    Article  CAS  Google Scholar 

  49. Dubois B, Hampel H, Feldman HH, Scheltens P, Aisen P, Andrieu S, et al. Preclinical Alzheimer’s disease: definition, natural history, and diagnostic criteria. Alzheimers Dement. 2016;12:292–323. https://doi.org/10.1016/j.jalz.2016.02.002.

    Article  Google Scholar 

  50. Beck J, Greenwood DA, Blanton L, Bollinger ST, Butcher MK, Condon JE, et al. National standards for diabetes self-management education and support. Diabetes Care. 2017;40:1409–19. https://doi.org/10.2337/dci17-0025.

    Article  Google Scholar 

  51. Feil DG, Zhu CW, Sultzer DL. The relationship between cognitive impairment and diabetes self-management in a population-based community sample of older adults with Type 2 diabetes. J Behav Med. 2012;35:190–9. https://doi.org/10.1007/s10865-011-9344-6.

    Article  Google Scholar 

  52. Crane PK, Walker R, Hubbard RA, Li G, Nathan DM, Zheng H, et al. Glucose levels and risk of dementia. New Engl J Med. 2013;369:540–8. https://doi.org/10.1056/NEJMoa1215740.

    Article  CAS  Google Scholar 

  53. Ruamviboonsuk P, Cheung CY, Zhang X, Raman R, Park SJ, Ting DSW. Artificial intelligence in ophthalmology: evolutions in asia. Asia Pac J Ophthalmol. 2020;9:78–84.

  54. Bellemo V, Lim G, Rim TH, Tan GSW, Cheung CY, Sadda S, et al. Artificial intelligence screening for diabetic retinopathy: the real-world emerging application. Curr Diabetes Rep. 2019;19:72. https://doi.org/10.1007/s11892-019-1189-3

    Article  Google Scholar 

  55. Cheung CY, Tang F, Ting DSW, Tan GSW, Wong TY. Artificial intelligence in diabetic eye disease screening. Asia Pac J Ophthalmol. 2019;8:158–64.

Download references

Funding

Health and Medical Research Fund, Hong Kong (Grant Number: 04153506). The funding organization had no role in the design or conduct of this research.

Author information

Authors and Affiliations

Authors

Contributions

RNFC was responsible for registering the protocol, conducting literature search, screening potentially eligible studies, extracting and analysing data, interpreting results, and writing the paper. ZT and VTTC contributed to data analysis, results interpretation, and writing the paper. RNCC, ETWC, and NCYN were responsible for designing the review protocol, writing the protocol, screening potentially eligible studies, and extracting and analysing data. CYC was responsible for designing the study, provided instructions on the paper, and supervised the entire process.

Corresponding author

Correspondence to Carol Y. Cheung.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chan, R.N.F., Tang, Z., Chan, V.T.T. et al. The cross-sectional and longitudinal relationship of diabetic retinopathy to cognitive impairment: a systematic review and meta-analysis. Eye 37, 220–227 (2023). https://doi.org/10.1038/s41433-022-02033-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41433-022-02033-2

Search

Quick links