Table 1.

miRNA	Primer (5′–3′)
hsa-miR-26a-5p	GSP:5′GGGTTCAAGTAATCCAGG3′ R:5′TGCGTGTCGTGGAGTC3′
hsa-miR-126-3p	GSP:5′GGGGTCGTACCGTGAGTAAT3′ R:5′GTGCGTGTCGTGGAGTCG3′
hsa-miR-378a-3p	GSP:5′GGGGTCTGGACTTGGAGTCA3′ R:5′GTGCGTGTCGTGGAGTCG3′
hsa-miR-146b-5p	GSP:5′GGGGTGAGAACTGAATTCCA3′ R:5′GTGCGTGTCGTGGAGTCG3′
hsa-miR-128-3p	GSP:5′GGGGAATCACAGTGAACCG3′ R:5′GTGCGTGTCGTGGAGTCG3′
hsa-miR-185-5p	GSP:5′GGTGGAGAGAAAGGCAGT3′ R:5′TGCGTGTCGTGGAGTC3′
hsa-miR-199a-5p	GSP:5′GGTGCCCAGTGTTCAGAC3′ R:5′CAGTGCGTGTCGTGGAGT3′
hsa-let-7a-5p	GSP:5′GGGGGTGAGGTAGTAGGTTGT3′ R:5′GTGCGTGTCGTGGAGTCG3′
hsa-miR-93-5p	GSP:5′GGCAAAGTGCTGTTCGTG3′ R:5′CAGTGCGTGTCGTGGAGT3′

Table 1. The sequences of primers used for qRT-PCR.

Table 2.

	Group 1 (NDR)	Group 2 (mild NPDR)	Group 3 (healthy controls)	F	P
Participants ( n )	33	33	20	–	–
Gender (M/W)	13/10	12/11	11/9	–	–
Age (yrs)	54.03 ± 13.25	55.12 ± 14.09	52.35 ± 15.77	0.237	0.789
Diabetes duration (yrs)	7.62 ± 6.21	11.37 ± 4.26*	–	2.861	0.005
BMI (kg/m 2)	24.72 ± 3.01	24.83 ± 3.27	23.19 ± 2.42	2.164	0.121
HbA1c (%)	7.19 ± 2.15***	7.94 ± 2.65***	4.14 ± 1.36	19.27	0.0001
Total cholesterol (mmol/l)	4.16 ± 1.20	4.22 ± 1.13	4.13 ± 1.12	0.042	0.958
Triglyceride (mmol/l)	2.24 ± 1.57	2.08 ± 1.26	2.15 ± 1.17	0.113	0.893
LDL-C (mmol/l)	2.51 ± 0.93	2.54 ± 0.85	2.48 ± 0.67	0.032	0.968
HDL-C (mmol/l)	1.29 ± 0.44	1.22 ± 0.31	1.26 ± 0.24	0.325	0.723
AL of right eye (mm)	23.39 ± 0.61	23.36 ± 0.48	23.35 ± 0.75	0.033	0.966
AL of left eye (mm)	23.48 ± 0.60	23.42 ± 0.57	23.34 ± 0.58	0.359	0.699
IOP of right eye (mmHg)	19.87 ± 3.96	18.44 ± 3.70	19.02 ± 3.51	1.203	0.305
IOP of left eye (mmHg)	19.74 ± 3.68	18.99 ± 4.67	19.76 ± 4.05	0.335	0.715

Table 2. Comparisons of the characteristics of patients according to DR status.

Data represented the mean ± standard deviation (SD) of each group. For diabetes duration, * represents comparisons between groups 1 and 2. For HbA1c, * represents the comparisons of group 1 or 2 to group 3. *** P < 0.001.

DR diabetic retinopathy, M / W man/woman, BMI body mass index, HbA1c glycated haemoglobin, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, RNFL retinal nerve fibre layer, ACR urinary albumin-to-creatinine ratio, AL axial length, IOP intraocular pressure.

Statistically significant p -values are in bold.

Table 3.

Dysregulation	miRNA	PRE-ACC	miRNA expression (rpkm)		Fold change	P-valuee
Dysregulation	miRNA	PRE-ACC	NPDR	NDR	Fold change	P-valuee
Up-regulated)	hsa-miR-185-5p	MI0000482	56,518.5	23,555	2.399	0.0071
Up-regulated	hsa-miR-378a-3p	MI0000786	6602	2820	2.341	0.0037
Down-regulated	hsa-miR-199a-5p	MI0000242	354.5	1151	0.308	0.0279
Down-regulated	hsa-miR-128-3p	MI0000447	405.5	1902.5	0.213	0.0068
Down-regulated	hsa-let-7a-5p	MI0000060	4430	7599.5	0.583	0.0361
Down-regulated	hsa-miR-146b-5p	MI0003129	1630	3919	0.416	0.0280
Down-regulated	miR-26a-5p	MI0000083	3569.5	18,071.5	0.198	0.0162
Down-regulated	hsa-miR-126-3p	MI0000471	13,511	57,936	0.233	0.0002

Table 3. Eight DR-related dysregulated miRNAs detected by miRNA sequencing.

PRE-ACC the pre-miRNA ACCESSION, NPDR non-proliferative diabetic retinopathy, NDR no diabetic retinopathy.

Table 4.

Eyes	Group 1(>n = 33)	> Group 2(n = 33)	Group 3 n = 20)	F	P
Right eye
Averagep	96.44 ± 10.36	97.98 ± 15.61	99.83 ± 12.88	0.417	0.6605
Superior quadrant	117.24 ± 19.88 #	102.52 ± 21.24**	128.77 ± 18.69	11.140	0.0001
Inferior quadrant	128.54 ± 18.21	122.72 ± 25.64	131.42 ± 17.46	1.190	0.3094
Nasal quadrant	75.33 ± 13.50	74.16 ± 17.44	76.38 ± 20.91	0.111	0.8954
Temporal quadrant	69.56 ± 19.36	71.88 ± 25.73	73.22 ± 18.40	0.199	0.8244
Left eye
Averagep	99.57 ± 19.71	95.89 ± 17.46	103.88 ± 21.40	1.080	0.3443
Superior quadrant	122.51 ± 12.48	109.63 ± 27.61*	127.29 ± 28.39	4.336	0.0160
Inferior quadrant	129.28 ± 25.18	131.73 ± 19.46	130.55 ± 17.20	0.108	0.8976
Nasal quadrant	80.62 ± 22.41	75.79 ± 29.67	79.28 ± 22.05	0.313	0.7325
Temporal quadrant	71.50 ± 12.69	68.66 ± 20.92	74.88 ± 16.33	0.834<	0.4380

Table 4. Peripapillary RNFL thickness of the subjects.

Data represented mean ± standard deviation (SD) of each group; * represents comparisons of group 1 or 2 to group 3. # represents comparisons between group 1 and 2. * P < 0.05, ** P < 0.001, # P < 0.05. P < 0.05 was considered statistically significant.
RNFL retinal nerve fibre layer, DR diabetic retinopathy.
Statistically significant p-values are in bold.

Table 5.

Differential miRNAs	Right eye					Left eye
Differential miRNAs	Superior	Inferior	Temporal	Nasal	Average	Superior	Inferior	Temporal	Nasal	Average
hsa-miR-185-5p	0.038	−0.251	−0.233	−0.302	−0.208	0.011	0.251	0.302	0.237	−0.250
P value	0.713	0.300	0.336	0.208	0.059	0.966	0.300	0.208	0.339	0.062
hsa-miR-378a-3p	−0.233	−0.069	0.072	−0.324	0.190	−0.274	0.070	0.120	−0.103	0.009
P value	0.054	0.571	0.491	0.162	0.097	0.121	0.522	0.447	0.256	0.85
hsa-miR-199a-5p	0.049	0.069	0.051	−0.195	−0.163	0.480	−0.274	0.224	0.097	0.113
P value	0.760	0.770	0.662	0.614	0.792	0.135	0.510	0.482	0.066	0.291
hsa-miR-128-3p	0.178	−0.092	−0.113	0.019	0.274	0.075	−0.018	−0.196	0.019	0.120
P value	0.008	0.391	0.291	0.853	0.121	0.481	0.868	0.067	0.866	0.219
hsa-let-7a-5p	0.093	−0.075	−0.173	0.075	−0.138	0.048	0.289	0.076	0.105	0.020
P value	0.383	0.485	0.110	0.490	0.195	0.651	0.865	0.479	0.323	0.846
hsa-miR-146b-5p	0.072	0.104	0.004	−0.143	−0.131	0.099	0.010	0.188	0.015	0.102
P value	0.502	0.349	0.966	0.201	0.243	0.472	0.901	0.103	0.904	0.212
miR-26a-5p	0.616	−0.216	−0.088	0.150	−0.022	0.651	−0.121	−0.131	0.069	−0.004
P value	*0.000	0.050	0.422	0.181	0.834	*0.000	0.205	0.243	0.517	0.967
hsa-miR-126-3p	−0.010	−0.096	0.107	−0.048	−0.201	−0.207	−0.025	0.102	−0.092	−0.169
P value	0.924	0.372	0.319	0.651	0.069	0.066	0.810	0.341	0.391	0.115

Table 5. Correlation analysis of RNFL thickness with differential miRNA expression.

Pearson correlation analysis was performed to analyse the association between RNFL thinning and differentially expressed miRNAs. * P < 0.05 was defined as statistically significant.
RNFL retinal nerve fibre layer.

Statistically significant p-values are in bold.

Table 6.

Category	Term	P-value	Gene counts
GO-CC	Actin filament (GO:0005884)	0.000533	6
	Platelet dense granule membrane (GO:0031088)	0.006565	2
	RNA polymerase II transcription factor complex (GO:0090575)	0.01909	7
	Mitochondrial outer membrane (GO:0005741)	0.019699	6
	Actin cytoskeleton (GO:0015629)	0.020151	11
	Polymeric cytoskeletal fibre (GO:0099513)	0.021144	9
	Transcription factor TFIID complex (GO:0005669)	0.027601	3
	Cytoskeleton (GO:0005856)	0.030491	16
	Peroxisomal membrane (GO:0005778)	0.038572	3
	Nucleolus (GO:0005730)	0.042895	19
GO-MF	Protein serine/threonine kinase activity (GO:0004674)	4.75E−06	21
	Kinase activity (GO:0016301)	1.76E−05	17
	Protein kinase activity (GO:0004672)	0.000209	22
	Kinase binding (GO:0019900)	0.000275	19
	Protein kinase binding (GO:0019901)	0.000848	20
	Phosphotransferase activity, alcohol group as acceptor (GO:0016773)	0.000868	13
	Mannosyl-oligosaccharide mannosidase activity (GO:0015924)	0.000892	3
	Ubiquitin-like protein-specific protease activity (GO:0019783)	0.002478	6
	Phosphatidylinositol-4,5-bisphosphate binding (GO:0005546)	0.005077	5
	Signal sequence binding (GO:0005048)	0.005941	4

Table 6. Top 10 predominant CC and MF terms in GO functional enrichment analysis.

GO gene ontology, CC cellular component, MF molecular function.

Stitt AW, Curtis TM, Chen M, Medina RJ, McKay GJ, Jenkins A, et al. The progress in understanding and treatment of diabetic retinopathy. Prog Retin Eye Res. 2016;51:156–86.
Liu F, Saul AB, Pichavaram P, Xu Z, Rudraraju M, Somanath PR, et al. Pharmacological inhibition of spermine oxidase reduces neurodegeneration and improves retinal function in diabetic mice. J Clin Med 2020;9:340.
Sohn EH, van Dijk HW, Jiao C, Kok PH, Jeong W, Demirkaya N, et al. Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus. Proc Natl Acad Sci USA. 2016;113:E2655–E2664.
Simo R, Hernandez C. Novel approaches for treating diabetic retinopathy based on recent pathogenic evidence. Prog Retin Eye Res. 2015;48:160–80.
Simo R, Stitt AW, Gardner TW. Neurodegeneration in diabetic retinopathy: does it really matter? Diabetologia. 2018;61: 1902–12.
Sampedro J, Bogdanov P, Ramos H, Sola-Adell C, Turch M, Valeri M, et al. New insights into the mechanisms of action of topical administration of GLP-1 in an experimental model of diabetic retinopathy. J Clin Med 2019;8:339.
Hafner J, Zadrazil M, Grisold A, Ricken G, Krenn M, Kitzmantl D, et al. Retinal and corneal neurodegeneration and their association with systemic signs of peripheral neuropathy in type 2 diabetes. Am J Ophthalmol. 2020;209:197–205.
Bogdanov P, Simo-Servat O, Sampedro J, Sola-Adell C, Garcia-Ramirez M, Ramos H, et al. Topical administration of bosentan prevents retinal neurodegeneration in experimental diabetes. Int J Mol Sci 2018;19:3578.
Choi JA, Ko SH, Park YR, Jee DH, Ko SH, Park CK. Retinal nerve fiber layer loss is associated with urinary albumin excretion in patients with type 2 diabetes. Ophthalmolology. 2015;122:976–81.
Nadri G, Saxena S, Stefanickova J, Ziak P, Benacka J, Gilhotra JS, et al. Disorganization of retinal inner layers correlates with ellipsoid zone disruption and retinal nerve fiber layer thinning in diabetic retinopathy. J Diabetes Complicat. 2019;33:550–3.
Liu L, Wang Y, Liu HX, Gao J. Peripapillary region perfusion and retinal nerve fiber layer thickness abnormalities in diabetic retinopathy assessed by OCT angiography. Transl Vis Sci Technol. 2019;8:14.
Obeid A, Su D, Patel SN, Uhr JH, Borkar D, Gao X, et al. Outcomes of eyes lost to follow-up with proliferative diabetic retinopathy that received panretinal photocoagulation versus intravitreal anti-vascular endothelial growth factor. Ophthalmolology. 2019;126:407–13.
Shafabakhsh R, Aghadavod E, Mobini M, Heidari-Soureshjani R, Asemi Z. Association between microRNAs expression and signaling pathways of inflammatory markers in diabetic retinopathy. J Cell Physiol. 2019;234:7781–7.
Willeit P, Skroblin P, Moschen AR, Yin X, Kaudewitz D, Zampetaki A, et al. Circulating MicroRNA-122 is associated with the risk of new-onset metabolic syndrome and type 2 diabetes. Diabetes. 2017;66:347–57.
Gandhi R, Healy B, Gholipour T, Egorova S, Musallam A, Hussain MS, et al. Circulating microRNAs as biomarkers for disease staging in multiple sclerosis. Ann Neurol. 2013;73: 729–40.
Platania C, Maisto R, Trotta MC, D’Amico M, Rossi S, Gesualdo C, et al. Retinal and circulating miRNA expression patterns in diabetic retinopathy: an in silico and in vivo approach. Br J Pharmacol. 2019;176:2179–94.
Mammadzada P, Bayle J, Gudmundsson J, Kvanta A, Andre H. Identification of diagnostic and prognostic microRNAs for recurrent vitreous hemorrhage in patients with proliferative diabetic retinopathy. J Clin Med 2019;8:2217.
Liu HN, Cao NJ, Li X, Qian W, Chen XL. Serum microRNA-211 as a biomarker for diabetic retinopathy via modulating Sirtuin 1. Biochem Biophys Res Commun. 2018;505:1236–43.
Singh RP, Elman MJ, Singh SK, Fung AE, Stoilov I. Advances in the treatment of diabetic retinopathy. J Diabetes Complicat. 2019;33:107417.
Zampetaki A, Willeit P, Burr S, Yin X, Langley SR, Kiechl S, et al. Angiogenic microRNAs linked to incidence and progression of diabetic retinopathy in Type 1 diabetes. Diabetes. 2016;65: 216–27.
Jeon SJ, Park HL, Lee JH, Park CK. Relationship between systemic vascular characteristics and retinal nerve fiber layer loss in patients with type 2 diabetes. Sci Rep. 2018;8:10510.
Shi R, Guo Z, Wang F, Li R, Zhao L, Lin R. Alterations in retinal nerve fiber layer thickness in early stages of diabetic retinopathy and potential risk factors. Curr Eye Res. 2018;43:244–53.
Shi R, Zhao L, Qi Y. The effect of fenofibrate on early retinal nerve fiber layer loss in type 2 diabetic patients: a case-control study. BMC Ophthalmol. 2018;18:100.
Joglekar MV, Januszewski AS, Jenkins AJ, Hardikar AA. Circulating microRNA biomarkers of diabetic retinopathy. Diabetes. 2016;65:22–24.
Farr RJ, Januszewski AS, Joglekar MV, Liang H, McAulley AK, Hewitt AW, et al. A comparative analysis of high-throughput platforms for validation of a circulating microRNA signature in diabetic retinopathy. Sci Rep. 2015;5:10375.
Pusparajah P, Lee LH, Abdul KK. Molecular markers of diabetic retinopathy: potential screening tool of the future? Front Physiol. 2016;7:200.
Chen Q, Qiu F, Zhou K, Matlock HG, Takahashi Y, Rajala R, et al. Pathogenic role of microRNA-21 in diabetic retinopathy through downregulation of PPARalpha. Diabetes. 2017;66:1671–82.
Han N, Tian W, Yu N, Yu L. YAP1 is required for the angiogenesis in retinal microvascular endothelial cells via the inhibition of MALAT1-mediated miR-200b-3p in high glucose-induced diabetic retinopathy. J Cell Physiol. 2020;235:1309–20.
Li Z, Dong Y, He C, Pan X, Liu D, Yang J, et al. RNA-Seq revealed novel non-proliferative retinopathy specific circulating MiRNAs in T2DM patients. Front Cenet. 2019;10:531.
Xing X, Guo S, Zhang G, Liu Y, Bi S, Wang X, et al. miR-26a-5p protects against myocardial ischemia/reperfusion injury by regulating the PTEN/PI3K/AKT signaling pathway. Braz J Med Biol Res. 2020;53:9106.

CME / ABIM MOC

Plasma miR-26a-5p Is a Biomarker for Retinal Neurodegeneration of Early Diabetic Retinopathy

Authors: Rui Shi, MD; Li Chen, MD; Weirong Wang, MD; Ying Deng, MD; YiZhen Liu, MD; Haiyan Zhou, MD; Rong Lin, MD, PhD
CME / ABIM MOC Released: 4/30/2021
THIS ACTIVITY HAS EXPIRED FOR CREDIT
Valid for credit through: 4/30/2022

Start Activity

Target Audience and Goal Statement

This activity is intended for ophthalmologists, endocrinologists, diabetologists, internists, and other clinicians caring for patients with diabetic retinopathy (DR).

The goal of this activity is to describe circulating microRNA (miRNA) targets and mechanisms underlying the pathogenic process of DR, according to a bioinformatic analysis of data from of 86 participants, including 33 patients with diabetes but without DR (group 1), 33 patients with diabetes with mild nonproliferative DR (group 2) and 20 healthy control participants (group 3).

Upon completion of this activity, participants will:

Describe plasma microRNAs (miRNAs) validated with qualitative reverse-transcriptase polymerase chain reaction in patients with type 2 diabetes with or without diabetic retinopathy (DR), and their correlation with neurodegeneration, according to retinal nerve fiber layer (RNFL) thickness measured using spectral-domain optical coherence tomography
Determine gene targets of miRNA and mechanisms underlying the pathogenic process of DR, according to bioinformatic analysis used to predict potential targets of miRNA associated with RNFL thickness and to investigate the functions of the potential target genes
Identify clinical implications of bioinformatic analysis of miRNAs in DR and their gene targets

Disclosures

As an organization accredited by the ACCME, Medscape, LLC, requires everyone who is in a position to control the content of an education activity to disclose all relevant financial relationships with any commercial interest. The ACCME defines "relevant financial relationships" as financial relationships in any amount, occurring within the past 12 months, including financial relationships of a spouse or life partner, that could create a conflict of interest.

Medscape, LLC, encourages Authors to identify investigational products or off-label uses of products regulated by the US Food and Drug Administration, at first mention and where appropriate in the content.

Faculty

Rui Shi, MD

Department of Pharmacology
School of Basic Medical Sciences
Xi'an Jiaotong University Health Science Center
Department of Ophthalmology
Shaanxi Provincial People’s Hospital
Xi’an, Shaanxi, China

Disclosures

Disclosure: Rui Shi, MD, disclosed has no relevant financial relationships.

Li Chen, MD

Department of Neurology
Shaanxi Provincial People’s Hospital
Xi’an, Shaanxi, China

Disclosures

Disclosure: Li Chen, MD, disclosed has no relevant financial relationships.

Weirong Wang, MD

Department of Medical Laboratory Animal Science
School of Basic Medical Sciences
Xi’an Jiaotong University Health Science Center
Xi’an, Shaanxi, China

Disclosures

Disclosure: Weirong Wang, MD, disclosed has no relevant financial relationships.

Ying Deng, MD

Department of Pharmacology
School of Basic Medical Sciences
Xi'an Jiaotong University Health Science Center
Xi’an, Shaanxi, China

Disclosures

Disclosure: Ying Deng, MD, disclosed has no relevant financial relationships.

YiZhen Liu, MD

Department of Pharmacology
School of Basic Medical Sciences
Xi'an Jiaotong University Health Science Center
Xi’an, Shaanxi, China

Disclosures

Disclosure: YiZhen Liu, MD, disclosed has no relevant financial relationships.

Haiyan Zhou, MD

Department of Ophthalmology
Shaanxi Provincial People’s Hospital
Xi’an, Shaanxi, China

Disclosures

Disclosure: Haiyan Zhou, MD, disclosed has no relevant financial relationships.

Rong Lin, MD, PhD

Department of Pharmacology
School of Basic Medical Sciences
Xi'an Jiaotong University Health Science Center
Xi'an, Shaanxi, China

Disclosures

Disclosure: Rong Lin, MD, PhD, disclosed has no relevant financial relationships.

CME Author

Laurie Barclay, MD

Freelance writer and reviewer
Medscape, LLC

Disclosures

Disclosure: Laurie Barclay, MD, has disclosed no relevant financial relationships.

Editor

Sobha Sivaprasad, MD

Editor, Eye

Disclosures

Disclosure: Sobha Sivaprasad, MD, has disclosed the following relevant financial relationships:
Served as an advisor or consultant for: Allergan, Inc.; Apellis; Bayer AG; Boehringer Ingelheim Pharmaceuticals, Inc.; Heidelberg Pharma GmbH; Novartis Pharmaceuticals Corporation; Oculis; Optos; Oxurion; Roche
Served as a speaker or a member of a speakers bureau for: Allergan, Inc.; Bayer AG; Novartis Pharmaceuticals Corporation; Optos
Received grants for clinical research from: Allergan, Inc.; Bayer AG; Boehringer Ingelheim Pharmaceuticals, Inc.; Novartis Pharmaceuticals Corporation; Optos

CME Reviewer

Stephanie Corder, ND, RN, CHCP

Associate Director, Accreditation and Compliance Medscape, LLC

Disclosures

Disclosure: Stephanie Corder, ND, RN, CHCP, has disclosed no relevant financial relationships.

CE Reviewer

Hazel Dennison, DNP, RN, FNP-BC, CHCP, CPHQ, CNE

Associate Director, Accreditation and Compliance
Medscape, LLC

Disclosures

Disclosure: Hazel Dennison, DNP, RN, FNP-BC, CHCP, CPHQ, CNE, has disclosed no relevant financial relationships.

Medscape, LLC staff have disclosed that they have no relevant financial relationships.

Accreditation Statements

In support of improving patient care, this activity has been planned and implemented by Medscape, LLC and Springer Nature. Medscape, LLC is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.

For Physicians

Medscape, LLC designates this Journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™ . Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to 1.0 MOC points in the American Board of Internal Medicine's (ABIM) Maintenance of Certification (MOC) program. Participants will earn MOC points equivalent to the amount of CME credits claimed for the activity. It is the CME activity provider's responsibility to submit participant completion information to ACCME for the purpose of granting ABIM MOC credit.

Contact This Provider

For questions regarding the content of this activity, contact the accredited provider for this CME/CE activity noted above. For technical assistance, contact [email protected]

Instructions for Participation and Credit

There are no fees for participating in or receiving credit for this online educational activity. For information on applicability and acceptance of continuing education credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within the time designated on the title page; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity online during the valid credit period that is noted on the title page. To receive AMA PRA Category 1 Credit™, you must receive a minimum score of 70% on the post-test.

Follow these steps to earn CME/CE credit*:

Read about the target audience, learning objectives, and author disclosures.
Study the educational content online or print it out.
Online, choose the best answer to each test question. To receive a certificate, you must receive a passing score as designated at the top of the test. We encourage you to complete the Activity Evaluation to provide feedback for future programming.

You may now view or print the certificate from your CME/CE Tracker. You may print the certificate, but you cannot alter it. Credits will be tallied in your CME/CE Tracker and archived for 6 years; at any point within this time period, you can print out the tally as well as the certificates from the CME/CE Tracker.

*The credit that you receive is based on your user profile.

From Eye

CME / ABIM MOC

Plasma miR-26a-5p Is a Biomarker for Retinal Neurodegeneration of Early Diabetic Retinopathy

Authors: Rui Shi, MD; Li Chen, MD; Weirong Wang, MD; Ying Deng, MD; YiZhen Liu, MD; Haiyan Zhou, MD; Rong Lin, MD, PhDFaculty and Disclosures

THIS ACTIVITY HAS EXPIRED FOR CREDIT

CME / ABIM MOC Released: 4/30/2021

Valid for credit through: 4/30/2022

processing....

Abstract and Introduction

Abstract

Purpose Retinal neurodegeneration is an early pathological change in diabetic retinopathy (DR). Early-stage retinal neurodegeneration is usually asymptomatic. This study aims to identify circulating microRNAs (miRNAs) as sensitive biomarkers for early retinal neurodegeneration.
Methods We profiled the plasma miRNA expression in three mild nonproliferative diabetic retinopathy (NPDR) cases and three matched non-DR patients using RNA sequencing. The differential miRNAs were validated with qRT-PCR. The retinal nerve fibre layer (RNFL) thickness of the eyes was measured using spectral-domain Optical coherence tomography (SD-OCT). The association between differential miRNAs and RNFL thickness was analysed using the Pearson correlation analysis. Bioinformatics tools were used to predict potential targets of miRNA associated with RNFL thickness and investigate the functions of the potential target genes.
Results RNA sequencing identified 69 differential miRNAs and eight of them were reported to be associated with DR. The qRT-PCR for these eight miRNAs validated the down-regulation of circulating miR-26a-5p and miR-126-5p in a larger validating cohort. A positive correlation between plasma miR-26a-5p level and the RNFL thickness of the superior quadrant of both eyes was identified in another cohort, including 33 mild NPDR cases, 33 matched non-DR patients and 20 healthy controls. Furthermore, 367 candidate targets of miR-26a-5p were predicted. The functional studies revealed that these target genes are profoundly involved in various cellular functions and signalling pathways.
Conclusions Circulating miR-26a-5p is a potential biomarker for early-stage retinal neurodegeneration and it may be involved in the development of DR via profoundly influencing the functions of retinal cells.

Introduction

Diabetic retinopathy (DR) is a multifactorial progressive disease of the retina, which has been considered a leading cause of irreversible blindness in patients with diabetes mellitus ^[1]. The activation of the retinal glial cells, apoptotic cell death, neuroinflammation and gradual neurodegeneration induced by hyperglycaemic environment play important roles in the pathogenesis of DR ^[2]. Concomitantly with the development of diabetes, the metabolic disorder results in the abnormalities of the microvascular, breakdown of the blood–retinal barrier, impairments in neurovascular interactions, central vision loss, proliferative retinopathy, macular oedema and retinal layer thinning [^[3,4]. These changes ultimately lead to progression into advanced DR. Many studies in recent years have paid attention to retinal neurodegeneration, which is the earliest pathology and may precede microvascular changes that are characteristic of DR^[5-8]. It has been proven that the retinal nerve fibre layer (RNFL) thinning is a manifestation of diabetic optic neuropathy ^[9-11]. However, the potential molecular mechanisms underlying these pathologies remain elusive. Besides, although current therapies, such as laser photocoagulation and anti-vascular endothelial growth factor (VEGF) injections, are available for late stages of DR with proliferation and macular oedema, effective strategies still lacked to prevent or reduce vision loss in the fture ^[12]. Therefore, it is imperative to identify novel biomarkers and therapeutic targets to improve the therapeutic effects of early DR.

MicroRNAs (miRNAs) are small non-coding RNA molecules that orchestrate biological networks via modulation of protein expression by interfering with the translation and stability of mRNA. Some miRNAs are released into the circulation and can exist stably in plasma, urine, and other body fluids ^[13]. The availability of quantitative detection of circulating miRNAs enhances the use of miRNAs as biomarkers for physiological and pathological processes ^[14,15].Numerous clinical and experimental studies have demonstrated the important roles of miRNAs in the development of diabetes and its complications ^[16]. Additionally, accumulating evidence has indicated that various circulating miRNAs can serve as potential biomarkers or therapeutic targets for DR ^[17,18].However, the relationship between plasma miRNA and diabetic neurodegeneration remains unclear. We speculate that circulating miRNAs may play an important role in the pathogenesis of retinal neurodegeneration in patients with or without DR.

In the present study, we screened the plasma miRNAs in type 2 diabetic patients with or without DR and investigated the neurodegeneration-related miRNAs for correlation analysis. Then we explored the targets of miRNA and elucidated the underlying mechanism in mediating the pathogenic process of DR by bioinformatic analysis.

Page 1 of 5

Materials and Methods

CME / ABIM MOC Information

Abstract and Introduction
Materials and Methods
Results
Discussion
Summary

Disclaimer

The educational activity presented above may involve simulated, case-based scenarios. The patients depicted in these scenarios are fictitious and no association with any actual patient, whether living or deceased, is intended or should be inferred. The material presented here does not necessarily reflect the views of Medscape, LLC, or any individuals or commercial entities that support companies that support educational programming on medscape.org. These materials may include discussion of therapeutic products that have not been approved by the US Food and Drug Administration, off-label uses of approved products, or data that were presented in abstract form. These data should be considered preliminary until published in a peer-reviewed journal. Readers should verify all information and data before treating patients or employing any therapies described in this or any educational activity. A qualified healthcare professional should be consulted before using any therapeutic product discussed herein.

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

Table 6.

References

Faculty and Disclosures

Faculty

Rui Shi, MD

Li Chen, MD

Weirong Wang, MD

Ying Deng, MD

YiZhen Liu, MD

Haiyan Zhou, MD

Rong Lin, MD, PhD

CME Author

Laurie Barclay, MD

Editor

Sobha Sivaprasad, MD

CME Reviewer

Stephanie Corder, ND, RN, CHCP

CE Reviewer

Hazel Dennison, DNP, RN, FNP-BC, CHCP, CPHQ, CNE

CME / ABIM MOC

Plasma miR-26a-5p Is a Biomarker for Retinal Neurodegeneration of Early Diabetic Retinopathy

Target Audience and Goal Statement

Disclosures

Faculty

Rui Shi, MD

Li Chen, MD

Weirong Wang, MD

Ying Deng, MD

YiZhen Liu, MD

Haiyan Zhou, MD

Rong Lin, MD, PhD

CME Author

Laurie Barclay, MD

Editor

Sobha Sivaprasad, MD

CME Reviewer

Stephanie Corder, ND, RN, CHCP

CE Reviewer

Hazel Dennison, DNP, RN, FNP-BC, CHCP, CPHQ, CNE

Accreditation Statements

For Physicians

Instructions for Participation and Credit

Plasma miR-26a-5p Is a Biomarker for Retinal Neurodegeneration of Early Diabetic Retinopathy

Abstract and Introduction

Abstract

Introduction

Table of Contents