Authors : Dr. Claret Onukogu

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/yzmft276

Scribd : https://tinyurl.com/y2ubtpk8

DOI : https://doi.org/10.38124/ijisrt/26jan1107

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Background: Diabetes could lead to diabetic retinopathy, an eye disease that causes damaged blood vessels and the growth of abnormal ones. The quantitative study examined the relationship between psychological distress and diabetic retinopathy while considering gender. The aim was to determine the role of psychological distress in diabetic retinopathy after controlling for covariates such as age, educational level, and income.  Methods: The study examined the association between the independent variables, psychological distress, and the dependent variable, diabetic retinopathy. For the study, a nationally representative dataset, The National Health and Nutrition Examination Survey (NHANES), was used to analyze data.  Result: There was no association between psychological distress and diabetic retinopathy among U.S. adults when controlling for education level, income, and age. A p-value of 0.87 shows that there is no association between psychological distress and diabetic retinopathy among U.S. adults after controlling for age, educational level, and income. The odds ratio of 0.96 (CI 0.60-1.56) denotes strong evidence to fail to reject the null hypothesis. The result shows that although the literature on the topic indicated a link between psychosocial functioning and diabetic macular edema (DME) or diabetic retinopathy, there is uncertainty behind the direction and extent of the association.  Conclusion: The results of the study have implications for positive social change and fill a public health knowledge gap, contributing to population health planning and implementation. Further research on patient education and regular screening for diabetic retinopathy in the United States for diabetic patients is necessary.

Keywords : Psychological Distress, Diabetic Retinopathy, Diabetes.

References :

1. Allen, M. (2017). Quantitative research. The Sage Encyclopedia of Communication Research Methods. https://dx.doi.org/10.4135/9781483381411.n476
2. Alperet, D. J., Rebello, S. A., Khoo, E. Y., Tay, Z., Seah, S. S., Tai, B., Tai, E., Emady-Azar, S., Chou, C. J., Darimont, C., Dam, R. M.V. (2019). The effect of coffee consumption on insulin sensitivity and other biological risk factors for type 2 diabetes: a randomized placebo-controlled trial. The American Journal of Clinical Nutrition, 111(2), 448-458. https://doi.org/10.1093/ajcn/nqz306
3. American Diabetes Association. (2018). Economic costs of diabetes in the U.S. in 2017. Diabetes Care, 41 (4). Doi: 10.2337/dci18-0007
4. Ansari, R. M., Hosseinzadeh, H., & Zwar, N. (2016). A qualitative research on self-management of type 2 diabetes in middle-aged population of rural area of Pakistan. International Education and Research Journal, 2(8), 62-65.
5. Ansari, P., Tabasumma, N., Snigdha, N. N., Siam, N. H., Panduru, R. V. N. R. S., Azam, S., Hannan, J. M. A., & Abdel-Wahab, Y. H. A. (2022). Diabetic retinopathy: An overview on mechanisms, pathophysiology and pharmacotherapy. Diabetology, 3, 159-175. https://doi.org/10.3390/diabetology3010011
6. Antonetti, D. A., Silva, P. S., & Stitt, A. W. (2021). Current understanding of the molecular and cellular pathology of diabetic retinopathy. Nature Reviews Endocrinology, 17, 195-206. https://doi.org/10.1038/s41574-020-00451-4
11. Atlanta Diabetes Association. (2020). Type 2 diabetes. Endocrine Specialty Group. https://atlantadiabetes.com/diabetes-care/type-2-diabetes/
12. Atwany, M. Z., Sahyoun, A. H., and Yaqub, M. (2022). Deep learning techniques for diabetic retinopathy classification: A survey. IEEE Acess. 10, 28642-28655. Doi: 10.1109/ACCESS.2022.3157632.
13. Aziz, T., Charoenlarpnopparut, C., & Mahapakulchai, S. (2023). Deep learning-based hemorrhage detection for diabetic retinopathy screening. Scientific Reports, 13(1), Article 1479. https://doi.org/10.1038/s41598-023-28680-3
14. Beaser, R.S., Turell, W.A., Howson, A. (2018). Strategies to improve prevention and management in diabetic retinopathy: Qualitative insights from mixed-methods study. Diabetes Spectrum, 31(1), 65-74. Doi: 10.2337/ds16-0043
15. Bora, A., Balasubramanian, S., Babenko, B., Virmani, S., Venugopalan, S., Mitani, A., Marinho, G., Cuadros, J., Ruamviboonsuk, P., Corrado, G., Peng, L., Webster, D., Varadarajan, A., Hammel, N., Liu, Y., & Bavishi, P. (2021). Predicting the risk of developing diabetic retinopathy using deep learning. The Lancet Digital Health, 3(1), e10-e19. https://doi.org/10.1016/S2589-7500(20)30250-8
16. Boyd, K., & Gregori, N. Z. (2020). What is diabetic retinopathy? American Academy of Ophthalmology. What Is diabetic retinopathy? - American Academy of Ophthalmology (aao.org)
17. Centers for Disease Control and Prevention. (2017). National health and nutrition examination survey (NHANES). 2015-2016 Data documentation, codebook, and frequencies. Mental Health- Depression Screener (DPQ_1). NHANES 2015-2016: Mental Health - Depression Screener Data Documentation, Codebook, and Frequencies (cdc.gov)
18. Centers for Disease Control and Prevention. (2019). Type 2 diabetes. United States Department of Health and Human Services. https://www.cdc.gov/diabetes/basics/type2.html
19. Centers for Disease Control and Prevention. (2021). The social-ecological model: a framework for prevention. violence prevention. United States Department of Health and Human Services. The Social-Ecological Model: A Framework for Prevention |Violence Prevention|Injury Center|CDC
20. Centers for Disease Control and Prevention. (2020). What is diabetes? United States Department of Health and Human Services. What is diabetes? | CDC
21. Centers for Disease Control and Prevention. (2021). Diabetes. Division of Diabetes Translation | CDC
22. Centers for Disease Control and Prevention. (2020). National diabetes statistics report 2020.  Estimates of diabetes and its burden in the United States. National Diabetes Statistics Report 2020. (cdc.gov)
23. Cherchi, S., Gigante, A., Spanu, M. A., Contini, P., Meloni, G., Fois, M. A., Pistis, D., Pilosu, R. M., Lai, A., Ruin, S., Campesi, I., & Tonolo, G. (2020). Sex-gender differences in diabetic retinopathy. Diabetology, 1, 1-10. https://doi.org/10.3390/diabetology1010001
24. Cleveland Clinic. (2021). Diabetes: An overview. 100 Years of Cleveland Clinic. Diabetes: Types, Risk Factors, Symptoms, Tests, Treatments & Prevention (clevelandclinic.org)
25. Cleveland Clinic. (2022). Optical coherence tomography. 100 Years of Cleveland Clinic. Diagnostics: Optical Coherence Tomography (clevelandclinic.org)
26. Curry, A. (2020). Fatty liver and type 2 diabetes. University of Florida Diabetes Institute. Fatty Liver and Type 2 Diabetes » Diabetes Institute » College of Medicine » University of Florida (ufl.edu)
27. Dai, L., Wu, L., Li, H., Cai, C., Wu, Q., Kong, H., Liu, R., Wang, X., Hou, X., Liu, Y., Long, X., Wen, Y., Lu, L., Shen, Y., Chen, Y., Shen, D., Yang, X., Zou, H., Sheng, B., & Jia, W. (2021). A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature Communications, 12(3242). https://doi.org/10.1038/s41467-021-23458-5
28. De Ritter, R., De Jong, M., Vos, R.C. (2020). Sex differences in the risk of vascular disease associated with diabetes. Biology of Sex Differences, 11 (1). https://doi.org/10.1186/s13293-019-0277-z
29. Demmin, D. L., & Silverstein, S. M. (2020). Visual impairment and mental health: Unmet needs and treatment options. Journal of Clinical Ophthalmology, 14, 4229-4251. https://doi.org/10.2147/OPTH.S258783
30. DiaGundluru, N., Rajput, D. S., Lakshmanna, K., Kaluri, R., Shorfuzzaman, M., Uddin, M., and Khan, M. A. R. (2022). Enhancement of detection of diabetic retinopathy using harris hawks optimization with deep learning model. Computational Intelligence and Neuroscience. 2022.  https://doi.org/10.1155/2022/8512469
31. Doheny, K. (2020). Pancreatic fat: Lifestyle habits count more than genetics. Endocrineweb. Pancreatic Fat: Lifestyle Habits Count More Than Genetics (endocrineweb.com)
32. Emani, S., Peters, E., Desai, S., Karson, A. S. Lipsitz, S. R., LaRocca, R., Stone, J., Suric, V., Wald, J.S., Wheeler, A., Williams, D.H., & Bates, D.W. (2018). Perceptions of adopters versus non-adopters of a patient portal: an application of diffusion of innovation theory. Journal of Innovation in Health Informatics, 25(3), 149-157. http://dx.doi.org/10.14236/jhi.v25i3.991
33. Farooq, M. S., Arooj, A., Alroobaea, R., Baqasah, A. M., Singh, D., and Sardar, R. (2022). Untangling computer-aided diagnostic system for screening diabetic retinopathy based on deep learning techniques. Sensors, 22(5), Article 1803. https://doi.org/10.3390/s22051803
34. Fenwick, E., Rees, G., Pesudovs, K., Dirani, M., Kawasaki, R., Wong, T. Y., and Lamoureux, E. (2011). Social and emotional impact of diabetic retinopathy: a review. Clinical & Experimental Ophthalmology. 40(1), 27-38. https://doi.org/10.1111/j.1442-9071.2011.02599.x
35. Forrester, J. V., Kuffova, L., & Delibegovic, M. (2020). The role of inflammation in diabetic retinopathy. (2020). Frontiers in Immunology, 11. https://doi.org/10.3389/fimmu.2020.583687
36. Gadekallu, T. R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P. K. R, & Srivastava, G. (2020). Deep neural networks to predict diabetic retinopathy. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-01963-7
37. Gomulka, K., Ruta, M. (2023). The role of inflammation and therapeutic concepts in diabetic retinopathy- a short review. International Journal of Molecular Sciences.  24(2). 1024. https://doi.org/10.3390/ijms24021024
38. Grzybowski, A., Brona, P., Lim, G., Ruamviboonsuk, P., Tan, G. S. W., Abramoff, M., and Ting, D. S. W. (2020). Artificial intelligence for diabetic retinopathy screening: A review. Eye, 34, 451-460. https://doi.org/10.1038/s41433-019-0566-0
39. Hayati, M., Muchtar, K., Roslidar, Maulina, N., Syamsuddin, I., Elwirehardja, G., N., Pardamean, B. (2023). Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning. Procedia Computer Science, 216,  57-66. https://doi.org/10.1016/j.procs.2022.12.111
40. Hewage S, Audimulam J, Sullivan E, Chi C, Yew TW, Yoong J. (2020). Barriers to gestational diabetes management and preferred interventions for women with gestational diabetes in singapore: Mixed methods study. JMIR Formative Research. 4(6). Barriers to Gestational Diabetes Management and Preferred Interventions for Women With Gestational Diabetes in Singapore: Mixed Methods Study - PMC (nih.gov)
41. International Diabetes Federation. (2022). Diabetes around the world in 2022. Diabetes Atlas. https://www.diabetesatlas.org/en/sections/worldwidetoll-of-diabetes.ht
42. International Diabetes Federation. (2023). Understanding Diabetes. International Diabetes Federation - Home (idf.org)
43. Jiwani, N., Gupta, K., and Afreen, N. (2022). A convolutional neural network approach for diabetic retinopathy classification. IEEE 11^th International Conference on Communication Systems and Network Technologies (CSNT). 357-361. Doi: 10.1109/CSNT54456.2022.9787577
44. Kang, Q., & Yang, C. (2020). Oxidative stress and diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications. Redox, 37, 2213-2317. https://doi.org/10.1016/j.redox.2020.101799
45. Kautzky-Willer, A., Harreiter, J., Pacini, G. (2016). Sex and gender differences in risk, pathophysiology and complications of type 2 diabetes mellitus. National Library of Medicine. National Center for Biotechnology Information. 37(3). 278-316. Doi: 10.1210/er.2015-1137
46. Kautzy-Willer, A., Harreiter, J., Abrahamian, H., Weitgasser, R., Fasching, P., Hoppichler, F., Lechleitner, M. (2019). Gender-specific aspects of prediabetes and diabetes mellitus-clinical recommendations. SpringerLink. 131 (1), 221-228. Gender-specific aspects of prediabetes and diabetes mellitus – clinical recommendations (Update 2019) | SpringerLink
47. Khoo, K., Man, R.E.K., Rees, G., Gupta, P., Lamoureux, E.L., Fenwick, E. K. (2019). The relationship between diabetic retinopathy and psychosocial functioning: a systematic review. Quality of Life Research, 28, (2017-2039). https://doi.org/10.1007/s11136-019-02165-1
48. Kovoor, E., Chauhan, S. K., and Hajrasouliha, A. (2022). Role of inflammatory cells in pathophysiology and management of diabetic retinopathy. Survey of Ophthamology. 67 (6). 1563-1573. https://doi.org/10.1016/j.survophthal.2022.07.008
49. Kowluru, R. A. (2023). Cross talks between oxidative stress, inflammation and epigenetics in diabetic retinopathy. Cells, 12(2), 300. https://doi.org/10.3390/cells12020300
50. Lauffenburger, J. C., Lewey, J., Jan, S., Lee, J., Ghazinouri, R., & Choudhry, N. K. (2020). Association of potentially modifiable diabetes care factors with glycemic control in patients with insulin-treated type 2 diabetes. JAMA Network Open, 3(1), e1919645– e1919645. https://doi.org/10.1001/jamanetworkopen.2019.19645
51. Li, Q., Wang, M., Li, X., Shao, Y. (2023). Aging and diabetic retinopathy: Inherently intertwined pathophysiological processes. Experimental Gerontology 175 (112138), 0531-5565. https://doi.org/10.1016/j.exger.2023.112138
52. Li, F., Wang, Y., Xu, T., Dong, L., Yan, L., Jiang, M., Zhang, X., Jiang, H., Wu, Z., & Zou, H. (2022). Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs. Eye, 36, 1433-1441. https://doi.org/10.1038/s41433-021-01552-8
53. Li, Q., Chang, Y., Zhang, K., Chen, H., Tao, S., and Zhang, Z. (2020). Implication of the gut microbiome composition of type 2 diabetic patients from northern China. Sci. Rep. 10 (1). Doi: 10.1038/s41598-020-62224-3
54. Li, X., Jiang, Y., Zhang, J., Li, M., Luo, H., and Yin, S. (2022). Lesion-attention pyramid network for diabetic retinopathy grading. Artificial Intelligence in Medicine. 126. https://doi.org/10.1016/j.artmed.2022.102259
55. Lien, A. S., & Jiang, Y. D. (2017). Integration of diffusion of innovation theory into diabetes care. Journal of diabetes investigation, 8(3), 259-260. Doi: 10.1111/jdi.12568
56. Madhuri, C., & Srilakshmi, B. (2019). Retrospective study on prevalence, risk factors, maternal and fetal outcome in gestational diabetes mellitus. International Archives of Integrated Medicine, 6(11), 94-98. iaim_2019_0611_14.pdf (iaimjournal.com)
57. Magne, F., Gotteland, M., Gauthier, L., Zazueta, A., Pesoa, S., Navarrete, P., and Balamurugan, R. (2020).The firmicutes/bacteroidetes ratio: A relevant marker of gut dysbiosis in obese patients? Nutrients, 12(5), 1474. Doi: 10.3390/nu12051474
58. Mahs, M., Pithan,  J. S., Bergmann, I., Gabrys, L., Graf, J., Holzemann, A., Van Laerhoven, K., Otto-Hagemann, S., Popescu, M. L., Schwermann, L., Wenz, B., Pahmeier, I., Teti, A. (2022). Activity tracker-based intervention to increase physical activity in patients with type 2 diabetes and healthy individuals: study protocol for a randomized controlled trial. BioMed Central, 23, 617. Activity tracker-based intervention to increase physical activity in patients with type 2 diabetes and healthy individuals: study protocol for a randomized controlled trial | Trials | Full Text (biomedcentral.com)
59. Mamo, Y., Bekele, F., Nigussie, T., & Zewudie, A. (2019). Determinants of poor glycemic control among adult patients with type 2 diabetes mellitus in Jimma University Medical Center, Jimma zone, south west Ethiopia: A case control study. BMC Endocrine Disorders, 19(1), 91. https://doi.org/10.1186/s12902-019-0421-0
60. McLachlan, K., & Gale, C. R. (2018). The effects of psychological distress and its interaction with socioeconomic position on risk of developing four chronic diseases. Journal of psychosomatic research, 109, 79-85. doi: 10.1016/j.jpsychores.2018.04.004
61. Mikkola, I., Hagnäs, M., Hartsenko, J., Kaila, M., & Winell, K. (2020). A personalized care plan is positively associated with better clinical outcomes in the care of patients with type 2 diabetes: A cross-sectional real-life study. Canadian Journal of Diabetes, 44(2), 133– 138. https://doi.org/10.1016/j.jcjd.2019.05.003
62. Muche, A. A., Olayemi, O. O., & Gete, Y. K. (2020). Effects of gestational diabetes mellitus on risk of  adverse maternal outcomes: a prospective cohort study in Northwest Ethiopia. BMC Pregnancy & Childbirth, 20(1), 1-13. Effects of gestational diabetes mellitus on risk of adverse maternal outcomes: a prospective cohort study in Northwest Ethiopia | BMC Pregnancy and Childbirth | Full Text (biomedcentral.com)
63. Muhwava, L. S., Murphy, K., Zarowsky, C., & Levitt, N. (2019). Experiences of lifestyle change among women with gestational diabetes mellitus (GDM): A behavioural diagnosis using the COM-B model in a low-income setting. Plos One Journal, 14(11). https://doi.org/10.1371/journal.pone.0225431
64. National Institute of Diabetes and Digestive and Kidney Diseases. (n.d.). Diabetes. Diabetes | NIDDK (nih.gov). Diabetes - NIDDK (nih.gov)
65. National Institutes of Health. (2019). Diabetic retinopathy. National Eye Institute. Diabetic Retinopathy | National Eye Institute (nih.gov)
66. Parthasharathi, G. U., Kumar, K. V., Premnivas, R., and Jasmine, K. (2022). Diabetic retinopathy detection using machine learning. Journal of Innovative Image Processing. 4(1), 26-33. Doi:10.36548/jiip.2022.1.003
67. Patel, D., Ananthakrishnan, A., Lin, T., Channa, R., Liu, T.Y.A, Wolf, R.M. (2022). Social determinants of health and impact on screening, prevalence, and management of diabetic retinopathy in adults: A narrative review. Journal of Clinical Medicine, 11(23), 7120. Doi: 10.3390/jcm11237120
68. Poly, T. N., Islam, M., Walther, B. A., Lin, M., C., & Li, Y.-C. (2023). Artificial intelligence in diabetic retinopathy: Bibliometric analysis. Computer Methods and Programs in Biomedicine. 231, Article 107358. https://doi.org/10.1016/j.cmpb.2023.107358
69. Porter, D. (2022). What is fluorescein angiography. American Academy of Ophthalmology. What Is Fluorescein Angiography? - American Academy of Ophthalmology (aao.org)
70. Prasad, R., Floyd, J. L., Dupont, M., Harbour, A., Agyeiwaah, Y. A., Bediako, B. A., Chakraborty, D., Kichler, K., Rohella, A., Calzi, S. L., Lammendella, R., Wright, J., Boulton, M. E., Oudit, G. Y., Raizada, M. K., Stevens, B. R., Li, Q., Grant, M. B. (2023). Maintenance of enteral ACE2 prevents diabetic retinopathy in type 1 diabetes. Circulation Research. 132 (1). https://doi.org/10.1161/CIRCRESAHA.122.322003
71. Qiao, L., Zhu, Y., & Zhou, H. (2020). Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms. IEEE Access, 8, 104292-104302. Doi: 10.1109/ACCESS.2020.2993937
72. Qummar, S., Khan, F. G., Shah, S., Khan, A., Shamshirband, S., Rehman, Z. U., Khan, I. A., and Jadoon, W. (2019). A deep learning ensemble approach for diabetic retinopathy detection. IEEE, 7, 150530-150539. Doi: 10.1109/ACCESS.2019.2947484
73. Regeneron Pharmaceuticals. (2021). About diabetic retinopathy. Now Eye See. Now Eye See | Diabetic Retinopathy
74. Rezazadeh-Gavgani, E., Oladghaffari, M., Bahramian, S., Majidazar, R., & Dolati, S. (2023). MicroRNA-21: A critical underestimated molecule in diabetic retinopathy. Gene, 859. https://doi.org/10.1016/j.gene.2023.147212
75. Romero, L. F. (2016). Diabetes: The current state of affairs from a population management view. MLO: Medical Laboratory Observer, 48(8), 12-20. https://ezp.waldenulibrary.org/login?url=https://search.ebscohost.com/login.aspx?
76. Ruamviboonsuk, P., Tiwari, R., Sayres, R., Nganthavee, V., Hemarat, K., Kongprayoon, A., Raman, R., Levinstein, B., Liu, Y., Schaekermann, M., Lee, R., Virmani, S., Widner, K., Chambers, J., Hersch, F., Peng, L., and Webster, D. (2022). Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. The Lancet Digital Health. 4 (4), e235-e244. https://doi.org/10.1016/S2589-7500(22)00017-6
77. Saadane, A., Veenstra, A. A., Minns, M. S., Tang, J., Du, Y., Elghazali, F. A., Lessieur, E. M., Pearlman, E., & Kern, T. S. (2023). Diabetologia. 66, 590-602. https://doi.org/10.1007/s00125-022-05860-w
78. Setia, M. S. (2016). Methodology series module 3: Cross-sectional studies. Indian Journal of Dermatology, 61(3), 261-264. https://doi.org/10.4103/0019-5154.182410
79. Stanford.edu. (n.d.). Patient Health Questionnaire (PHQ-9). Patient Health Questionnaire (PHQ-9) (stanford.edu)
80. Teo, Z. L., Tham, Y. C., Yu, M., Chee, M. L., Rim, T. H., Cheung, N., Bikbov, M. M., Wang, Y. X., Tang, Y., Lu, Y., Wong, I. Y., Ting, D. S. W., Tan, G. S. W., Jonas, J. B, Sabanayagam, Wong, T. Y., Cheng, C. Y. (2020). Global prevalence of diabetic retinopathy and protection of burden through 2045: Systemic review and meta-analysis. Ophthalmology, 128(11), 1580-1591. https://doi.org/10.1016/j.ophtha.2021.04.027
81. Torpy, J. M., Glass, T. J., Glass, R. M. (2007). Retinopathy. Journal of American Medical Association (JAMA), 298(8), 944. Doi:10.1001/jama.298.8.944
82. Tymchenko, B., Marchenko, P., and Spodarets, D. (2020). Deep learning approach to diabetic retinopathy detection. Cornell University. arXiv. https://doi.org/10.48550/arXiv.2003.02261
83. University of North Carolina. (n.d.). Diabetes questionnaire. Campus Health. diabetes_questionnaire (unc.edu)
84. Vitger, T., Langberg, H., Grabowski, D. (2017). Mutual involvement in families with type 2 diabetes through web-based health care solutions: Quantitative survey study of family preferences, challenges, and potentials. Journal of Medical Internet Research Publication (JMIR), 2(2), 2371-4379. Doi: 10.2196/diabetes.7456
85. Vujosevic, S, Aldington, S. J., Silva, P., Hernandez, C., Scanlon, P., Peto, T., & Simo, R. (2020). Screening for diabetic retinopathy: new perspectives and challenges. The Lancet Diabetes & Endocrinology, 8(4), 337-347. https://doi.org/10.1016/S2213-8587(19)30411-5
86. Wang, M., Yang, Y., Liao, Z. (2020). Diabetes and cancer: Epidemiological and biological links. World Journal of Diabetes. 11(6), 227-238. Doi: 10.4239/wjd.v11.i6.227
87. Winston-Salem State University. (n.d.). Quantitative design. Key Elements of a Research Proposal. Key Elements of a Research Proposal - Quantitative Design (wssu.edu)
88. Zhang, H., Wang, Q., He, S., Wu, K., Ren, M., Dong, H., Di, J., Yu, Z., & Huang, C. (2020). Ambient air pollution and gestational diabetes mellitus: A review of evidence from biological mechanisms to population epidemiology. Science of The Total Environment. 719. https://doi.org/10.1016/j.scitotenv.2020.137349
89. Zheng, D., Li, N., Hou, R., Zhang, X., Wu, L., Sundquist, J., Sundquist, K., & Ji, J. (2023). Glucagon-like peptide-1 receptor agonists and diabetic retinopathy: nationwide cohort and mendelian randomization studies. BMC Medicine, 21 (40). https://doi.org/10.1186/s12916-023-02753-6
90. Zhou, A., Y., Taylor, B., E., Barber, K., G., Lee, C., A., Taylor, Z., R., R., Howell, S., J., Taylor, P., R. (2023). Anti-IL17A halts the onset of diabetic retinopathy in type I and II diabetic mice. International Journal of Molecular Sciences. 24, 1347. https://doi.org/10.3390/ijms24021347