Health screening program revealed risk factors associated with development and progression of papillomacular bundle defect

Abstract

Background/aims

The papillomacular bundle (PMB) area is an important anatomical site associated with central vision. As preventive medicine and health screening examinations are now becoming commonplace, the incidental detection of papillomacular bundle defect (PMBD) on fundus photography has been increasing. However, clinical significance of incidental PMBD has not been well documented to date. Thus, through long-term and longitudinal observation, we aimed to investigate the risk factors for the development and progression of PMBD and its predictive role associated with systemic diseases and glaucoma.

Methods

This longitudinal study included subjects who had undergone standardized health screening. We retrospectively reviewed patients for whom PMBD had been detected in fundus photography and followed up for more than 5 years. For a comparative analysis, non-PMBD groups of age- and gender-matched healthy controls were selected.

Results

A total of about 67,000 fundus photographs were analyzed for 8.0 years, and 587 PMBD eyes were found. Among them, 234 eyes of 234 patients who had had fundus photographs taken for more than 5 years were finally included. A total of 216 eyes (92.3%) did not progress during the 8.1 ± 2.7 years, whereas 18 eyes (7.7%) showed progression at 7.6 ± 2.9 years after initial detection. A multivariate logistic regression analysis using 224 non-PMBD healthy controls revealed low body mass index (BMI < 20 kg/m2), systemic hypertension, and sclerotic changes of retinal artery as the significant risk factors for the development of PMBD. Regarding PMBD progression, low BMI, concomitant retinal nerve fiber layer defect (RNFLD) at non-PMB sites, optic disc hemorrhage, and higher vertical cup/disc ratio were individual significant risk factors.

Conclusion

PMBD is associated with ischemic effects. Although the majority of PMBD do not progress, some of cases are associated with glaucomatous damage in a long-term way. PMBD might be a personalized indicator representing ischemia-associated diseases and a predictive factor for diagnosis and preventive management of glaucoma.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Abbreviations

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

BMI:

Body mass index

CI:

Confidence intervals

CWS:

Cotton wool spot

DH:

Optic disc hemorrhage

FS:

Flammer syndrome

HR:

Hazard ratios

IOP:

Intraocular pressure

PMB:

Papillomacular bundle

PMBD:

Papillomacular bundle defect

PPPM:

Predictive, preventive, and personalized medicine

RNFL:

Retinal nerve fiber layer

RNFLD:

Retinal nerve fiber layer defect

VCDR:

Vertical cup to disc ratio

WBC:

White blood cell

References

  1. 1.

    Beaglehole R, Bonita R, Horton R, Adams C, Alleyne G, Asaria P, et al. Priority actions for the non-communicable disease crisis. Lancet. 2011;377(9775):1438–47.

    Article  Google Scholar 

  2. 2.

    Kim HC, Oh SM. Noncommunicable diseases: current status of major modifiable risk factors in Korea. J Prev Med Public Health. 2013;46(4):165–72.

    Article  Google Scholar 

  3. 3.

    Breslow L, Somers AR. The lifetime health-monitoring program: a practical approach to preventive medicine. N Engl J Med. 1977;296(11):601–8.

    CAS  Article  Google Scholar 

  4. 4.

    Jameson JL, Longo DL. Precision medicine—personalized, problematic, and promising. Obstet Gynecol Surv. 2015;70(10):612–4.

    Article  Google Scholar 

  5. 5.

    Alyabsi M, Alhumaid A, Allah-Bakhsh H, Alkelya M, Aziz MA. Colorectal cancer in Saudi Arabia as the proof-of-principle model for implementing strategies of predictive, preventive, and personalized medicine in healthcare. EPMA Journal. 2019:1–13.

  6. 6.

    Lee JH, Yu SE, Kim K-H, Yu MH, Jeong I-H, Cho JY, et al. Individualized metabolic profiling stratifies pancreatic and biliary tract cancer: a useful tool for innovative screening programs and predictive strategies in healthcare. EPMA Journal. 2018;9(3):287–97.

    Article  Google Scholar 

  7. 7.

    Muramatsu C, Hayashi Y, Sawada A, Hatanaka Y, Hara T, Yamamoto T, et al. Detection of retinal nerve fiber layer defects on retinal fundus images for early diagnosis of glaucoma. J Biomed Opt. 2010;15(1):016021.

    Article  Google Scholar 

  8. 8.

    Abràmoff MD, Reinhardt JM, Russell SR, Folk JC, Mahajan VB, Niemeijer M, et al. Automated early detection of diabetic retinopathy. Ophthalmology. 2010;117(6):1147–54.

    Article  Google Scholar 

  9. 9.

    Pirbhai A, Sheidow T, Hooper P. Prospective evaluation of digital non-stereo color fundus photography as a screening tool in age-related macular degeneration. Am J Ophthalmol. 2005;139(3):455–61.

    Article  Google Scholar 

  10. 10.

    Wagner SK, Fu DJ, Faes L, Liu X, Huemer J, Khalid H et al. Insights into systemic disease through retinal imaging-based oculomics. Translational Vision Science & Technology. 2020;9(2):6-.

  11. 11.

    Chihara E, Matsuoka T, Ogura Y, Matsumura M. Retinal nerve fiber layer defect as an early manifestation of diabetic retinopathy. Ophthalmology. 1993;100(8):1147–51.

    CAS  Article  Google Scholar 

  12. 12.

    Ogden TE. Nerve fiber layer of the primate retina: morphometric analysis. Invest Ophthalmol Vis Sci. 1984;25(1):19–29.

    CAS  PubMed  Google Scholar 

  13. 13.

    Barr CC, Glaser JS, Blankenship G. Acute disc swelling in juvenile diabetes: clinical profile and natural history of 12 cases. Arch Ophthalmol. 1980;98(12):2185–92.

    CAS  Article  Google Scholar 

  14. 14.

    Chihara E, Honda Y. Topographic changes in the optic disc in eyes with cotton-wool spots and primary open-angle glaucoma. Graefes Arch Clin Exp Ophthalmol. 1991;229(1):13–8.

    CAS  Article  Google Scholar 

  15. 15.

    Sheets C, Grewal D, Greenfield DS. Ocular toxoplasmosis presenting with focal retinal nerve fiber atrophy simulating glaucoma. J Glaucoma. 2009;18(2):129–31.

    Article  Google Scholar 

  16. 16.

    Kim KE, Kim MJ, Park KH, Jeoung JW, Kim SH, Kim CY, et al. Prevalence, awareness, and risk factors of primary open-angle glaucoma: Korea National Health and Nutrition Examination Survey 2008–2011. Ophthalmology. 2016;123(3):532–41.

    Article  Google Scholar 

  17. 17.

    Uchida H, Yamamoto T, Tomita G, Kitazawa Y. Peripapillary atrophy in primary angle-closure glaucoma: a comparative study with primary open-angle glaucoma. Am J Ophthalmol. 1999;127(2):121–8.

    CAS  Article  Google Scholar 

  18. 18.

    Scheie HG. Evaluation of ophthalmoscopic changes of hypertension and arteriolar sclerosis. AMA archives of ophthalmology. 1953;49(2):117–38.

    CAS  Article  Google Scholar 

  19. 19.

    Airaksinen PJ, Mustonen E, Alanko HI. Optic disc hemorrhages: analysis of stereophotographs and clinical data of 112 patients. Arch Ophthalmol. 1981;99(10):1795–801.

    CAS  Article  Google Scholar 

  20. 20.

    Chihara E, Tanihara H. Parameters associated with papillomacular bundle defects in glaucoma. Graefes Arch Clin Exp Ophthalmol. 1992;230(6):511–7.

    CAS  Article  Google Scholar 

  21. 21.

    Kimura Y, Hangai M, Morooka S, Takayama K, Nakano N, Nukada M, et al. Retinal nerve fiber layer defects in highly myopic eyes with early glaucoma. Invest Ophthalmol Vis Sci. 2012;53(10):6472–8.

    Article  Google Scholar 

  22. 22.

    Kim DM, Seo JH, Kim SH, Hwang S-S. Comparison of localized retinal nerve fiber layer defects between a low-teen intraocular pressure group and a high-teen intraocular pressure group in normal-tension glaucoma patients. J Glaucoma. 2007;16(3):293–6.

    Article  Google Scholar 

  23. 23.

    Shin JW, Sung KR, Park S-W. Patterns of progressive ganglion cell–inner plexiform layer thinning in glaucoma detected by OCT. Ophthalmology. 2018;125(10):1515–25.

    Article  Google Scholar 

  24. 24.

    Landis JR, Koch GG. The measurement of observer agreement for categorical data. biometrics. 1977:159-74.

  25. 25.

    Black AA, Wood JM, Lovie-Kitchin JE. Inferior visual field reductions are associated with poorer functional status among older adults with glaucoma. Ophthalmic Physiol Opt. 2011;31(3):283–91.

    Article  Google Scholar 

  26. 26.

    Sumi I, Shirato S, Matsumoto S, Araie M. The relationship between visual disability and visual field in patients with glaucoma. Ophthalmology. 2003;110(2):332–9.

    Article  Google Scholar 

  27. 27.

    Aspinall PA, Johnson ZK, Azuara-Blanco A, Montarzino A, Brice R, Vickers A. Evaluation of quality of life and priorities of patients with glaucoma. Invest Ophthalmol Vis Sci. 2008;49(5):1907–15.

    Article  Google Scholar 

  28. 28.

    Blumberg DM, De Moraes CG, Prager AJ, Yu Q, Al-Aswad L, Cioffi GA, et al. Association between undetected 10-2 visual field damage and vision-related quality of life in patients with glaucoma. JAMA ophthalmology. 2017;135(7):742–7.

    Article  Google Scholar 

  29. 29.

    Cho KH, Ahn SJ, Jung C, Han MK, Park KH, Woo SJ. Ischemic injury of the papillomacular bundle is a predictive marker of poor vision in eyes with branch retinal artery occlusion. Am J Ophthalmol. 2016;162:107–20.e2.

    Article  Google Scholar 

  30. 30.

    Rebolleda G, Sánchez-Sánchez C, González-López JJ, Contreras I, Munoz-Negrete FJ. Papillomacular bundle and inner retinal thicknesses correlate with visual acuity in nonarteritic anterior ischemic optic neuropathy. Invest Ophthalmol Vis Sci. 2015;56(2):682–92.

    Article  Google Scholar 

  31. 31.

    Flammer J, Konieczka K, Flammer AJ. The primary vascular dysregulation syndrome: implications for eye diseases. EPMA Journal. 2013;4(1):14.

    Article  Google Scholar 

  32. 32.

    Konieczka K, Choi HJ, Koch S, Fankhauser F, Schoetzau A, Kim DM. Relationship between normal tension glaucoma and Flammer syndrome. EPMA Journal. 2017;8(2):111–7.

    Article  Google Scholar 

  33. 33.

    McLEOD D, Marshall J, Kohner E, Bird AC. The role of axoplasmic transport in the pathogenesis of retinal cotton-wool spots. Br J Ophthalmol. 1977;61(3):177–91.

    CAS  Article  Google Scholar 

  34. 34.

    Flammer J, Konieczka K, Bruno RM, Virdis A, Flammer AJ, Taddei S. The eye and the heart. Eur Heart J. 2013;34(17):1270–8. https://doi.org/10.1093/eurheartj/eht023.

    Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Song Y-J, Cho K-I, Kim S-M, Jang H-D, Park J-M, Kim S-S, et al. The predictive value of retinal vascular findings for carotid artery atherosclerosis: are further recommendations with regard to carotid atherosclerosis screening needed? Heart Vessel. 2013;28(3):369–76.

    Article  Google Scholar 

  36. 36.

    Sugiyama K, Tomita G, Kitazawa Y, Onda E, Shinohara H, Park KH. The associations of optic disc hemorrhage with retinal nerve fiber layer defect and peripapillary atrophy in normal-tension glaucoma. Ophthalmology. 1997;104(11):1926–33.

    CAS  Article  Google Scholar 

  37. 37.

    Suh MH, Park KH. Period prevalence and incidence of optic disc haemorrhage in normal tension glaucoma and primary open-angle glaucoma. Clin Exp Ophthalmol. 2011;39(6):513–9.

    Article  Google Scholar 

  38. 38.

    Leske MC, Heijl A, Hussein M, Bengtsson B, Hyman L, Komaroff E. Factors for glaucoma progression and the effect of treatment: the early manifest glaucoma trial. Arch Ophthalmol. 2003;121(1):48–56.

    Article  Google Scholar 

  39. 39.

    Zheng Y, Cheung CY, Wong TY, Mitchell P, Aung T. Influence of height, weight, and body mass index on optic disc parameters. Invest Ophthalmol Vis Sci. 2010;51(6):2998–3002.

    Article  Google Scholar 

  40. 40.

    Konieczka K, Ritch R, Traverso CE, Kim DM, Kook MS, Gallino A, et al. Flammer syndrome. EPMA J. 2014;5(1):11. https://doi.org/10.1186/1878-5085-5-11.

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Kang JH, Loomis SJ, Rosner BA, Wiggs JL, Pasquale LR. Comparison of risk factor profiles for primary open-angle glaucoma subtypes defined by pattern of visual field loss: a prospective study. Invest Ophthalmol Vis Sci. 2015;56(4):2439–48.

    Article  Google Scholar 

  42. 42.

    Higashi Y, Sasaki S, Nakagawa K, Kimura M, Noma K, Sasaki S, et al. Low body mass index is a risk factor forimpaired endothelium-dependent vasodilation in humans: role of nitric oxide and oxidative stress. J Am Coll Cardiol. 2003;42(2):256–63.

    CAS  Article  Google Scholar 

  43. 43.

    Konieczka K, Erb C. Diseases potentially related to Flammer syndrome. EPMA J. 2017;8(4):327–32. https://doi.org/10.1007/s13167-017-0116-4.

    Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Flammer J, Konieczka K. The discovery of the Flammer syndrome: a historical and personal perspective. EPMA J. 2017;8(2):75–97. https://doi.org/10.1007/s13167-017-0090-x.

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Barthelmes J, Nägele MP, Ludovici V, Ruschitzka F, Sudano I, Flammer AJ. Endothelial dysfunction in cardiovascular disease and Flammer syndrome-similarities and differences. EPMA J. 2017;8(2):99–109. https://doi.org/10.1007/s13167-017-0099-1.

    Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Terelak-Borys B, Grabska-Liberek I, Piekarniak-Wozniak A, Konieczka K. Choroidal infarction in a glaucoma patient with Flammer syndrome: a case report with a long term follow-up. BMC Ophthalmol. 2017;17(1):23. https://doi.org/10.1186/s12886-017-0416-4.

    Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Konieczka K, Koch S, Binggeli T, Schoetzau A, Kesselring J. Multiple sclerosis and primary vascular dysregulation (Flammer syndrome). EPMA J. 2016;7(1):13. https://doi.org/10.1186/s13167-016-0062-6.

    Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Sabel BA, Wang J, Fähse S, Cárdenas-Morales L, Antal A. Personality and stress influence vision restoration and recovery in glaucoma and optic neuropathy following alternating current stimulation: implications for personalized neuromodulation and rehabilitation. EPMA J. 2020;11(2):177–96. https://doi.org/10.1007/s13167-020-00204-3.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Berger JS, Haskell L, Ting W, Lurie F, Chang SC, Mueller LA, et al. Evaluation of machine learning methodology for the prediction of healthcare resource utilization and healthcare costs in patients with critical limb ischemia-is preventive and personalized approach on the horizon? EPMA J. 2020;11(1):53–64. https://doi.org/10.1007/s13167-019-00196-9.

    Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Chen Q, Zhao B, Wang MY, Chen XY, Li D, Jiang XQ, et al. Associations between the red blood cell distribution width and primary angle-closure glaucoma: a potential for disease prediction. EPMA J. 2019;10(2):185–93. https://doi.org/10.1007/s13167-019-00166-1.

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Li S, Shao M, Wan Y, Tang B, Sun X, Cao W. Relationship between ocular biometry and severity of primary angle-closure glaucoma: relevance for predictive, preventive, and personalized medicine. EPMA J. 2019;10(3):261–71. https://doi.org/10.1007/s13167-019-00174-1.

    Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Lee JS, Lee SH, Oum BS, Chung JS, Cho BM, Hong JW. Relationship between intraocular pressure and systemic health parameters in a Korean population. Clin Exp Ophthalmol. 2002;30(4):237–41.

    Article  Google Scholar 

Download references

Funding

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019R1F1A1058426).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Hyuk Jin Choi.

Ethics declarations

Ethical approval

The project was approved by Institutional Review Board of Seoul National University Hospital (No. 1906-141-1043)

Statement of informed consent

The requirement to obtain written informed consent was waived by the Institutional Review Board, because our study was retrospective research based on medical records, and also because this research presented no more than minimal risk of harm to subjects

Statement of human and animal rights

The study was carried out according to the Declaration of Helsinki

Conflict of interest

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.

Meeting presentation

None

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Baek, S.U., Lee, W.J., Park, K.H. et al. Health screening program revealed risk factors associated with development and progression of papillomacular bundle defect. EPMA Journal 12, 41–55 (2021). https://doi.org/10.1007/s13167-021-00235-4

Download citation

Keywords

  • Papillomacular bundle defect
  • Ophthalmology
  • Health screening examination
  • Program
  • Low body mass index
  • Ischemia-associated diseases
  • Risk assessment
  • Risk factors
  • Screening
  • Cardiovascular disease
  • Longitudinal study
  • Disease development and progression
  • Systemic effects and characteristics
  • Glaucoma
  • Predictive factors
  • Personalized indicator
  • Preventive management
  • Predictive preventive personalized medicine (PPPM/3PM)