
==== Front
Acta Diabetol
Acta Diabetol
Acta Diabetologica
0940-5429
1432-5233
Springer Milan Milan

38789609
2303
10.1007/s00592-024-02303-3
Original Article
Association between sodium–glucose cotransporter-2 (SGLT2) inhibitors and macular degeneration in patients with diabetes: a nationwide population-based study in Taiwan
Hsu Min-Yen 12
Luo Kai-Shin 12
Chou Chien-Chih 3456
Lin Yu-Hsiang 1
Hung Yu-Chien 12
Chuang Wu-Lung 78
Tsai Stella Chin-Shaw 910
Lin Heng-Jun 1112
Yu Teng-Shun 1112
Tsai Fuu-Jen 13141516
http://orcid.org/0000-0002-4453-0068
Chang Kuang-Hsi kuanghsichang@gmail.com

171819
1 https://ror.org/059ryjv25 grid.411641.7 0000 0004 0532 2041 School of Medicine, Chung Shan Medical University, Taichung City, 402 Taiwan
2 https://ror.org/01abtsn51 grid.411645.3 0000 0004 0638 9256 Department of Ophthalmology, Chung Shan Medical University Hospital, Taichung City, 402 Taiwan
3 https://ror.org/05bqach95 grid.19188.39 0000 0004 0546 0241 Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
4 https://ror.org/00se2k293 grid.260539.b 0000 0001 2059 7017 School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
5 https://ror.org/00e87hq62 grid.410764.0 0000 0004 0573 0731 Department of Ophthalmology, Taichung Veterans General Hospital, Taichung, Taiwan
6 grid.260542.7 0000 0004 0532 3749 Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
7 https://ror.org/05d9dtr71 grid.413814.b 0000 0004 0572 7372 Division of Endocrinology and Metabolism, Department of Internal Medicine, Changhua Christian Hospital, Changhua City, 500 Taiwan
8 Division of Endocrinology and Metabolism, Department of Internal Medicine, Lukang Christian Hospital, Changhua County, 505 Taiwan
9 https://ror.org/0452q7b74 grid.417350.4 0000 0004 1794 6820 Department of Otolaryngology, Tungs’ Taichung MetroHarbor Hospital, Taichung, 435 Taiwan
10 grid.260542.7 0000 0004 0532 3749 Rong Hsing Research Center for Translational Medicine, College of Life Sciences, National Chung Hsing University, Taichung, 402 Taiwan
11 https://ror.org/0368s4g32 grid.411508.9 0000 0004 0572 9415 Management Office for Health Data, China Medical University Hospital, Taichung, 404 Taiwan
12 grid.254145.3 0000 0001 0083 6092 College of Medicine, China Medical University, Taichung City, 404 Taiwan
13 https://ror.org/032d4f246 grid.412449.e 0000 0000 9678 1884 School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung City, 404 Taiwan
14 Department of Medical Research, China Medical University Hospital, China Medical University, Taichung City, 404 Taiwan
15 grid.254145.3 0000 0001 0083 6092 Division of Medical Genetics, China Medical University Children’s Hospital, Taichung City, 404 Taiwan
16 https://ror.org/038a1tp19 grid.252470.6 0000 0000 9263 9645 Department of Biotechnology and Bioinformatics, Asia University, Taichung City, 413 Taiwan
17 https://ror.org/0452q7b74 grid.417350.4 0000 0004 1794 6820 Department of Medical Research, Tungs’ Taichung MetroHarbor Hospital, Taichung City, 435 Taiwan
18 https://ror.org/032d4f246 grid.412449.e 0000 0000 9678 1884 Center for General Education, China Medical University, Taichung City, 404 Taiwan
19 General Education Center, Nursing and Management, Jen-Teh Junior College of Medicine, Miaoli County, 356 Taiwan
Managed By Massimo Federici.

24 5 2024
24 5 2024
2024
61 9 11611168
1 12 2023
3 3 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Aims

Evidence showed that SGLT2 inhibitors have greater protective effects against retinal diseases compared to other hypoglycemic agents. Thus, we explore the association between SGLT2 inhibitor usage and macular degeneration (MD) in Taiwanese patients with diabetes.

Methods

The National Health Insurance (NHI) program's claim data are released as the National Health Insurance Research Database (NHIRD). This database covers more than 99% of the residents in Taiwan. We included data on patients who were newly diagnosed with type 2 diabetes mellitus (ICD-9-CM: 250, exclude 250.1x; ICD-10-CM: E11), with an age at diagnosis of over 20 years as our study population. Patients who received (sodium-glucose cotransporter 2 inhibitor) SGLT2i (ATC code: A10BK) over 90 days in 2016–2019 were defined as the SGLT2i cohort. Conversely, patients who did never received SGLT2i were defined as the non-SGLT2i cohort. The exclusion criteria were having MD before the index date, receiving SGLT2i within 1–89 days, and missing data on sex, age, or days of SGLT2i usage. Two cohorts were matched by 1:1 propensity score matching, which was based on age, sex, payroll bracket grade, urbanization, comorbidities, and medications.

Results

Compared to non-SGLT2i cohort, patients who received SGLT2i had a significantly lower risk of MD (adjusted hazard ratio = 0.70, 95%CI = 0.66–0.75).

Conclusions

We found that SGLT2is has a strong protective effect against MD in patients with diabetes. SGLT2is may have benefits beyond glycemic control in patients with DR. However, additional clinical and experimental studies are required.

Keywords

Sodium-glucose cotransporter-2 (SGLT2)
Macular degeneration (MD)
National Health Insurance Research Database (NHIRD)
Tungs’ Taichung Metro Harbor Hospital research grantTTMHH-R1130010 TTMHH-R1130011 Chang Kuang-Hsi http://dx.doi.org/10.13039/100007225 Ministry of Science and Technology MOHW112-TDU-B-212-144004 Tsai Fuu-Jen http://dx.doi.org/10.13039/501100004391 China Medical University Hospital DMR-111-105 DMR-112-087 DMR-113-009 Tsai Fuu-Jen issue-copyright-statement© Sanofi-Aventis S.p.A. 2024
==== Body
pmcIntroduction

A novel type of drug, sodium-glucose cotransporter-2 (SGLT2) inhibitors, has been approved for diabetes treatment by the U.S. Food and Drug Administration (FDA) since 2014 [1]. SGLT2 is primarily expressed in the proximal convoluted tubule and functions as a reabsorption for glucose [2, 3]. SGLT2 inhibitors block the reabsorption of glucose by inhibiting SGLT2, thereby promoting glucose renal excretion; this lowers the elevated glucose levels of patients with diabetes and produces an osmotic diuretic effect [4].

Compared to other antihyperglycemic agents, SGLT2 inhibitors have a lower risk of hypoglycemia because they only affect the reabsorption of glucose that is filtered rather than blood glucose [5–7]. Moreover, SGLT2 inhibitors have cardiovascular protective effects and renal benefits [8–11]. In addition to cardiovascular and kidney problems, however, macular degeneration (MD) is also a complication among patients with diabetes [12–14]. A former review showed that the global incidence of MD was 1.59 (95% CrI 1.18–2.11) per 100 person-years [15]. A recent study revealed that from 2005 to 2014 [16], 3.75 to 3.95% of Taiwanese patients with diabetes experienced diabetic eye diseases, and 0.29 to 0.35% encountered poor vision and even blindness. A meta-analysis also showed that SGLT2 inhibitors have greater protective effects against retinal diseases compared to other hypoglycemic agents [17]. However, related evidence about SGLT2 inhibitor usage and MD is limited. Thus, we explore the association between SGLT2 inhibitor usage and MD in Taiwanese patients with diabetes.

Methods

Data source

The National Health Insurance (NHI) program of Taiwan was established in 1995, and this program's claim data are released as the National Health Insurance Research Database (NHIRD). This database covers more than 99% of the residents in Taiwan, and diseases such as type 2 diabetes mellitus or MD can be defined by the International Classification of Diseases, 9th Revision, Clinical Modification, and the International Classification of Diseases, 10th Revision (ICD-9-CM and ICD-10-CM). Additionally, therapeutic uses such as drugs for type 2 diabetes mellitus can be defined by the Anatomical Therapeutic Chemical (ATC) code. All analyses were performed in the Health and Welfare Data Center, and privacy policies were implemented to protect the privacy of beneficiaries. The Institutional Review Board (IRB) of China Medical University (CMUH110-REC3-133) has approved this study.

Study population

We included data on patients who were newly diagnosed with type 2 diabetes mellitus (ICD-9-CM: 250, exclude 250.1x; ICD-10-CM: E11), with an age at diagnosis of over 20 years as our study population. Patients who received (sodium-glucose cotransporter 2 inhibitor) SGLT2i (ATC code: A10BK) over 90 days in 2016–2019 were defined as the SGLT2i cohort. Conversely, patients who did never received SGLT2i were defined as the non-SGLT2i cohort. The index date was defined as the first date of receiving SGLT2i in the SGLT2i cohort and as a random date between 2016 and 2019 in the non-SGLT2i cohort. The exclusion criteria were having MD before the index date, receiving SGLT2i within 1 to 89 days, and missing data on sex, age, or days of SGLT2i usage. Two cohorts were matched by 1:1 propensity score matching, which was based on age, sex, payroll bracket grade, urbanization, comorbidities, and medications.

Main outcome and covariates

All study participants were followed from the index date until the onset of MD (ICD-9-CM: 362.5, ICD-10-CM: H35.3), withdrawal from the NHI program, or the end of 2019. The covariates included sex, age group (20–39, 40–59, 60 + years), payroll bracket grade (low, medium, high), urbanization (high, medium, low), comorbidities, and medications. Payroll bracket grade levels were defined as monthly income < 700 USD, 700–1300 USD, and > 1300 USD, respectively.

Comorbidities diagnosed before the index date, including hypertension (ICD-9-CM: 401–405; ICD-10-CM: I10-I13, I15, and N26.2), dyslipidemia (ICD-9-CM: 272; ICD-10-CM: E71.30, E75.21, E75.22, E75.24, E75.3, E75.5, E75.6, E77, E78.0-E78.6, E78.70, E78.79, E78.8, and E78.9), coronary artery disease (CAD) (ICD-9-CM: 410–414; ICD-10-CM: I20-I25), alcoholism (ICD-9-CM: 291, 303, 305.0, 571.0–571.3, 790.3, V11.3, and V79.1; ICD-10-CM: F10, K70, R78.0, and Z65.8), chronic obstructive pulmonary disease (COPD) (ICD-9-CM: 490–496, and 504–506; ICD-10-CM: J40-J47, and J64-J68), diabetic retinopathy (DR) (ICD-9-CM: 362.0; ICD-10-CM: E11.31-E11.35), and macular edema (ICD-9-CM: 362.83; ICD-10-CM: H35.81), were considered.

Eight common antidiabetic drugs, including metformin, sulphonylurea, meglitinides, a-glucosidase inhibitors (AGI), thiazolidinedione, dipeptidyl peptidase 4 inhibitors (DPP4i), glucagon-like peptide-1 receptor agonists (GLP-1RA), and insulin, were considered.

Statistical method

Mean and standard error (SD) were used to perform characteristics of continuous variables such as age and follow-up time, and percentage was used to perform characteristics of category variables. The difference can be evaluated using the standardized mean difference (SMD). The incidence rate was performed per 1,000 person-years (PY) and was calculated by n/PY*1,000. Univariable and multivariable Cox regression models were used to calculate the hazard ratio and the 95% interval estimation of the hazard ratio. The multivariable Cox regression adjusted age, sex, payroll bracket grade, urbanization, comorbidities, and medications. All analyses were performed using SAS version 9.4 (SAS Institute, Inc., Cary, NC, USA), and p values of 0.05 or less were considered statistically significant.

Result

Table 1 presents the demographic characteristics of the two cohorts. There were 147,664 patients matched 1:1 with another 147,664 patients in both cohorts. The SMD shows that age, sex, payroll bracket grade, urbanization, comorbidities, and medications had no significant difference between non-SGLT2i and SGLT2i cohorts. There were approximately 58% male patients among patients with type 2 diabetes mellitus (DM). The major urbanization level was high, with approximately 52%, and the major payroll bracket grade level was medium, with approximately 54%. The top two comorbidities, including hypertension and dyslipidemia, were performed. The proportions of patients receiving medications with and without SGLT2i were as follows: metformin (97.34% versus 97.51%), sulphonylurea (83.63% versus 84.25%), meglitinides (18.06% versus 18.12%), AGI (36.71% versus 35.82%), thiazolidinedione (39.21% versus 37.94%), DPP4i (72.00% versus 71.69%), GLP-1RA (2.02% versus 1.89%), and insulin (40.60% versus 41.55%), respectively. The mean (SD) follow-up times were 2.24 (0.78) years in the study group and 2.21 (0.98) years in the control group.Table 1 Comparison of demographic characteristics and comorbidities between T2DM patients with SGLT2i and controls

Variables	Groups	Non-SGLT2i (N = 147,664)	SGLT2i (N = 147,664)	SMD	
Sex	Male	85,938	58.20	85,689	58.03	0.003	
Age	20–39	9762	6.61	9843	6.67	0.002	
40–59	64,890	43.94	67,161	45.48	0.031	
60 + 	73,012	49.44	70,660	47.85	0.032	
Mean, (SD)	58.55	11.88	58.10	11.71	0.038	
Payroll bracket grade	Low	31,923	21.62	30,221	20.47	0.028	
Medium	79,127	53.59	79,441	53.80	0.004	
High	36,614	24.80	38,002	25.74	0.022	
Urbanization	Low	12,460	8.44	12,625	8.55	0.004	
Medium	57,819	39.16	57,729	39.09	0.001	
High	77,385	52.41	77,310	52.36	0.001	
Comorbidities	Hypertension	112,062	75.89	110,259	74.67	0.028	
Dyslipidemia	123,897	83.90	123,867	83.88	0.001	
CAD	45,307	30.68	44,244	29.96	0.016	
Alcoholism	3571	2.42	3804	2.58	0.010	
COPD	37,916	25.68	37,626	25.48	0.005	
DR	22,339	15.13	22,248	15.07	0.002	
Macular edema	1127	0.76	786	0.53	0.029	
Medication	Metformin	143,986	97.51	143,738	97.34	0.011	
Sulphonylurea	124,413	84.25	123,498	83.63	0.017	
Meglitinides	26,761	18.12	26,661	18.06	0.002	
AGI	52,895	35.82	54,202	36.71	0.018	
Thiazolidinedione	56,030	37.94	57,897	39.21	0.026	
DPP4i	105,865	71.69	106,320	72.00	0.007	
GLP-1RA	2788	1.89	2976	2.02	0.009	
Insulin	61,359	41.55	59,956	40.60	0.019	
Follow-up time	Mean, (SD)	2.21	0.98	2.24	0.78	0.029	
SMD, standardized mean difference; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; DR, diabetic retinopathy; payroll bracket grade, low: < 700 USD, medium: 700–1300 USD, high: > 1300 USD

Table 2 shows the risk of MD according to whether the patients have SGLT2i, comorbidities, or medications. Compared to patients who did not receive SGLT2i, patients who received SGLT2i had a significantly lower risk of MD (adjusted hazard ratio [aHR] = 0.70, 95% CI = 0.66–0.75). For different age groups, we found that patients over 60 had a higher risk of MD compared to those aged 20–39 (aHR = 4.06, 95% CI = 3.17–5.18). There was no significantly different risk of MD between female and male patients. Patients with a payroll bracket grade higher than 700 USD had lower risks of MD than those with a payroll bracket grade lower than 700 USD. Moreover, patients with comorbidities, including CAD (aHR = 1.11, 95% CI = 1.03–1.19), DR (aHR = 2.47, 95% CI = 2.30–2.65), macular edema (aHR = 1.65, 95% CI = 1.32–2.05), as well as those who received medications such as AGI (aHR = 1.16, 95% CI = 1.08–1.24), thiazolidinedione (aHR = 1.09, 95% CI = 1.01–1.16), DPP4i (aHR = 1.12, 95% CI = 1.02–1.22), and insulin (aHR = 1.13, 95% CI = 1.06–1.21) had higher risks of MD compared with the corresponding groups.Table 2 Hazard ratios and 95% confidence intervals of macular degeneration development

Variables	Groups	n	PY	IR	cHR (95% CI)	aHR (95% CI)	
SGLT2i	No	2185	326,735	6.69	1.00	1.00	
Yes	1536	330,514	4.65	0.69 (0.65, 0.74)	0.70 (0.66, 0.75)	
Sex	Female	1772	280,113	6.33	1.00	1.00	
Male	1949	377,136	5.17	0.82 (0.77, 0.87)	0.98 (0.92, 1.04)	
Age	20–39	68	43,545	1.56	1.00	1.00	
40–59	954	296,118	3.22	2.06 (1.61, 2.64)	1.85 (1.44, 2.37)	
	60 + 	2699	317,587	8.50	5.45 (4.28, 6.93)	4.06 (3.17, 5.18)	
Payroll bracket grade	Low	935	137,373	6.81	1.00	1.00	
Medium	1962	353,283	5.55	0.82 (0.75, 0.88)	0.88 (0.82, 0.96)	
	High	824	166,593	4.95	0.73 (0.66, 0.80)	0.90 (0.81, 0.98)	
Urbanization	Low	335	56,000	5.98	1.00	1.00	
Medium	1442	257,064	5.61	0.94 (0.83, 1.06)	0.95 (0.84, 1.07)	
	High	1944	344,185	5.65	0.94 (0.84, 1.06)	0.98 (0.87, 1.10)	
Comorbidities	
Hypertension	No	683	164,022	4.16	1.00	1.00	
Yes	3038	493,227	6.16	1.48 (1.36, 1.61)	1.00 (0.91, 1.09)	
Dyslipidemia	No	575	105,464	5.45	1.00	1.00	
Yes	3146	551,785	5.70	1.04 (0.96, 1.14)	0.92 (0.84, 1.01)	
CAD	No	2299	461,302	4.98	1.00	1.00	
Yes	1422	195,947	7.26	1.46 (1.37, 1.56)	1.11 (1.03, 1.19)	
Alcoholism	No	3665	641,466	5.71	1.00	1.00	
Yes	56	15,783	3.55	0.62 (0.48, 0.81)	0.78 (0.60, 1.02)	
COPD	No	2624	491,705	5.34	1.00	1.00	
Yes	1097	165,544	6.63	1.24 (1.16, 1.33)	1.03 (0.95, 1.10)	
DR	No	2361	556,642	4.24	1.00	1.00	
Yes	1360	100,607	13.52	3.19 (2.98, 3.41)	2.47 (2.30, 2.65)	
Macular edema	No	3637	653,015	5.57	1.00		
Yes	84	4235	19.84	3.57 (2.87, 4.43)	1.65 (1.32, 2.05)	
Medication	
Metformin	No	61	15,422	3.96	1.00	1.00	
Yes	3660	641,827	5.70	1.43 (1.11, 1.84)	0.96 (0.74, 1.25)	
Sulphonylurea	No	378	99,082	3.82	1.00	1.00	
Yes	3343	558,167	5.99	1.56 (1.41, 1.74)	1.02 (0.91, 1.14)	
Meglitinides	No	2845	538,228	5.29	1.00	1.00	
Yes	876	119,021	7.36	1.39 (1.29, 1.50)	1.01 (0.93, 1.09)	
AGI	No	1939	415,606	4.67	1.00	1.00	
Yes	1782	241,644	7.37	1.58 (1.48, 1.68)	1.16 (1.08, 1.24)	
Thiazolidinedione	No	1861	399,786	4.65	1.00	1.00	
Yes	1860	257,463	7.22	1.55 (1.45, 1.65)	1.09 (1.01, 1.16)	
DPP4i	No	730	179,576	4.07	1.00	1.00	
Yes	2991	477,673	6.26	1.54 (1.42, 1.67)	1.12 (1.02, 1.22)	
GLP-1RA	No	3646	645,874	5.65	1.00	1.00	
Yes	75	11,375	6.59	1.18 (0.94, 1.48)	1.11 (0.88, 1.39)	
Insulin	No	1810	388,269	4.66	1.00	1.00	
Yes	1911	268,980	7.10	1.52 (1.43, 1.63)	1.13 (1.06, 1.21)	
n, number of patients with macular degeneration; IR, incidence rate (per 1000 person-years); PY, person-year; cHR, crude hazard ratio; aHR, adjusted hazard ratio; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; DR, diabetic retinopathy; AGI, a-glucosidase inhibitors; payroll bracket grade, low: < 700 USD, medium: 700–1300 USD, high: > 1300 USD

When patients were categorized according to covariates, the aHRs were statistically lower in the SGLT2i group than in the control group, except in alcoholism patients, macular edema patients, without metformin, without sulphonylurea, and with GLP-1RA; the aHR were 0.72 (95% CI = 0.43–1.23), 0.78 (95% CI = 0.50–1.24), 0.71 (95% CI =  0.42–1.18), 0.82 (95% CI = 0.67–1.00), and 0.63 (95% CI = 0.40, 1.01), respectively (Table 3).Table 3 Hazard ratios and 95% confidence intervals of macular degeneration development with and without SGLT2i stratified by age, comorbidities, and medication

Variables	Groups	Non-SGLT2i	SGLT2i	cHR (95%CI)	aHR (95%CI)	
n	PY	IR	n	PY	IR	
Sex	Female	1020	139,243	7.33	752	140,869	5.34	0.72 (0.66, 0.79)	0.74 (0.67, 0.82)	
Male	1165	187,492	6.21	784	189,645	4.13	0.66 (0.60, 0.72)	0.66 (0.61, 0.73)	
Age	20–39	46	21,389	2.15	22	22,156	0.99	0.45 (0.27, 0.75)	0.47 (0.28, 0.78)	
40–59	565	144,789	3.90	389	151,329	2.57	0.66 (0.58, 0.75)	0.65 (0.57, 0.74)	
60 + 	1574	160,557	9.80	1125	157,030	7.16	0.72 (0.67, 0.78)	0.72 (0.67, 0.78)	
Payroll bracket grade	Low	515	69,631	7.40	420	67,742	6.20	0.82 (0.72, 0.94)	0.81 (0.71, 0.93)	
Medium	1182	175,537	6.73	780	177,746	4.39	0.64 (0.59, 0.71)	0.66 (0.60, 0.72)	
High	488	81,567	5.98	336	85,026	3.95	0.66 (0.58, 0.76)	0.69 (0.60, 0.79)	
Urbanization	Low	219	27,974	7.83	116	28,026	4.14	0.53 (0.42, 0.66)	0.56 (0.45, 0.70)	
Medium	849	127,773	6.64	593	129,291	4.59	0.68 (0.61, 0.76)	0.69 (0.62, 0.77)	
High	1117	170,988	6.53	827	173,197	4.77	0.73 (0.66, 0.80)	0.73 (0.67, 0.80)	
Comorbidities	
Hypertension	No	381	79,820	4.77	302	84,202	3.59	0.75 (0.64, 0.87)	0.69 (0.59, 0.80)	
Yes	1804	246,915	7.31	1234	246,312	5.01	0.68 (0.63, 0.73)	0.70 (0.65, 0.75)	
Dyslipidemia	No	314	51,898	6.05	261	53,566	4.87	0.80 (0.67, 0.94)	0.77 (0.65, 0.90)	
Yes	1871	274,837	6.81	1275	276,948	4.60	0.67 (0.63, 0.72)	0.69 (0.64, 0.74)	
CAD	No	1326	228,547	5.80	973	232,755	4.18	0.72 (0.66, 0.78)	0.72 (0.66, 0.78)	
Yes	859	98,188	8.75	563	97,759	5.76	0.65 (0.58, 0.72)	0.67 (0.60, 0.74)	
Alcoholism	No	2156	319,265	6.75	1509	322,201	4.68	0.69 (0.64, 0.73)	0.70 (0.65, 0.75)	
Yes	29	7470	3.88	27	8313	3.25	0.82 (0.49, 1.39)	0.72 (0.43, 1.23)	
COPD	No	1531	244,421	6.26	1093	247,285	4.42	0.70 (0.65, 0.76)	0.71 (0.66, 0.77)	
Yes	654	82,314	7.95	443	83,229	5.32	0.66 (0.58, 0.74)	0.67 (0.60, 0.76)	
DR	No	1398	277,059	5.05	963	279,583	3.44	0.68 (0.62, 0.74)	0.68 (0.63, 0.74)	
Yes	787	49,676	15.84	573	50,931	11.25	0.70 (0.63, 0.78)	0.72 (0.65, 0.80)	
Macular edema	No	2133	324,290	6.58	1504	328,724	4.58	0.69 (0.65, 0.74)	0.70 (0.65, 0.74)	
Yes	52	2445	21.27	32	1790	17.88	0.84 (0.54, 1.30)	0.78 (0.50, 1.24)	
Medication	
Metformin	No	32	7412	4.32	29	8010	3.62	0.83 (0.50, 1.37)	0.71 (0.42, 1.18)	
Yes	2153	319,323	6.74	1507	322,504	4.67	0.69 (0.64, 0.73)	0.70 (0.65, 0.75)	
Sulphonylurea	No	192	48,373	3.97	186	50,709	3.67	0.91 (0.75, 1.12)	0.82 (0.67, 1.00)	
Yes	1993	278,362	7.16	1350	279,805	4.82	0.67 (0.62, 0.72)	0.68 (0.64, 0.73)	
Meglitinides	No	1664	268,391	6.20	1181	269,837	4.38	0.70 (0.65, 0.75)	0.69 (0.64, 0.74)	
Yes	521	58,344	8.93	355	60,677	5.85	0.65 (0.57, 0.74)	0.73 (0.63, 0.83)	
AGI	No	1128	209,166	5.39	811	206,439	3.93	0.72 (0.66, 0.79)	0.72 (0.66, 0.79)	
Yes	1057	117,569	8.99	725	124,075	5.84	0.64 (0.59, 0.71)	0.68 (0.61, 0.74)	
Thiazolidinedione	No	1086	202,087	5.37	775	197,699	3.92	0.72 (0.66, 0.79)	0.72 (0.66, 0.79)	
Yes	1099	124,648	8.82	761	132,816	5.73	0.64 (0.59, 0.71)	0.68 (0.62, 0.74)	
DPP4i	No	432	91,202	4.74	298	88,374	3.37	0.70 (0.61, 0.82)	0.67 (0.57, 0.77)	
Yes	1753	235,533	7.44	1238	242,141	5.11	0.68 (0.63, 0.73)	0.71 (0.66, 0.76)	
GLP-1RA	No	2144	321,941	6.66	1502	323,933	4.64	0.69 (0.65, 0.74)	0.70 (0.66, 0.75)	
Yes	41	4794	8.55	34	6581	5.17	0.58 (0.37, 0.92)	0.63 (0.40, 1.01)	
Insulin	No	1086	194,081	5.60	724	194,188	3.73	0.66 (0.60, 0.73)	0.65 (0.59, 0.72)	
Yes	1099	132,654	8.28	812	136,326	5.96	0.71 (0.65, 0.78)	0.74 (0.67, 0.81)	
n, number of patients with macular degeneration; IR, incidence rate (per 1000 person-years); PY, person-year; cHR, crude hazard ratio; aHR, adjusted hazard ratio; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; DR, diabetic retinopathy; payroll bracket grade, low: < 700 USD, medium: 700–1300 USD, high: > 1300 USD

Discussions

The study investigates the use of SGLT2 inhibitors. SGLT2 inhibitors block the reabsorption of glucose by inhibiting SGLT2, promoting glucose renal excretion, lowering the elevated glucose levels of patients with diabetes, and producing an osmotic diuretic effect [18–20]. This study explored the association between SGLT2 inhibitors in patients with DM and the development of MD. The study used data from the Longitudinal Generation Tracking Database (LGTD), which was derived from a large database of a single-payer NHI program initiated in Taiwan since 1995, called the NHIRD. We aimed to explore the risk of MD in patients with diabetes treated with SGLT2 inhibitors. We found that the incidence of MD was lower in diabetic patients treated with SGLT2 inhibitors than in those treated without SGLT2 inhibitors. This may imply some positive correlation between SGLT2 inhibitors and the prevention of MD.

The mechanisms underlying the protective effect of SGLT2 inhibitors that reduce the risk of MD remain understudied. During the early stage of diabetic retinopathy, pericyte swelling and pericyte loss occur, which results in microaneurysm formation in the retina. The swollen pericytes lose their contractile ability and lead to retinal hyperperfusion [21, 22]. These processes result in a similar clinical observation as the early MD disease sign, edematous macula. The SGLT2 inhibitor has been proven to decrease inflammatory responses and oxidative processes, such as changing the phenotype of astrocytes and microglia into pro-inflammatory ones [22], which also plays a crucial role in MD disease. SGLT2 inhibitors elevate the ratios of adenosine diphosphate/adenosine triphosphate (ATP) and AMP/ATP, leading to a decrease in ATP production and oxidative phosphorylation. Consequently, cellular ATP levels are reduced, which triggers AMPK activation. AMPK activation hinders protein synthesis and cell proliferation. A previous study has shown that metformin exerts its anti-inflammatory and antioxidative effects primarily through the activation of AMP-activated protein kinase (AMPK). This activation leads to the inhibition of nuclear factor-kappa B, a reduction in reactive oxygen species, and the inhibition of the mammalian target of rapamycin. AMPK, a metabolic-sensing Ser/Thr kinase expressed in all cell types, plays a central role in maintaining energy homeostasis and regulating metabolic stress. It also contributes to the dysfunction of the metabolic ecosystem, potentially influencing the pathogenesis of age-related macular degeneration (AMD).

Metformin's protective effects against oxidative damage and mitochondrial dysfunction are considered additional mechanisms for its potential efficacy in AMD treatment [23]. These effects have been confirmed in retinal pigment epithelial (RPE) cells, where metformin stimulates autophagy through the activation of the AMPK pathway, effectively shielding RPE cells from oxidative damage. Furthermore, metformin safeguards photoreceptors and RPE cells from acute injury and delays inherited retinal degeneration by reducing oxidative stress and increasing mitochondrial energy production. Additionally, metformin demonstrates anti-inflammatory properties on the RPE by attenuating pro-inflammatory and adhesion molecule genes. This is particularly relevant in early AMD, characterized by drusen formation, as it results from an inflammatory reaction triggered by RPE damage. Thus, the anti-inflammatory and antioxidant effects of metformin help protect the RPE from early AMD lesions [13, 24]. Thus, in our study, SGLT2 inhibitors showed a similar effect to metformin and resulted in a decreased relative risk of MD in patients with DM.

Subretinal fibrosis and geographic atrophy are challenging clinical situations. Type IV collagen synthesis is increased under high-glucose conditions in bovine pericytes. SGLT2 inhibitors may inhibit mesangial expansion in the glomerulus and microvessel occlusion in the retina (22). Thus, the collagen synthesis modulation could be a positive impact on MD disease prevention.

The study has several strengths, such as the use of a large database, adjustment of potential confounders, and multivariate Cox proportional hazards models. However, the study has some limitations: It is a retrospective cohort study that could not determine the causal relationship between SGLT2 inhibitors and MD. Moreover, the study was conducted in Taiwan; therefore, the results may not be generalizable to other populations. Finally, the Image evaluation and results of hematology test are not available from NHIRD. However, all the patients who need anti-VEFG drugs to treat macular degeneration disease will turn in optical coherence tomography (OCT) image for audited. Similarly, all the patients who need anti-VEFG drugs to treating diabetic macular edema will turn in HbA1C data for audited. The data of HbA1C for first diagnosis should below 10%. Overall, the study suggests that SGLT2 inhibitors may have a protective effect on MD in patients with diabetes.

In conclusion, we found that SGLT2is has a stronger protective effect against MD in patients with diabetes than diuretics. SGLT2is may have benefits beyond glycemic control in patients with DR. Compared with other anti-diabetic drugs, SGLT2 inhibitors show strong ability of anti-inflammatory and antioxidant effects by regulating adenosine diphosphate/adenosine triphosphate (ATP) and AMP/ATP pathway. SGLT2 inhibitor may have a better chance to propone macular degeneration disease process and lead to the protecting effect. However, additional clinical and experimental studies are required.

Acknowledgements

We express our gratitude to the Health Data Science Center at China Medical University Hospital for their generous provision of administrative, technical, and financial assistance.

Author contributions

Conceptualization, K.-H.C., K.-S. L. and M.-Y. H.; methodology, K.-H.C. and T.-S. Y.; software, K.-H.C. and T.-S. Y.; validation, K.-H.C. and M.-Y. H.; formal analysis, K.-H.C. H.-J. L. and T.-S. Y.; investigation, all authors; resources, F.-J. T., and K.-H.C.; data curation, K.-H.C., H.-J. L. and T.-S. Y.; writing—original draft preparation, M.-Y. H., K.-S. L., and K.-H.C.; writing—review and editing, K.-H.C.,C.-C.C.,K.-S. L. and M.-Y. H.; visualization, all authors; supervision, K.-H.C.; project administration, F.-J. T., and K.-H.C; funding acquisition, F.-J. T., and K.-H.C.. All authors have read and agreed to the published version of the manuscript.

Funding

This study is supported in part by Tungs’ Taichung Metro Harbor Hospital research grant (TTMHH-R1130010; TTMHH-R1130011), the Ministry of Science and Technology (MOHW112-TDU-B-212–144004), China Medical University Hospital (DMR-111–105; DMR-112–087; DMR-113–009). The funders had no role in the study design, data collection and analysis, the decision to publish, or the preparation of the manuscript. No additional external funding was received for this study.

Data availability

Information is accessible through the National Health Insurance Research Database (NHIRD) provided by the Taiwan National Health Insurance (NHI) Administration. However, due to legal constraints stipulated by the Taiwan government under the "Personal Information Protection Act," the data cannot be released to the public. If you wish to obtain the data, you may submit formal requests to the NHIRD via their website (https://dep.mohw.gov.tw/DOS/lp-2506-113.html).

Declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

The NHIRD secures patient personal data by employing encryption to safeguard privacy. It also provides researchers with anonymous identification numbers that are associated with relevant claims data, including details such as gender, date of birth, medical services, and prescriptions. As a result, patient consent is not required to access the NHIRD for this research project. This research project received approval for exemption from the Institutional Review Board (IRB) at China Medical University (Approval Number CMUH109-REC2-031 (CR2)), and the IRB explicitly waived the requirement for informed consent. All the procedures were followed by the relevant guidelines and regulations.

Consent to participate

This research project obtained an exemption approval from the Institutional Review Board (IRB) at China Medical University (Approval Number CMUH109-REC2-031 (CR2)), and the IRB explicitly waived the need for consent.

Consent for publication

Not applicable.

Publisher's Note

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

Min-Yen Hsu and Kai-Shin Luo have contributed equally to this article.
==== Refs
References

1. Holden C, Foster B, Panasewicz S, Mehrotra L. US FDA approves INVOKANA™(Canagliflozin) for the treatment of adults with type 2 diabetes.
2. Patel RB (2023) Cardiovascular benefits of sodium-glucose cotransporter 2 inhibitors in heart failure—does the story begin and end with the kidney? JAMA Cardiology.
3. Rizzo MR Di Meo I Polito R Auriemma MC Gambardella A di Mauro G Cognitive impairment and type 2 diabetes mellitus: focus of SGLT2 inhibitors treatment Pharmacol Res 2022 176 106062 10.1016/j.phrs.2022.106062 35017046
Rizzo MR, Di Meo I, Polito R, Auriemma MC, Gambardella A, di Mauro G et al (2022) Cognitive impairment and type 2 diabetes mellitus: focus of SGLT2 inhibitors treatment. Pharmacol Res 176:10606235017046 10.1016/j.phrs.2022.106062
4. Idris I Donnelly R Sodium-glucose co-transporter-2 inhibitors: an emerging new class of oral antidiabetic drug Diabetes Obes Metab 2009 11 79 88 10.1111/j.1463-1326.2008.00982.x 19125776
Idris I, Donnelly R (2009) Sodium-glucose co-transporter-2 inhibitors: an emerging new class of oral antidiabetic drug. Diabetes Obes Metab 11:79–8819125776 10.1111/j.1463-1326.2008.00982.x
5. Yamashita K Yoshiike S Yamashita T Mori JI Aizawa T Reduction of severe hypoglycemic events among outpatients with type 2 diabetes following sodium-glucose cotransporter 2 inhibitor marketing in Japan Horm Metab Res 2022 54 747 753 10.1055/a-1932-0194 36027909
Yamashita K, Yoshiike S, Yamashita T, Mori JI, Aizawa T (2022) Reduction of severe hypoglycemic events among outpatients with type 2 diabetes following sodium-glucose cotransporter 2 inhibitor marketing in Japan. Horm Metab Res 54:747–75336027909 10.1055/a-1932-0194
6. Suzuki D Yamada H Yoshida M Funazaki S Amamoto M Morimoto J Sodium-glucose cotransporter 2 inhibitors improved time-in-range without increasing hypoglycemia in Japanese patients with type 1 diabetes: a retrospective, single-center, pilot study J Diabetes Investig 2020 11 1230 1237 10.1111/jdi.13240 32100964
Suzuki D, Yamada H, Yoshida M, Funazaki S, Amamoto M, Morimoto J et al (2020) Sodium-glucose cotransporter 2 inhibitors improved time-in-range without increasing hypoglycemia in Japanese patients with type 1 diabetes: a retrospective, single-center, pilot study. J Diabetes Investig 11:1230–123732100964 10.1111/jdi.13240
7. Farahani P Nonsevere hypoglycemia episode clinical and economic outcomes: a comparison between sulfonylurea and sodium-glucose cotransporter 2 inhibitor as add-on to metformin from a canadian perspective Int J Endocrinol 2018 2018 3718958 10.1155/2018/3718958 30123259
Farahani P (2018) Nonsevere hypoglycemia episode clinical and economic outcomes: a comparison between sulfonylurea and sodium-glucose cotransporter 2 inhibitor as add-on to metformin from a canadian perspective. Int J Endocrinol 2018:371895830123259 10.1155/2018/3718958
8. Verma S McMurray JJV SGLT2 inhibitors and mechanisms of cardiovascular benefit: a state-of-the-art review Diabetologia 2018 61 2108 2117 10.1007/s00125-018-4670-7 30132036
Verma S, McMurray JJV (2018) SGLT2 inhibitors and mechanisms of cardiovascular benefit: a state-of-the-art review. Diabetologia 61:2108–211730132036 10.1007/s00125-018-4670-7
9. Vallon V Verma S Effects of sglt2 inhibitors on kidney and cardiovascular function Annu Rev Physiol 2021 83 503 528 10.1146/annurev-physiol-031620-095920 33197224
Vallon V, Verma S (2021) Effects of sglt2 inhibitors on kidney and cardiovascular function. Annu Rev Physiol 83:503–52833197224 10.1146/annurev-physiol-031620-095920
10. Waijer SW Vart P Cherney DZI Chertow GM Jongs N Langkilde AM Effect of dapagliflozin on kidney and cardiovascular outcomes by baseline KDIGO risk categories: a post hoc analysis of the DAPA-CKD trial Diabetologia 2022 65 1085 1097 10.1007/s00125-022-05694-6 35445820
Waijer SW, Vart P, Cherney DZI, Chertow GM, Jongs N, Langkilde AM et al (2022) Effect of dapagliflozin on kidney and cardiovascular outcomes by baseline KDIGO risk categories: a post hoc analysis of the DAPA-CKD trial. Diabetologia 65:1085–109735445820 10.1007/s00125-022-05694-6
11. Jardine M Zhou Z Lambers Heerspink HJ Hockham C Li Q Agarwal R Kidney, cardiovascular, and safety outcomes of canagliflozin according to baseline albuminuria: a CREDENCE secondary analysis Clin J Am Soc Nephrol 2021 16 384 395 10.2215/CJN.15260920 33619120
Jardine M, Zhou Z, Lambers Heerspink HJ, Hockham C, Li Q, Agarwal R et al (2021) Kidney, cardiovascular, and safety outcomes of canagliflozin according to baseline albuminuria: a CREDENCE secondary analysis. Clin J Am Soc Nephrol 16:384–39533619120 10.2215/CJN.15260920
12. Chen YY Shen YC Lai YJ Wang CY Lin KH Feng SC Association between metformin and a lower risk of age-related macular degeneration in patients with type 2 diabetes J Ophthalmol 2019 2019 1649156 31781371
Chen YY, Shen YC, Lai YJ, Wang CY, Lin KH, Feng SC et al (2019) Association between metformin and a lower risk of age-related macular degeneration in patients with type 2 diabetes. J Ophthalmol 2019:164915631781371
13. Jiang J Chen Y Zhang H Yuan W Zhao T Wang N Association between metformin use and the risk of age-related macular degeneration in patients with type 2 diabetes: a retrospective study BMJ Open 2022 12 e054420 10.1136/bmjopen-2021-054420 35473747
Jiang J, Chen Y, Zhang H, Yuan W, Zhao T, Wang N et al (2022) Association between metformin use and the risk of age-related macular degeneration in patients with type 2 diabetes: a retrospective study. BMJ Open 12:e05442035473747 10.1136/bmjopen-2021-054420
14. Tseng CH The risk of age-related macular degeneration is reduced in type 2 diabetes patients who use metformin Pharm (Basel) 2023 16 224
Tseng CH (2023) The risk of age-related macular degeneration is reduced in type 2 diabetes patients who use metformin. Pharm (Basel). 16:224.
15. Wang Y Zhong Y Zhang L Wu Q Tham Y Rim TH Global incidence, progression, and risk factors of age-related macular degeneration and projection of disease statistics in 30 years: a modeling study Gerontology 2022 68 721 735 10.1159/000518822 34569526
Wang Y, Zhong Y, Zhang L, Wu Q, Tham Y, Rim TH et al (2022) Global incidence, progression, and risk factors of age-related macular degeneration and projection of disease statistics in 30 years: a modeling study. Gerontology 68:721–73534569526 10.1159/000518822
16. Lin KY Hsih WH Lin YB Wen CY Chang TJ Update in the epidemiology, risk factors, screening, and treatment of diabetic retinopathy J Diabetes Investig 2021 12 1322 1325 10.1111/jdi.13480 33316144
Lin KY, Hsih WH, Lin YB, Wen CY, Chang TJ (2021) Update in the epidemiology, risk factors, screening, and treatment of diabetic retinopathy. J Diabetes Investig 12:1322–132533316144 10.1111/jdi.13480
17. Zhou B Shi Y Fu R Ni H Gu L Si Y Relationship between SGLT-2i and ocular diseases in patients with type 2 diabetes mellitus: a meta-analysis of randomized controlled trials Front Endocrinol (Lausanne) 2022 13 907340 10.3389/fendo.2022.907340 35692406
Zhou B, Shi Y, Fu R, Ni H, Gu L, Si Y et al (2022) Relationship between SGLT-2i and ocular diseases in patients with type 2 diabetes mellitus: a meta-analysis of randomized controlled trials. Front Endocrinol (Lausanne) 13:90734035692406 10.3389/fendo.2022.907340
18. Chao EC Henry RR SGLT2 inhibition—a novel strategy for diabetes treatment Nat Rev Drug Discovery 2010 9 551 559 10.1038/nrd3180 20508640
Chao EC, Henry RR (2010) SGLT2 inhibition—a novel strategy for diabetes treatment. Nat Rev Drug Discovery 9:551–55920508640 10.1038/nrd3180
19. Dardi I Kouvatsos T Jabbour S SGLT2 inhibitors Biochem Pharmacol 2016 101 27 39 10.1016/j.bcp.2015.09.005 26362302
Dardi I, Kouvatsos T, Jabbour S (2016) SGLT2 inhibitors. Biochem Pharmacol 101:27–3926362302 10.1016/j.bcp.2015.09.005
20. Masuda T Muto S Fukuda K Watanabe M Ohara K Koepsell H Osmotic diuresis by SGLT2 inhibition stimulates vasopressin-induced water reabsorption to maintain body fluid volume Physiol Rep 2020 8 e14360 10.14814/phy2.14360 31994353
Masuda T, Muto S, Fukuda K, Watanabe M, Ohara K, Koepsell H et al (2020) Osmotic diuresis by SGLT2 inhibition stimulates vasopressin-induced water reabsorption to maintain body fluid volume. Physiol Rep 8:e1436031994353 10.14814/phy2.14360
21. Wakisaka M Nagao T Sodium glucose cotransporter 2 in mesangial cells and retinal pericytes and its implications for diabetic nephropathy and retinopathy Glycobiology 2017 27 691 695 10.1093/glycob/cwx047 28535208
Wakisaka M, Nagao T (2017) Sodium glucose cotransporter 2 in mesangial cells and retinal pericytes and its implications for diabetic nephropathy and retinopathy. Glycobiology 27:691–69528535208 10.1093/glycob/cwx047
22. Pawlos A Broncel M Woźniak E Gorzelak-Pabiś P Neuroprotective effect of SGLT2 inhibitors Molecules 2021 26 7213 10.3390/molecules26237213 34885795
Pawlos A, Broncel M, Woźniak E, Gorzelak-Pabiś P (2021) Neuroprotective effect of SGLT2 inhibitors. Molecules 26:721334885795 10.3390/molecules26237213
23. Brown EE Ball JD Chen Z Khurshid GS Prosperi M Ash JD The common antidiabetic drug metformin reduces odds of developing age-related macular degeneration Invest Ophthalmol Vis Sci 2019 60 1470 1477 10.1167/iovs.18-26422 30973575
Brown EE, Ball JD, Chen Z, Khurshid GS, Prosperi M, Ash JD (2019) The common antidiabetic drug metformin reduces odds of developing age-related macular degeneration. Invest Ophthalmol Vis Sci 60:1470–147730973575 10.1167/iovs.18-26422
24. Blitzer AL Ham SA Colby KA Skondra D Association of metformin use with age-related macular degeneration: a case-control study JAMA Ophthalmol 2021 139 302 309 10.1001/jamaophthalmol.2020.6331 33475696
Blitzer AL, Ham SA, Colby KA, Skondra D (2021) Association of metformin use with age-related macular degeneration: a case-control study. JAMA Ophthalmol 139:302–30933475696 10.1001/jamaophthalmol.2020.6331
