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Development and validation of a model that predicts the risk of diabetic retinopathy in type 2 diabetes mellitus patients

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Abstract

Aims

Diabetic retinopathy is the leading cause of blindness in people with type 2 diabetes. To enable primary care physicians to identify high-risk type 2 diabetic patients with diabetic retinopathy at an early stage, we developed a nomogram model to predict the risk of developing diabetic retinopathy in the Xinjiang type 2 diabetic population.

Methods

In a retrospective study, we collected data on 834 patients with type 2 diabetes through an electronic medical record system. Stepwise regression was used to filter variables. Logistic regression was applied to build a nomogram prediction model and further validated in the training set. The c-index, forest plot, calibration plot, and clinical decision curve analysis were used to comprehensively validate the model and evaluate its accuracy and clinical validity.

Results

Four predictors were selected to establish the final model: hypertension, blood urea nitrogen, duration of diabetes, and diabetic peripheral neuropathy. The model displayed medium predictive power with a C-index of 0.781(95%CI:0.741–0.822) in the training set and 0.865(95%CI:0.807–0.923)in the validation set. The calibration curve of the DR probability shows that the predicted results of the nomogram are in good agreement with the actual results. Decision curve analysis demonstrated that the novel nomogram was clinically valuable.

Conclusions

The nomogram of the risk of developing diabetic nephropathy contains 4 characteristics.

that can help primary care physicians quickly identify individuals at high risk of developing DR in patients with type 2 diabetes, to intervene as soon as possible.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

Abbreviations

T2DM:

Type 2 diabetes mellitus

DR:

Diabetic Retinopathy

DN:

Diabetic nephropathy

DPN:

Diabetic peripheral neuropathy

ROC:

Receiver operating characteristic

DCA:

Decision curve analysis

BMI:

Body mass index

HbA1c:

Glycosylated hemoglobin A1c

Scr:

Serum creatinine

BUN:

Blood urea nitrogen

LDL-C:

Low-density lipoprotein

HDL-C:

High-density lipoprotein

TC:

Total cholesterol; TG: triglycerides

IGF-1:

Insulin-like growth factor-1

IGFBP-3:

Insulin-like growth factor binding protein-3

FBG:

Fasting blood glucose

OR:

Odds ratio

MDRD:

Modification of diet in renal disease

References

  1. Guo Z, Priefer R (2021) Current progress in pharmacogenomics of Type 2 diabetes: a systemic overview[J]. Diabetes Metab Syndr 15(5):102239

    Article  Google Scholar 

  2. Joshi RD, Dhakal CK (2021) Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches[J]. Int J Environ Res Public Health 18(14):7346

    Article  Google Scholar 

  3. Yoon J, Oh IH, Seo H et al (2016) Disability-adjusted life years for 313 diseases and injuries: the 2012 Korean burden of disease study[J]. J Korean Med Sci 31(Suppl 2):S146–S157

    Article  Google Scholar 

  4. Yau JWY, Rogers SL, Kawasaki R et al (2012) Global prevalence and major risk factors of diabetic retinopathy.[J]. Diabetes care 35(3):556–564

    Article  Google Scholar 

  5. The EDPR (2004) The Prevalence of diabetic retinopathy among adults in the united states[J]. Arch Ophthalmol 122(4):552–563

    Article  Google Scholar 

  6. Pang C, Jia L, Jiang S et al (2012) Determination of diabetic retinopathy prevalence and associated risk factors in Chinese diabetic and pre-diabetic subjects: shanghai diabetic complications study.[J]. Diabetes/metabm Res Rev 28(3):276–283

    Article  Google Scholar 

  7. Xu J, Wei WB, Yuan MX et al (2012) Prevalence and risk factors for diabetic retinopathy: the Beijing communities diabetes study 6.[J]. Retina (Philadelphia, Pa.) 32(2):322–329

    Article  CAS  Google Scholar 

  8. Koziol M, Nowak MS, Udziela M et al (2020) First nation-wide study of diabetic retinopathy in Poland in the years 2013–2017[J]. Acta Diabetol 57(10):1255–1264

    Article  CAS  Google Scholar 

  9. Jinan BS, Amanda AH, VenkatZhang KM et al (2008) Projection of diabetic retinopathy and other major eye diseases among people with diabetes mellitus: united states, 2005–2050[J]. Archives of Ophthalmology 126(12):1740–1747

    Article  Google Scholar 

  10. Kashif M, Rohit V, Farzana C et al (2011) Severity of diabetic retinopathy and health-related quality of life[J]. Ophthalmology 118(4):649–655

    Article  Google Scholar 

  11. Zhang G, Chen H, Chen W et al (2017) Prevalence and risk factors for diabetic retinopathy in China: a multi-hospital-based cross-sectional study.[J]. British j ophthalmol 101(12):1591–1595

    Article  Google Scholar 

  12. Alberti KG, Zimmet PZ (1998) Definition, diagnosis and classification of diabetes mellitus and its complications Part.1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation[J]. Diabet Med 15(7):539–553

    Article  CAS  Google Scholar 

  13. Jalalonmuhali M, Lim SK, Md SMN et al (2017) MDRD vs. CKD-EPI in comparison to 51Chromium EDTA: a cross sectional study of Malaysian CKD cohort.[J]. BMC nephrol 18(1):1–6

    Article  Google Scholar 

  14. Tien Y W, Chui M G C, Michael L, et al. (2016) Diabetic retinopathy.[J]. Nat Rev Disease prim 2(1).

  15. Wei L, Bo X, Shanhu Q et al (2018) Non-lab and semi-lab algorithms for screening undiagnosed diabetes: a cross-sectional study[J]. EBioMedicine 35:307–316

    Article  Google Scholar 

  16. Yin L, Zhang D, Ren Q et al (2020) Prevalence and risk factors of diabetic retinopathy in diabetic patients: a community based cross-sectional study[J]. Med (Baltimore) 99(9):e19236

    Article  Google Scholar 

  17. Tien YW, Ronald K, Amirul FM et al (2005) Diabetic retinopathy in a multi-ethnic cohort in the united states[J]. Am J Ophthalmol 141(3):446–455

    Google Scholar 

  18. Xie XW, Xu L, Wang YX et al (2008) Prevalence and associated factors of diabetic retinopathy The Beijing eye study 2006 [J]. Gra Arch clin exp ophthalmol 246(11):1526

    Google Scholar 

  19. Rajalakshmi R, Amutha A, Ranjani H et al (2014) Prevalence and risk factors for diabetic retinopathy in Asian Indians with young onset type 1 and type 2 diabetes[J]. J Diabet Complicat 28(3):291–297

    Article  Google Scholar 

  20. Varma R, Macias GL, Torres M et al (2007) Biologic risk factors associated with diabetic retinopathy: the los angeles latino eye study[J]. Ophthalmology 114(7):1332–1340

    Article  Google Scholar 

  21. Sun Q, Jing Y, Zhang B, et al. (2021) The Risk Factors for Diabetic Retinopathy in a Chinese Population: A Cross-Sectional Study[J]. Journal of Diabetes Research 2021.

  22. Cui Y, Zhang M, Zhang L et al (2019) Prevalence and risk factors for diabetic retinopathy in a cross-sectional population-based study from rural southern China: dongguan eye study.[J]. BMJ Open 9(9):e023586

    Article  Google Scholar 

  23. Singh HV, Das S, Deka DC et al (2021) Prevalence of diabetic retinopathy in self-reported diabetics among various ethnic groups and associated risk factors in north-east India: a hospital-based study [J]. Indian J Ophthalmol 69(11):3132

    Article  Google Scholar 

  24. Pang C, Jia L, Hou X et al (2014) The significance of screening for microvascular diseases in Chinese community-based subjects with various metabolic abnormalities.[J]. PloS one 9(5):e97928

    Article  Google Scholar 

  25. Ramachandran R, Anandakumar A, Harish R et al (2014) Prevalence and risk factors for diabetic retinopathy in Asian Indians with young onset Type 1 and Type 2 Diabetes[J]. J Diabetes Its Complicati 28(3):291–297

    Article  Google Scholar 

  26. Christy LA, Anna K, Keri LM et al (2012) Impact of long-term measures of glucose and blood pressure on the retinal microvasculature[J]. Atherosclerosis 225(2):412–417

    Article  Google Scholar 

  27. Sergi D, Boulestin H, Campbell FM et al (2021) The role of dietary advanced glycation end products in metabolic dysfunction[J]. Mol Nutr Food Res 65(1):e1900934

    Article  Google Scholar 

  28. Pace LA, Crowe SE (2016) Complex relationships between food, diet, and the microbiome[J]. Gastroenterol Clin North Am 45(2):253–265

    Article  Google Scholar 

  29. Tseng S, Chou S, Low B et al (2015) Risk factors associated with diabetic retinopathy onset and progression in diabetes patients: a Taiwanese cohort study[J]. Int j clin exp med 8(11):21507

    CAS  Google Scholar 

  30. Rasheed R, Pillai GS, Kumar H et al (2021) Relationship between diabetic retinopathy and diabetic peripheral neuropathy - neurodegenerative and microvascular changes [J]. Indian j ophthalmol 69(11):3370

    Article  Google Scholar 

  31. Li S, Cui Y, Luo Z et al (2021) Association between blood urea nitrogen and incidence of type 2 diabetes mellitus in a Chinese population: a cohort study.[J]. Endocr j 68(9):1057–1065

    Article  CAS  Google Scholar 

  32. Koppe L, Nyam E, Vivot K et al (2016) Urea impairs β cell glycolysis and insulin secretion in chronic kidney disease [J]. J clin invest 126(9):3598–3612

    Article  Google Scholar 

  33. Shi R, Niu Z, Wu B et al (2020) Nomogram for the risk of diabetic nephropathy or diabetic retinopathy among patients with type 2 diabetes mellitus based on questionnaire and biochemical indicators: a cross-sectional study [J]. Diabetes metab syndr obes Targ Ther 13:1215

    Article  CAS  Google Scholar 

Download references

Funding

This work was supported by the fund project in the state key laboratory of Pathogenesis, Prevention, and Treatment of high incidence diseases in Central Asia. (Name of the fund: The role of TCF7L2/Wnt/GLP-1 signaling pathway and environmental factors in the pathogenesis of type-2 diabetes in Kazakhs (No.SKL-HIDCA-2019–15).

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Author notes

  1. Jing Yang and Sheng Jiang have contributed equally.

Authors and Affiliations

  1. State Key Laboratory of Pathogenesis, Prevention andTreatment of High Incidence Diseases in Central Asia, Department of Endocrinology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830017, China

    Jing Yang & Sheng Jiang

Authors
  1. Jing Yang
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  2. Sheng Jiang
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Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation, or in all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted, and agree to be. accountable for all aspects of the work.

Corresponding author

Correspondence to Sheng Jiang.

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Conflict of interest

The authors report no conflicts of interest in this work.

Ethics approval

The study was performed by the ethical guidelines of the 1975 Declaration of Helsinki and was reviewed and approved by the human research ethics committee of the Affiliated Hospital of Xinjiang Medical University.

Informed consent

Written informed consent was waived due to the retrospective nature and low risk of the study.

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This article belongs to the topical collection Eye Complications of Diabetes, managed by Giuseppe Querques.

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Yang, J., Jiang, S. Development and validation of a model that predicts the risk of diabetic retinopathy in type 2 diabetes mellitus patients. Acta Diabetol 60, 43–51 (2023). https://doi.org/10.1007/s00592-022-01973-1

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