Sage Journals: Discover world-class research

Abstract

Summary

This study is the first large-scale, head-to-head comparison suggesting that sodium-glucose cotransporter-2 inhibitors (SGLT2is) may offer greater neuroprotection against Parkinson's disease (PD) compared to metformin in patients with type 2 diabetes mellitus (T2DM). Utilizing a 20-year real-world dataset and propensity score matching, we found that SGLT2i users had a 28% lower adjusted hazard ratio (aHR) for PD (0.72; 95% CI, 0.62–0.84) and reduced all-cause mortality. Unlike previous studies suggesting a potential increased PD risk with SGLT2is, our robust study design, stringent exclusion criteria, and competing risk adjustments support a protective association. The findings highlight the need for further prospective research to explore the neuroprotective benefits of SGLT2is, which may justify prioritizing their use in T2DM patients at risk for neurodegeneration.

Background

Type 2 diabetes mellitus (T2DM) is linked to an increased risk of Parkinson's disease (PD), likely mediated by insulin resistance, inflammation, and mitochondrial dysfunction. While metformin has shown neuroprotective effects, sodium-glucose cotransporter-2 inhibitors (SGLT2is) have emerging benefits in neurodegeneration. This study provides the first real-world head-to-head comparison of SGLT2is and metformin on PD risk in T2DM patients.

Methods

Using the TriNetX platform, we analyzed a 20-year dataset (2005–2025) from 142 healthcare organizations, identifying 913,428 T2DM patients (96,018 SGLT2i, 817,410 metformin users). Patients with prior PD, neurodegenerative diseases, or exposure to neuroprotective/neurotoxic antidiabetic drugs were excluded. Propensity score matching (1:1) balanced cohorts across demographic, clinical, and pharmacological variables. Cox proportional hazards models estimated adjusted hazard ratios (aHRs), validated by positive and negative controls.

Results

SGLT2i use was associated with a 28% lower PD risk than metformin (aHR = 0.72; 95% CI, 0.62–0.84; p < 0.0001). Dementia, a positive control, also showed reduced risk (aHR = 0.73; 95% CI, 0.68–0.78; p < 0.0001), reinforcing the neuroprotective effect. Negative controls confirmed specificity. SGLT2i users had significantly lower all-cause mortality (aHR = 0.85; 95% CI, 0.83–0.89; p < 0.0001).

Conclusions

This first large-scale comparison suggests SGLT2is provide superior neuroprotection against PD compared to metformin in T2DM patients, warranting further investigation.

Plain Language Summary

Can Diabetes Medications Help Lower the Risk of Parkinson's Disease?

Parkinson's disease is a brain condition that affects movement and is more likely to occur in people with type 2 diabetes. Some diabetes medications may offer protection for the brain, but it's not clear which ones work better. In this large real-world study using electronic health records from over 900,000 patients, we compared two common diabetes drugs: SGLT2 inhibitors and metformin. We found that people who took SGLT2 inhibitors had a significantly lower risk of developing Parkinson's disease compared to those who took metformin. This protective effect was especially clear in older adults, people with poor blood sugar control, and those with kidney problems. The results remained strong even after adjusting for many other health conditions, medications, and lifestyle factors such as smoking and alcohol use. Importantly, SGLT2 inhibitor users also lived longer, which could have affected how often Parkinson's disease was diagnosed. However, we accounted for this in our analysis and still found a consistent benefit. These findings suggest that SGLT2 inhibitors might not only help control blood sugar but also protect the brain in people with type 2 diabetes. More research is needed to understand how these drugs work on the brain and whether they should be considered as first-line treatment for patients at risk of neurodegenerative diseases like Parkinson's.

Keywords

type 2 diabetes mellitus parkinson's disease sodium-glucose cotransporter-2 inhibitors metformin real-world study

Introduction

Type 2 diabetes mellitus (T2DM) is a growing global health burden, affecting millions and increasing the risk of neurodegenerative diseases, including Parkinson's disease (PD).^1–5 Epidemiological studies have consistently demonstrated that T2DM is associated with an elevated PD risk, particularly in younger patients, those with longer diabetes duration, and individuals with more advanced disease.^4,6,7 The pathophysiological connection between T2DM and PD is supported by evidence from epidemiological studies, as well as in vivo rodent models and in vitro neuronal studies, implicating insulin resistance, oxidative stress, neuroinflammation, mitochondrial dysfunction, ferroptosis, and α-synuclein aggregation as shared biological mechanisms.^1,8,9 Notably, methylglyoxal, a reactive glycolytic byproduct and potent endogenous glycating agent, accumulates in patients with T2DM and may promote MPTP-like neurotoxicity, thereby contributing to dopaminergic neurodegeneration.^1,8,9 Given the rising prevalence of both conditions, addressing this relationship is a public health priority.^8–11

PD imposes a substantial socioeconomic burden, leading to significant healthcare costs, productivity loss, and declining quality of life.^12,13 Strategies to mitigate PD risk in T2DM patients include optimal glycemic control and the potential repurposing of antidiabetic medications with neuroprotective properties.^14–16 Metformin lowers blood glucose primarily by suppressing hepatic gluconeogenesis and improving peripheral insulin sensitivity.¹⁷ These metabolic effects may indirectly reduce systemic inflammation, oxidative stress, and mitochondrial dysfunction—mechanisms also implicated in neurodegeneration.¹⁸ Metformin has also been hypothesized to confer neuroprotective benefits through AMP-activated protein kinase (AMPK) activation, enhancement of mitochondrial function, and modulation of neuroinflammation and α-synuclein aggregation.^19–21 However, observational studies examining the association between metformin use and PD risk have yielded mixed results. Ping et al. and Qin et al. reported increased PD risk with metformin monotherapy (OR 1.66 and HR 1.50, respectively),^22,23 potentially linked to long-term vitamin B12 deficiency or reverse causality. In contrast, Wahlqvist et al. observed a reduced PD risk among metformin users (HR 0.62),²⁴ and several other studies have demonstrated an inverse dose-response association, suggesting even lower cumulative exposures may confer neuroprotection.^25,26 These inconsistent findings may reflect a non-linear, dose-dependent relationship.^23,25 Low cumulative exposure (<300 defined daily doses) has been associated with reduced PD risk,²⁵ while higher cumulative doses or prolonged use may attenuate benefits or increase risk.^23,25 Biologically, mild AMPK activation at lower doses may promote neuroprotection,^21,25,27 whereas prolonged exposure could lead to B12 depletion and mitochondrial complex I inhibition, both detrimental to neuronal health.^23,25,27 Further studies are needed to clarify metformin's dose-response dynamics in neurodegenerative outcomes.

Sodium-glucose cotransporter-2 inhibitors (SGLT2is) are a newer class of antidiabetic agents with cardiometabolic benefits beyond glucose control.^28,29 By inhibiting renal glucose reabsorption, they improve glycemic control, reduce body weight, and lower blood pressure.^28,29 Recent observational studies and meta-analyses suggest that SGLT2is may also confer neuroprotective effects, particularly in lowering the risk of dementia and cognitive decline among patients with T2DM.^30–35 These findings provide a rationale for evaluating their impact on PD, a neurodegenerative disorder with overlapping pathophysiologic pathways. Proposed mechanisms for neuroprotection include improved cerebral perfusion, suppression of neuroinflammation, enhanced mitochondrial function, and reduced oxidative stress.^{11,28,30,32,36} SGLT2is may also activate autophagy to clear α-synuclein aggregates and promote ketone body utilization as alternative neuronal fuels.^37,38 Given metformin's established but inconsistent associations with PD risk,^{21,24–27,39–43} we conducted a retrospective cohort study to compare the incidence of PD among patients with T2DM who initiated SGLT2is versus metformin using a large real-world dataset. Our objective was to evaluate whether SGLT2is provide additional neuroprotective benefits relative to metformin.

Methods

Data source

This study utilized the TriNetX platform, a global federated health research network aggregating de-identified electronic health records (EHRs) from 142 healthcare organizations (HCOs) worldwide.⁴⁴ The dataset includes demographics, diagnoses, lab results, medications, procedures, and genomic data while adhering to HIPAA and GDPR privacy regulations to ensure patient anonymity.^44–46

A cohort of 913,428 T2DM patients was identified, comprising 96,018 SGLT2i users and 817,410 metformin users, spanning January 20, 2005, to January 20, 2025. The dataset, covering hospitals, primary care, and specialty clinics, provided extensive real-world data for robust epidemiological analysis and rigorous evaluation of clinical outcomes across different antidiabetic treatments.^44,45 The analysis was conducted using the TriNetX Analytics Platform on February 26, 2025, at 06:33:24 UTC, based on de-identified real-world data from 142 healthcare organizations worldwide.

Study population and design

This study included adults (≥18 years) with type 2 diabetes mellitus (T2DM; ICD-10-CM: E11) who were diagnosed between January 20, 2005, and January 20, 2025, and initiated treatment with either an SGLT2 inhibitor or metformin without prior exposure to either drug. To ensure data completeness, all patients were required to have at least 12 months of continuous clinical records prior to the index date, defined as the date of the first prescription. Individuals without sufficient baseline data prior to the end of the study period were excluded.

To minimize confounding, we excluded individuals with prior PD (ICD-10-CM: G20), secondary parkinsonism (G21), or anti-parkinsonian drug use (ATC: N04). Patients with neurodegenerative diseases (Alzheimer's, vascular dementia, or other dementias), substance-induced cognitive impairment, or prior glucagon-like peptide-1 receptor agonists (GLP-1 RAs, ATC code A10BJ), or dipeptidyl peptidase-4 inhibitors (DPP-4i, ATC code A10BH) exposure were also excluded due to potential neuroprotective or neurotoxic effects.

Exposure definitions

Patients newly prescribed SGLT2i (A10BK) or metformin (A10BA) were assigned to Cohort A and Cohort B, respectively. To ensure an incident user design, those with prior SGLT2i or metformin exposure or who switched treatments during follow-up were excluded. To minimize confounding, individuals with prior or concurrent GLP-1 RA or DPP-4i use were excluded, while first-generation antidiabetic agents (e.g., sulfonylureas, thiazolidinediones, alpha-glucosidase inhibitors, insulins) were adjusted for using propensity score matching (PSM) (Table 1). To ensure adequate medication exposure, patients required ≥1 year of continuous prescriptions with no extended refill gaps. An extended refill gap was defined as a period exceeding 90 consecutive days without prescription renewal after the expected coverage of the previous prescription. Those with only a single prescription and no continued use were excluded to prevent misclassification of short-term users.

Table 1.

Baseline characteristics of patients receiving SGLT2 inhibitors or metformin and the risk of Parkinson's disease, before and after propensity score matching.

	Before PSM					After PSM
	SGLT2i use		Metformin use		ASMD	SGLT2i use		Metformin use		ASMD
	N = 94,952		N = 814,501			N = 91,080		N = 91,080
	N	%	N	%		N	%	N	%
Index Age (Mean ± SD)	66.42	12.28	60.55	13.32	0.4580	66.08	12.27	66.24	11.69	0.0133
Sex
Male	55,151	58.08%	411,483	50.52%	0.1523	52,619	57.77%	52,659	57.82%	0.0009
Female	36,321	38.25%	381,418	46.83%	0.1741	35,137	38.58%	34,958	38.38%	0.0040
Unknown/Unspecified Gender	3480	3.67%	21,600	2.65%	0.0580	3324	3.65%	3463	3.80%	0.0081
Ethnicity
White	53,257	56.09%	450,828	55.35%	0.0149	51,235	56.25%	52,193	57.31%	0.0212
Black or African American	18,861	19.86%	151,020	18.54%	0.0336	17,815	19.56%	16,863	18.51%	0.0266
Native Hawaiian or Pacific Islander	5264	5.54%	54,029	6.63%	0.0456	5086	5.58%	5003	5.49%	0.0040
American Indian or Alaska Native	969	1.02%	8737	1.07%	0.0051	931	1.02%	923	1.01%	0.0009
Asian	307	0.32%	2850	0.35%	0.0046	292	0.32%	300	0.33%	0.0015
Other	3057	3.22%	35,920	4.41%	0.0622	2975	3.27%	2925	3.21%	0.0031
Unknown/Unspecified	13,237	13.94%	111,117	13.64%	0.0087	12,746	13.99%	12,873	14.13%	0.0040
Comorbidities
Hypertension and related cardiovascular conditions	69,786	73.50%	476,120	58.46%	0.3215	66,098	72.57%	65,023	71.39%	0.0263
Metabolic disorders	64,016	67.42%	421,725	51.78%	0.3229	60,402	66.32%	59,255	65.06%	0.0265
Other forms of cardiovascular disease	52,474	55.26%	157,416	19.33%	0.8004	48,621	53.38%	48,774	53.55%	0.0034
Ischemic heart disease	41,082	43.27%	127,446	15.65%	0.6358	37,537	41.21%	37,624	41.31%	0.0019
Overweight, obesity, and other hyperalimentation disorders	25,363	26.71%	154,403	18.96%	0.1855	23,606	25.92%	23,125	25.39%	0.0121
Sleep disorders	19,428	20.46%	91,840	11.28%	0.2534	17,751	19.49%	17,271	18.96%	0.0134
History of nicotine dependence	16,610	17.49%	63,179	7.76%	0.2963	14,900	16.36%	14,675	16.11%	0.0067
Cerebrovascular diseases	12,050	12.69%	63,924	7.85%	0.1600	11,309	12.42%	11,156	12.25%	0.0051
Current nicotine dependence	10,450	11.01%	73,322	9.00%	0.0668	9939	10.91%	9652	10.60%	0.0102
Neurological and musculoskeletal symptoms and disorders	10,519	11.08%	55,230	6.78%	0.1511	9697	10.65%	9478	10.41%	0.0078
Gout	6050	6.37%	21,716	2.67%	0.1791	5127	5.63%	4944	5.43%	0.0088
Head injuries and traumatic brain injury (TBI)	3724	3.92%	26,593	3.27%	0.0353	3499	3.84%	3374	3.70%	0.0072
Tobacco use	3471	3.66%	18,647	2.29%	0.0805	3227	3.54%	3095	3.40%	0.0079
Alcohol use disorders	3273	3.45%	22,177	2.72%	0.0419	3071	3.37%	2928	3.22%	0.0088
Other psychoactive substance use disorders	912	0.96%	6201	0.76%	0.0216	843	0.93%	813	0.89%	0.0035
Purine and pyrimidine metabolism disorders	926	0.98%	2105	0.26%	0.0916	690	0.76%	680	0.75%	0.0013
Hyperuricemia without inflammatory arthritis or tophaceous disease	916	0.97%	2056	0.25%	0.0917	680	0.75%	671	0.74%	0.0012
Other stimulant-related disorders	725	0.76%	3083	0.38%	0.0511	645	0.71%	605	0.66%	0.0053
Exposure to hazardous, primarily non-medicinal chemicals	206	0.22%	800	0.10%	0.0299	184	0.20%	167	0.18%	0.0043
Exposure to environmental tobacco smoke (acute and chronic)	220	0.23%	626	0.08%	0.0395	177	0.19%	169	0.19%	0.0020
Exposure to other hazardous, primarily non-medicinal chemicals	197	0.21%	730	0.09%	0.0306	175	0.19%	157	0.17%	0.0046
Psychotropic drug toxicity	138	0.15%	1102	0.14%	0.0027	130	0.14%	138	0.15%	0.0023
Physical inactivity and sedentary lifestyle	70	0.07%	216	0.03%	0.0211	54	0.06%	61	0.07%	0.0031
Toxic effects of metals	24	0.03%	154	0.02%	0.0043	22	0.02%	18	0.02%	0.0030
Toxic effects of pesticides	10	0.01%	46	0.01%	0.0054	10	0.01%	10	0.01%	0.0000
Exposure to hazardous metals	10	0.01%	67	0.01%	0.0024	10	0.01%	10	0.01%	0.0000
Exposure to hazardous aromatic compounds	10	0.01%	10	0.00%	0.0121	10	0.01%	0	0.00%	0.0148
Occupational exposure to toxic agents in agricultural settings	10	0.01%	19	0.00%	0.0102	10	0.01%	10	0.01%	0.0000
Occupational exposure to toxic agents in industrial settings	12	0.01%	36	0.00%	0.0089	10	0.01%	10	0.01%	0.0000
Pregnancy tobacco use	10	0.01%	660	0.08%	0.0330	10	0.01%	15	0.02%	0.0047
Poisoning, adverse effects, and underdosing of caffeine	10	0.01%	164	0.02%	0.0078	10	0.01%	10	0.01%	0.0000
Toxic effects of organic solvents	0	0.00%	35	0.00%	0.0093	0	0.00%	10	0.01%	0.0148
Occupational exposure to vibration	0	0.00%	0	0.00%	0.0000	0	0.00%	0	0.00%	0.0000
Medications
Cardiovascular Medications
Statins (HMG-CoA reductase inhibitors)	45,041	47.44%	222,381	27.30%	0.4255	41,817	45.91%	41,456	45.52%	0.0080
Alpha-blockers (α-Adrenergic antagonists)	1382	1.46%	5871	0.72%	0.0709	1229	1.35%	1218	1.34%	0.0010
Beta-blockers	44,422	46.78%	182,014	22.35%	0.5317	40,969	44.98%	40,603	44.58%	0.0081
Terazosin (α1-Adrenergic antagonist)	452	0.48%	2624	0.32%	0.0244	431	0.47%	421	0.46%	0.0016
Neurological & Psychiatric Medications
Antipsychotics	9653	10.17%	61,787	7.59%	0.0908	9022	9.91%	8946	9.82%	0.0028
Lithium	10	0.01%	205	0.03%	0.0110	10	0.01%	13	0.01%	0.0029
Selective serotonin reuptake inhibitors (SSRIs)	8161	8.60%	55,058	6.76%	0.0690	7678	8.43%	7517	8.25%	0.0064
Non-selective monoamine reuptake inhibitors (NSMRIs)	1803	1.90%	13,607	1.67%	0.0172	1718	1.89%	1721	1.89%	0.0002
Monoamine oxidase inhibitors (MAOIs, non-selective)	10	0.01%	20	0.00%	0.0100	10	0.01%	10	0.01%	0.0000
Other antidepressants	10,785	11.36%	58,833	7.22%	0.1428	9862	10.83%	9804	10.76%	0.0021
Pain & Migraine Medications
Opioids	34,897	36.75%	219,705	26.97%	0.2110	32,718	35.92%	32,543	35.73%	0.0040
Antimigraine drugs	3093	3.26%	18,379	2.26%	0.0612	2849	3.13%	2893	3.18%	0.0028
Antidiabetic Medications
Insulins and analogues	36,282	38.21%	215,266	26.43%	0.2539	34,052	37.39%	35,084	38.52%	0.0234
Sulfonylureas	5300	5.58%	38,467	4.72%	0.0389	5084	5.58%	5353	5.88%	0.0127
Alpha-glucosidase inhibitors	120	0.13%	717	0.09%	0.0117	112	0.12%	119	0.13%	0.0022
Thiazolidinediones	1024	1.08%	6597	0.81%	0.0278	974	1.07%	997	1.10%	0.0024
Other blood glucose-lowering drugs (excluding insulins)	1003	1.06%	3082	0.38%	0.0804	880	0.97%	937	1.03%	0.0063
Laboratory,
Glomerular filtration rate, mean (SD), mL/min/1.73 m²	63.78 (29.40)		83.89 (29.56)		0.6823	70.12 (29.37)		70.42 (28.83)		0.0090
Body Mass Index (BMI), mean (SD), kg/m²	31.67 (7.73)		32.49 (7.83)		0.1055	31.74 (7.75)		31.88 (7.74)		0.0183
Hemoglobin A1c Levels (%), mean (SD)	7.43 (1.88)		7.73 (2.12)		0.1541	7.46 (1.88)		7.61 (1.99)		0.0222

Abbreviations: SGLT2i – Sodium-Glucose Cotransporter-2 Inhibitor, PSM – Propensity Score Matching, ASMD – Absolute Standardized Mean Difference, SD – Standard Deviation, BMI – Body Mass Index, HbA1c – Hemoglobin A1c, HTN – Hypertension, CVD – Cardiovascular Disease, IHD – Ischemic Heart Disease, TBI – Traumatic Brain Injury, ETS – Environmental Tobacco Smoke, NSMRIs – Non-Selective Monoamine Reuptake Inhibitors, MAOIs – Monoamine Oxidase Inhibitors, SSRIs – Selective Serotonin Reuptake Inhibitors

Outcome measures

The primary outcome was the first recorded diagnosis of PD (ICD-10-CM: G20, G21), while all-cause mortality was analyzed as a secondary outcome to account for competing risks. Since SGLT2i users may have lower mortality rates, failing to adjust for this could underestimate their potential neuroprotective effect against PD.

To minimize reverse causation, patients diagnosed with PD within six months of treatment initiation were excluded. A total of 302 patients were excluded on this basis from the initial cohort to minimize the impact of reverse causality and immortal time bias. Follow-up began on day 181 post-index and continued until PD diagnosis, death, loss to follow-up, or study completion.

To validate our findings, we included dementia (ICD-10-CM: F01, F02, F03, G30) as a biologically plausible positive control, given its overlapping pathophysiology with PD and emerging evidence of reduced dementia risk associated with SGLT2i use in patients with type 2 diabetes.^34,47 Negative controls included appendicitis, hematologic malignancies, common cold, burn injuries, and suicidal ideation—conditions with no plausible link to antidiabetic medications or PD risk. Additionally, a negative exposure control (vitamin C use)^48,49 was assessed to rule out systematic biases in healthcare utilization and prescribing patterns. We acknowledge that over-the-counter use of vitamin C may not be comprehensively captured; however, its documentation in health system pharmacies or provider orders allows partial visibility within the TriNetX network. Vitamin C was used as a supplementary exposure control, rather than a definitive comparator, to help detect potential recording or utilization biases.

Statistical analysis

To minimize confounding and ensure comparability between treatment groups, we performed 1:1 nearest-neighbor PSM with a caliper of 0.2 standard deviations. Propensity scores were estimated using logistic regression incorporating a comprehensive set of covariates (Table 1), including demographic factors (age, sex, race/ethnicity), comorbidities (e.g., hypertension, ischemic heart disease, stroke), glycemic and renal markers (HbA1c, BMI, eGFR), and relevant medication use. All covariates listed in Table 1 were included in the model. Covariates were selected a priori based on clinical and epidemiologic evidence regarding PD risk factors and antidiabetic medication use, rather than through data-driven methods, to minimize overfitting and avoid collider bias. Multicollinearity was assessed using variance inflation factors (VIF), with no significant collinearity observed.⁵⁰ Patients with missing data on key covariates were excluded from the analysis. All continuous variables were summarized as mean ± standard deviation (SD), and categorical variables as counts and percentages. A complete-case approach was adopted due to the large sample size, which preserved statistical power while minimizing potential imputation bias. Neuroactive drugs (e.g., statins, beta-blockers, opioids, antipsychotics, benzodiazepines, antidepressants), environmental exposures (e.g., tobacco, alcohol, toxic agents), psychiatric disorders, and history of traumatic brain injury were also included as covariates due to their associations with PD risk.

Covariate balance was confirmed using absolute standardized mean differences (ASMDs), all <0.1 post-matching. To assess model robustness, we performed inverse probability of treatment weighting (IPTW), which yielded consistent results. Cox proportional hazards regression was used to estimate adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs), with Schoenfeld residuals confirming the proportional hazards assumption. Cox models are particularly suited for long-term follow-up studies as they incorporate the dimension of time, estimating the instantaneous risk (hazard) of an outcome at any point during follow-up. This allows for more accurate modeling of chronic conditions like PD, which may develop years after initial exposure. In contrast, relative risks (RRs) were calculated as a simple measure of cumulative incidence and do not account for time-to-event or censoring. While RRs can provide an intuitive comparison of overall event proportions between groups, they are not intended for causal inference in the context of time-to-event data. To address potential reverse causation arising from the prodromal phase of PD, we conducted sensitivity analyses excluding patients diagnosed with PD within 6, 12, and 24 months after treatment initiation. We also performed competing risk analysis using Fine-Gray models and conducted subgroup analyses stratified by age, sex, glycemic control, renal function, and medication use. Positive and negative control outcomes were evaluated to detect potential residual confounding.

All statistical tests were two-sided, with significance set at p < 0.05. To account for multiple comparisons in secondary and exploratory analyses, the Benjamini-Hochberg false discovery rate correction was applied.

Results

Baseline characteristics of the study cohort

Table 1 summarizes the baseline characteristics of patients receiving SGLT2i or metformin, both before and after PSM. Before matching, the SGLT2i group was older (66.42 ± 12.28 vs. 60.55 ± 13.32 years; ASMD = 0.4580) and had higher comorbidity burdens, including hypertension (73.50% vs. 58.46%; ASMD = 0.3215), ischemic heart disease (43.27% vs. 15.65%; ASMD = 0.6358), metabolic disorders (67.42% vs. 51.78%; ASMD = 0.3229), and cerebrovascular disease (12.69% vs. 7.85%; ASMD = 0.1600). They also had higher nicotine dependence (17.49% vs. 7.76%; ASMD = 0.2963) and sleep disorders (20.46% vs. 11.28%; ASMD = 0.2534). Before matching, males comprised 58.08% of the SGLT2i group and 50.52% of the metformin group (ASMD = 0.1523). After PSM, sex distribution was well balanced (57.77% vs. 57.82% male; ASMD = 0.0009).

After PSM, a well-balanced cohort was achieved (ASMD < 0.1 for all variables), with 91,080 patients per group. The matched mean age was similar (66.08 ± 12.27 vs. 66.24 ± 11.69 years; ASMD = 0.0133), along with comorbidities such as hypertension (72.57% vs. 71.39%; ASMD = 0.0263), ischemic heart disease (41.21% vs. 41.31%; ASMD = 0.0019), and metabolic disorders (66.32% vs. 65.06%; ASMD = 0.0265).

Risk of PD and all-cause mortality

The incidence of PD was 240 out of 91,080 (0.26%) in the SGLT2i group and 772 out of 91,080 (0.85%) in the metformin group. Among PD cases, the mean time from treatment initiation to diagnosis was 987.4 ± 603.2 days for SGLT2i users and 944.7 ± 591.5 days for metformin users. For the primary outcome, the incidence of PD was significantly lower in the SGLT2i group compared to the metformin group (0.26% vs. 0.85%) (Table 2). The AHR for PD in SGLT2i users was 0.72 (95% CI, 0.62–0.84), indicating a 28% lower risk compared to metformin users. The RR was 0.31 (95% CI, 0.27–0.36), confirming a substantial reduction in PD incidence.

Table 2.

Risk of Parkinson's disease and all-cause mortality in patients receiving SGLT2 inhibitors vs. metformin after propensity score matching.

Outcome	Patients with Outcome, No./Total No. (%)		AHR (95% CI)	Risk Ratio (95% CI)
Outcome	SGLT-2i Group	Metformin Group	AHR (95% CI)	Risk Ratio (95% CI)
Primary Outcome
Parkinson's Disease	240/91,080 (0.26%)	772/ 91,080 (0.85%)	0.72 (0.62–0.84)	0.31 (0.27–0.36)
Secondary Outcomes
All-Cause Mortality	7381/91,080 (8.10%)	16,535/91,080 (18.15%)	0.85 (0.83–0.89)	0.45 (0.43–0.46)

Abbreviations: SGLT2i – Sodium-Glucose Cotransporter-2 Inhibitor, PSM – Propensity Score Matching, AHR – Adjusted Hazard Ratio, CI – Confidence Interval

For the secondary outcome, all-cause mortality was significantly lower in the SGLT2i group compared to the metformin group (8.10% vs. 18.15%). The AHR for all-cause mortality was 0.85 (95% CI, 0.83–0.89), suggesting a 15% reduction in mortality risk among SGLT2i users. The corresponding RR was 0.45 (95% CI, 0.43–0.46), further supporting the survival benefit associated with SGLT2i treatment.

These findings highlight a potential neuroprotective effect of SGLT2is, with a lower risk of PD and a reduced competing risk of mortality compared to metformin.

Sensitivity analysis of reverse causation

In sensitivity analyses excluding PD diagnoses occurring within 6, 12, and 24 months after treatment initiation, the adjusted hazard ratios (aHRs) remained consistent: 0.72 (95% CI, 0.62–0.84), 0.73 (95% CI, 0.61–0.86), and 0.71 (95% CI, 0.58–0.87), respectively, supporting the robustness of the observed association. Full results are provided in Supplemental Table 1.

Kaplan-Meier analysis of PD incidence and overall survival

Figure 1 shows that PD incidence was significantly lower in the SGLT2i group than in the metformin group, with the survival curves diverging early and continuing to separate over time. The log-rank test confirmed a statistically significant difference, supporting a potential neuroprotective effect of SGLT2is.

Figure 1.

Kaplan–Meier estimates of Parkinson's disease incidence in patients receiving SGLT2 inhibitors vs. metformin. Notes: Although the matched cohort included the same number of individuals in both groups (n = 91,080), the metformin group experienced a substantially higher number of PD events (772 vs. 240). This accounts for the smoother appearance of the metformin curve. The SGLT2i curve appears more stepwise due to fewer observed events. The number-at-risk table confirms that population sizes remained balanced over time.

Supplemental Figure 1 demonstrates a significant survival advantage for SGLT2i users, with lower all-cause mortality compared to metformin. The log-rank test confirmed a statistically significant difference, indicating that SGLT2is reduce both PD risk and mortality, addressing competing risk concerns.

Subgroup analysis of PD risk

Figure 2 presents a subgroup analysis of the association between SGLT2i use and PD risk across key demographic and clinical variables. The protective effect of SGLT2i compared to metformin was observed in most subgroups, with adjusted hazard ratios (aHRs) consistently favoring SGLT2i.

Figure 2.

Subgroup analysis of SGLT2 inhibitors vs. metformin on Parkinson's disease risk. Abbreviations: aHR – Adjusted Hazard Ratio, CI – Confidence Interval, HbA1c – Hemoglobin A1c, eGFR – Estimated Glomerular Filtration Rate.

When stratified by ethnicity, significant associations were found in White (aHR, 0.65; 95% CI, 0.54–0.79) and Asian (aHR, 0.57; 95% CI, 0.34–0.96) patients. However, no significant association was observed in Black or African American (aHR, 1.17; 95% CI, 0.25–5.44) or American Indian/Alaska Native (aHR, 0.74; 95% CI, 0.35–1.58) subgroups. These nonsignificant results likely reflect limited statistical power due to small sample sizes or differential SGLT2i prescribing patterns, rather than a true absence of effect. The wide confidence intervals support this interpretation.

The association was particularly pronounced in older adults (≥60 years), individuals with HbA1c ≥ 7.5%, and those with impaired renal function (eGFR <60 mL/min/1.73 m²), suggesting stronger neuroprotective effects in clinically vulnerable populations. The protective association persisted regardless of sex or concurrent statin use, indicating robustness across diverse patient groups.

These findings highlight the potential generalizability of the neuroprotective benefit of SGLT2is while emphasizing the importance of evaluating effect modification across ethnic groups. Future studies using larger, multi-ethnic datasets are warranted to confirm these differential effects and explore underlying mechanisms.

Validation analysis using positive and negative controls

The primary outcome analysis confirmed that SGLT2i use was associated with a significantly lower risk of PD compared to metformin, consistent with the findings in the main analysis. The positive control outcome (dementia) showed a similar protective association, supporting the validity of the neuroprotective effect observed with SGLT2i.

For negative outcome controls (acute appendicitis, hematologic malignancies, common cold, burn injuries, and suicidal ideation or attempts), no significant associations were observed, reinforcing that the primary findings were not due to residual confounding or systematic biases. The negative exposure control (vitamin C use) also showed no association with PD risk, further supporting the specificity of the SGLT2i effect.

These findings strengthen the causal inference that SGLT2i may confer a protective effect against PD, independent of confounding factors or competing risks.

Discussion

This is the first large-scale real-world study to directly compare SGLT2is and metformin in relation to PD risk among patients with T2DM. Using a 20-year dataset, SGLT2i use was associated with a 28% lower risk of PD (aHR, 0.72; 95% CI, 0.62–0.84) and a markedly lower relative risk (RR, 0.31; 95% CI, 0.27–0.36) (Table 2). After PSM, baseline characteristics were well balanced, supporting the validity of the comparison. SGLT2is also conferred lower all-cause mortality (aHR, 0.85; RR, 0.45), suggesting a survival benefit that helps mitigate bias from competing risk.

Kaplan-Meier analysis (Figure 1) demonstrated an early and sustained divergence in PD incidence, favoring SGLT2is over metformin. While both RRs and aHRs indicated consistent neuroprotective associations of SGLT2is versus metformin, we observed numerical discrepancies between these measures, particularly for all-cause mortality. This reflects fundamental differences in interpretation: RRs summarize cumulative incidence over the entire follow-up, whereas aHRs from Cox models reflect instantaneous risk at any given time point. For PD, the early and persistent separation of Kaplan-Meier curves (Figure 1) yielded concordant estimates (RR = 0.31; aHR = 0.72). In contrast, survival curves for all-cause mortality remained similar for over three years (Figure S1) before diverging, resulting in a more modest aHR (0.85) despite a substantial RR (0.45). This temporal separation likely reflects non-proportional hazards, a common feature in long-term follow-up studies, and underscores the complementary value of reporting both RRs and aHRs in pharmacoepidemiologic research. Supplemental analysis (Figure 1S) confirmed a survival benefit, addressing competing mortality. Subgroup analyses (Figure 2) revealed significant PD risk reduction among patients ≥60 years, those with HbA1c ≥ 7.5%, and impaired renal function, suggesting greater neuroprotection in high-risk populations. Nonsignificant findings in younger subgroups likely reflect fewer PD events and limited power. Positive and negative control analyses (Figure 3) confirmed robustness—dementia risk was reduced, while unrelated outcomes showed no association. The use of vitamin C as a negative exposure control further supported specificity. These findings challenge metformin's presumed neuroprotective role and highlight SGLT2is as a potentially superior option for PD risk reduction in T2DM, warranting further investigation.

Figure 3.

Subgroup analysis of SGLT2 inhibitors vs. metformin on primary, positive, and negative outcome controls, and negative exposure control. Abbreviations: aHR – Adjusted Hazard Ratio, CI – Confidence Interval.

SGLT2is may provide superior neuroprotection against PD in patients with T2DM compared to metformin due to their broader mechanistic effects beyond glycemic control.^{11,28,30,32,36} While metformin has well-documented neuroprotective properties, primarily mediated through AMPK activation, mitochondrial preservation, and anti-inflammatory effects,^{21,24–27,39–43} our findings suggest that SGLT2is confer additional benefits that may more effectively mitigate diabetes-related neurodegeneration. SGLT2is directly reduce oxidative stress, a key driver of PD pathology, by limiting reactive oxygen species (ROS) production, restoring mitochondrial function, and modulating NADPH oxidase activity.^11,38,51,52 Furthermore, SGLT2is enhance autophagy-lysosomal pathways, crucial for clearing α-synuclein aggregates, a hallmark of PD.^37,53–55 Unlike metformin, which primarily improves metabolic health, SGLT2is have been shown to increase ketone body production, providing an alternative and neuroprotective energy substrate for dopaminergic neurons.^11,38,56,57 Importantly, our study demonstrated that even after widespread adoption post-2020 (Figure 2), SGLT2is continued to exhibit a stronger protective association against PD (aHR = 0.66, 95% CI: 0.54–0.81, p < 0.0001), suggesting that increased clinical familiarity and optimized prescribing further enhance their benefits. SGLT2i users exhibited a significantly lower risk of both PD (aHR = 0.72, 95% CI: 0.62–0.84, p < 0.0001) and dementia (aHR = 0.73, 95% CI: 0.68–0.78, p < 0.0001), reinforcing their broader neuroprotective benefits. Given their well-established cardiovascular, renal, and emerging neuroprotective benefits,⁵⁸ SGLT2is may warrant prioritization as a first-line therapy for T2DM patients at risk for neurodegeneration, pending further prospective studies to validate their potential in PD prevention.

While prior studies—such as Mendelian randomization analyses—have explored the neurodegenerative effects of SGLT1/2 inhibition,⁵⁹ they differ in methodology and do not reflect real-world exposure to SGLT2is. Our findings, based on large-scale observational data with robust confounder control, provide complementary evidence. Discrepancies may be due to confounding by indication,^1,59 reverse causality from prodromal PD influencing treatment decisions,² and surveillance bias from more frequent monitoring in SGLT2i users.⁶ We addressed these by implementing rigorous PSM, excluding PD diagnoses within six months of treatment initiation, accounting for competing mortality risks, and validating findings with positive and negative control outcomes. Unlike prior studies with heterogeneous designs and short follow-up, our 20-year real-world analysis supports SGLT2is as offering superior protection against PD compared to metformin, highlighting the need for prospective confirmation.

This study leverages a 20-year real-world dataset from 142 healthcare organizations, offering robust power and generalizability. A strict new-user design, exclusion of prior SGLT2i or metformin exposure, and propensity score matching with comprehensive clinical, metabolic, and lifestyle covariates minimized confounding. Use of an active comparator (SGLT2i vs. metformin) mitigated healthy user bias more effectively than comparisons with non-users.⁶⁰ Control outcomes (Figure 3), competing risk models, and subgroup analyses (Figure 2) further validated findings. Temporal stratification revealed a stronger protective effect post-2020 (aHR = 0.66; 95% CI, 0.54–0.81), likely reflecting improved prescribing practices. Adherence was ensured by requiring ≥1 year of continuous prescriptions without extended refill gaps, minimizing exposure misclassification and reinforcing the reliability of long-term effect estimates.

Despite rigorous adjustments, residual confounding cannot be excluded given the non-randomized nature of treatment assignment. Large-scale observational studies remain essential for real-world evidence, especially in pharmacovigilance and long-term safety evaluation. Reliance on ICD-10-CM coding may introduce misclassification, though this risk is minimized through validated case definitions, strict exclusions, and extended follow-up. The negative exposure control (vitamin C) may be under-recorded due to its over-the-counter status but serves as a useful proxy for detecting systematic biases in healthcare utilization. Future studies should incorporate biomarkers, neuroimaging, and genetic profiling to enhance diagnostic accuracy and elucidate SGLT2is’ neuroprotective mechanisms. Despite these limitations, our study offers one of the most comprehensive real-world assessments of SGLT2i use and PD risk, supporting the need for prospective validation.

Conclusions

Our study provides real-world evidence that SGLT2is are associated with a 28% lower risk of PD compared to metformin in T2DM patients. Leveraging a large-scale, 20-year dataset and rigorous PSM, our findings remained robust across sensitivity analyses, particularly in the post-2020 cohort. While RCTs are the gold standard for causal inference, ethical and feasibility constraints highlight the importance of real-world studies in informing clinical decisions. These results warrant further prospective investigations into the potential neuroprotective benefits of SGLT2is.

Supplemental Material

sj-docx-1-pkn-10.1177_1877718X251359391 - Supplemental material for SGLT2 inhibitors vs. metformin for Parkinson's disease risk reduction in type 2 diabetes

Supplemental material, sj-docx-1-pkn-10.1177_1877718X251359391 for SGLT2 inhibitors vs. metformin for Parkinson's disease risk reduction in type 2 diabetes by Mingyang Sun, Xiaoling Wang, Zhongyuan Lu, Yitian Yang, Shuang Lv, Mengrong Miao, Wan-Ming Chen, Szu-Yuan Wu and Jiaqiang Zhang in Journal of Parkinson's Disease

Footnotes

Abbreviations

Ethics approval and consent

This study utilized the TriNetX platform, which exclusively provides de-identified and aggregated electronic health record data. In accordance with the Health Insurance Portability and Accountability Act (HIPAA), such data do not allow for individual identification and thus do not meet the federal definition of human subjects research. Consequently, research conducted using this platform is exempt from Institutional Review Board (IRB) oversight and does not require informed consent.

Author contributions

Conceptualization: Mingyang Sun, MD; Xiaoling Wang, PhD

Methodology: Zhongyuan Lu, PhD; Yitian Yang, MD, PhD

Software: Suang Lv, MD; Mengrong Miao, MD

Validation: Wan-Ming Chen, PhD; Szu-Yuan Wu, MD, MPH, PhD

Formal Analysis: Jiaqiang Zhang, MD, PhD; Xiaoling Wang, PhD

Investigation: Yitian Yang, MD, PhD; Zhongyuan Lu, PhD

Resources: Mengrong Miao, MD; Wan-Ming Chen, PhD

Data Curation: Suang Lv, MD; Mingyang Sun, MD

Writing – Original Draft: Jiaqiang Zhang, MD, PhD; Szu-Yuan Wu, MD, MPH, PhD

Writing – Review & Editing: Xiaoling Wang, PhD; Wan-Ming Chen, PhD

Visualization: Zhongyuan Lu, PhD; Yitian Yang, MD, PhD

Supervision: Szu-Yuan Wu, MD, MPH, PhD; Jiaqiang Zhang, MD, PhD

Project Administration: Mingyang Sun, MD; Suang Lv, MD

Funding Acquisition: Wan-Ming Chen, PhD; Szu-Yuan Wu, MD, MPH, PhD

Funding

This work was supported by the National Key Research and Development Program of China (Funding Number: 2023YFC2506900), which funded Zhang's research, and by the Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, which supported the work of Szu-Yuan Wu (Funding Numbers: 11403 and 11404).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Guarantor statement

Prof. Jiaqiang Zhang is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Supplemental material

Supplemental material for this article is available online.

ORCID iD

Jiaqiang Zhang

References

Cheong

JLY

de Pablo-Fernandez

Foltynie

, et al. The association between type 2 diabetes Mellitus and Parkinson's disease. J Parkinsons Dis 2020; 10: 775–789.

Park

Huang

, et al. Diabetes and risk of Parkinson's disease. Diabetes Care 2011; 34: 910–915.

Brauer

Wei

, et al. Diabetes medications and risk of Parkinson's disease: a cohort study of patients with diabetes. Brain 2020; 143: 3067–3076.

Driver

Smith

Buring

, et al. Prospective cohort study of type 2 diabetes and the risk of Parkinson's disease. Diabetes Care 2008; 31: 2003–2005.

Schernhammer

Hansen

Rugbjerg

, et al. Diabetes and the risk of developing Parkinson's disease in Denmark. Diabetes Care 2011; 34: 1102–1108.

De Pablo-Fernandez

Goldacre

Pakpoor

, et al. Association between diabetes and subsequent Parkinson disease: a record-linkage cohort study. Neurology 2018; 91: e139–e142.

Chohan

Senkevich

Patel

, et al. Type 2 diabetes as a determinant of Parkinson's disease risk and progression. Mov Disord 2021; 36: 1420–1429.

Duta

Muscurel

Dogaru

, et al. Ferroptosis-A shared mechanism for Parkinson's disease and type 2 diabetes. Int J Mol Sci 2024; 25. doi:https://doi.org/10.3390/ijms25168838

Faizan

Sarkar

Singh

. Type 2 diabetes mellitus augments Parkinson's disease risk or the other way around: facts, challenges and future possibilities. Ageing Res Rev 2022; 81: 101727.

10.

Sergi

Renaud

Simola

, et al. Diabetes, a contemporary risk for Parkinson's disease: epidemiological and cellular evidences. Front Aging Neurosci 2019; 11: 302.

11.

Lin

Wang

Chen

, et al. Two birds one stone: the neuroprotective effect of antidiabetic agents on parkinson disease-focus on sodium-glucose cotransporter 2 (SGLT2) inhibitors. Antioxidants (Basel) 2021; 10.

12.

Chaudhuri

Azulay

Odin

, et al. Economic burden of Parkinson's disease: a multinational, real-world, cost-of-illness study. Drugs Real World Outcomes 2024; 11: 1–11.

13.

Yang

Hamilton

Kopil

, et al. Current and projected future economic burden of Parkinson's disease in the U.S. NPJ Parkinsons Dis 2020; 6: 15.

14.

Cullinane

de Pablo Fernandez

Konig

, et al. Type 2 diabetes and Parkinson's disease: a focused review of current concepts. Mov Disord 2023; 38: 162–177.

15.

Greco

Munir

Musaro

, et al. Restoring autophagic function: a case for type 2 diabetes mellitus drug repurposing in Parkinson's disease. Front Neurosci 2023; 17: 1244022.

16.

Birajdar

Mazahir

Alam

, et al. Repurposing and clinical attributes of antidiabetic drugs for the treatment of neurodegenerative disorders. Eur J Pharmacol 2023; 961: 176117.

17.

Krentz

. Metformin: new insights into an archetypal cardiometabolic drug. Cardiovasc Endocrinol 2017; 6: 92–94.

18.

Gao

Liu

, et al. Exploring the pharmacological potential of metformin for neurodegenerative diseases. Front Aging Neurosci 2022; 14: 838173.

19.

Sun

Chen

, et al. Metformin in elderly type 2 diabetes mellitus: dose-dependent dementia risk reduction. Brain 2024; 147: 1474–1482.

20.

Sun

Wang

, et al. Metformin use and risk of delirium in older adults with type 2 diabetes. Diabetes Care 2024.

21.

Paudel

Angelopoulou

Piperi

, et al. Emerging neuroprotective effect of metformin in Parkinson's disease: a molecular crosstalk. Pharmacol Res 2020; 152: 104593.

22.

Qin

Zhang

, et al. Association between diabetes medications and the risk of Parkinson's disease: a systematic review and meta-analysis. Front Neurol 2021; 12: 678649.

23.

Ping

Jiang

. Association between metformin and neurodegenerative diseases of observational studies: systematic review and meta-analysis. BMJ Open Diabetes Res Care 2020; 8. doi:https://doi.org/10.1136/bmjdrc-2020-001370

24.

Wahlqvist

Lee

Hsu

, et al. Metformin-inclusive sulfonylurea therapy reduces the risk of Parkinson's disease occurring with type 2 diabetes in a Taiwanese population cohort. Parkinsonism Relat Disord 2012; 18: 753–758.

25.

Huang

Chang

Gau

, et al. Dose-response association of metformin with Parkinson's disease odds in type 2 diabetes mellitus. Pharmaceutics 2022; 14. doi:https://doi.org/10.3390/pharmaceutics14050946

26.

Shi

Liu

Fonseca

, et al. Effect of metformin on neurodegenerative disease among elderly adult US veterans with type 2 diabetes mellitus. BMJ Open 2019; 9: e024954.

27.

Agostini

Masato

Bubacco

, et al. Metformin repurposing for parkinson disease therapy: opportunities and challenges. Int J Mol Sci 2021; 23. doi:https://doi.org/10.3390/ijms23010398

28.

Minze

Will

Terrell

, et al. Benefits of SGLT2 inhibitors beyond glycemic control: a focus on metabolic, cardiovascular and renal outcomes. Curr Diabetes Rev 2018; 14: 509–517.

29.

Gupta

Canovatchel

Lokesh

, et al. Sodium-glucose cotransporter-2 inhibitors: moving beyond the glycemic treatment goal. Indian J Endocrinol Metab 2017; 21: 909–918.

30.

Sun

Wang

, et al. Comparative effectiveness of SGLT2 inhibitors and GLP-1 receptor agonists in preventing Alzheimer's disease, vascular dementia, and other dementia types among patients with type 2 diabetes. Diabetes Metab 2025; 51: 101623.

31.

Lardaro

Quarta

Pagnotta

, et al. Impact of sodium glucose cotransporter 2 inhibitors (SGLT2i) therapy on dementia and cognitive decline. Biomedicines 2024; 12. doi:https://doi.org/10.3390/biomedicines12081750

32.

Kim

Biessels

, et al. SGLT2 Inhibitor use and risk of dementia and Parkinson disease among patients with type 2 diabetes. Neurology 2024; 103: e209805.

33.

Mui

Zhou

Lee

, et al. Sodium-glucose cotransporter 2 (SGLT2) inhibitors vs. Dipeptidyl peptidase-4 (DPP4) inhibitors for new-onset dementia: a propensity score-matched population-based study with competing risk analysis. Front Cardiovasc Med 2021; 8: 747620.

34.

Gunawan

Hariyanto

. Risk of dementia in patients with diabetes using sodium-glucose transporter 2 inhibitors (SGLT2i): a systematic review, meta-analysis, and meta-regression. Diabetes Ther 2024; 15: 663–675.

35.

Chuang

Tsai

Yang

, et al. Correlation between the utilization of sodium-glucose cotransporter-2 (SGLT2) inhibitors and the risk of dementia: a nationwide population-based study in Taiwan. Naunyn Schmiedebergs Arch Pharmacol 2024.

36.

Guo

Tang

Shao

, et al. 916-P: SGLT2 inhibitors and Parkinson’s disease (PD) risk in older populations with type 2 diabetes (T2D). Diabetes 2024; 73.

37.

Unal

Cansiz

Beler

, et al. Sodium-dependent glucose co-transporter-2 inhibitor Empagliflozin exerts neuroprotective effects in rotenone-induced Parkinson's disease model in zebrafish; mechanism involving Ketogenesis and autophagy. Brain Res 2023; 1820: 148536.

38.

Ahmed

El-Sayed

Kandeil

, et al. Empagliflozin attenuates neurodegeneration through antioxidant, anti-inflammatory, and modulation of alpha-synuclein and Parkin levels in rotenone-induced Parkinson's disease in rats. Saudi Pharm J 2022; 30: 863–873.

39.

Tayara

Espinosa-Oliva

Garcia-Dominguez

, et al. Divergent effects of metformin on an inflammatory model of Parkinson's disease. Front Cell Neurosci 2018; 12: 440.

40.

Ryu

Park

, et al. Metformin regulates astrocyte reactivity in Parkinson's disease and normal aging. Neuropharmacology 2020; 175: 108173.

41.

Mor

Sohrabi

Kaletsky

, et al. Metformin rescues Parkinson's disease phenotypes caused by hyperactive mitochondria. Proc Natl Acad Sci U S A 2020; 117: 26438–26447.

42.

Patil

Jain

Ghumatkar

, et al. Neuroprotective effect of metformin in MPTP-induced Parkinson's disease in mice. Neurosci 2014; 277: 747–754.

43.

Chen

Nie

, et al. The potential role of metformin in the treatment of Parkinson’s disease. J Bio-X Res 2020; 3: 27–35.

44.

Taquet

Geddes

Husain

, et al. 6-month Neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records. Lancet Psychiatry 2021; 8: 416–427.

45.

Kochhar

Desai

Farraye

, et al. Efficacy of biologic and small molecule agents as second-line therapy after exposure to TNF inhibitors in patients with ulcerative colitis: a propensity-matched cohort study. Aliment Pharmacol Ther 2023; 58: 297–308.

46.

Kochhar

Khataniar

Jairath

, et al. Comparative effectiveness of upadacitinib and tofacitinib in ulcerative colitis: a US propensity-matched cohort study. Am J Gastroenterol 2024; 119: 2471–2479.

47.

Iskander

Wang

, et al. Association of sodium-glucose cotransporter 2 inhibitors with time to dementia: a population-based cohort study. Diabetes Care 2023; 46: 297–304.

48.

Niu

Xie

Zhang

, et al. Vitamin C, vitamin E, beta-carotene and risk of Parkinson's disease: a systematic review and dose-response meta-analysis of observational studies. Nutr Neurosci 2024; 27: 329–341.

49.

Chang

Kwak

. Effect of dietary vitamins C and E on the risk of Parkinson's disease: a meta-analysis. Clin Nutr 2021; 40: 3922–3930.

50.

O’brien

. A caution regarding rules of thumb for variance inflation factors. J Quality Quantity 2007; 41: 673–690.

51.

Yaribeygi

Atkin

Butler

, et al. Sodium-glucose cotransporter inhibitors and oxidative stress: an update. J Cell Physiol 2019; 234: 3231–3237.

52.

Luna-Marco

Iannantuoni

Hermo-Argibay

, et al. Cardiovascular benefits of SGLT2 inhibitors and GLP-1 receptor agonists through effects on mitochondrial function and oxidative stress. Free Radic Biol Med 2024; 213: 19–35.

53.

Xilouri

Brekk

Stefanis

. Autophagy and alpha-synuclein: relevance to Parkinson's disease and related synucleopathies. Mov Disord 2016; 31: 178–192.

54.

Moors

Hoozemans

Ingrassia

, et al. Therapeutic potential of autophagy-enhancing agents in Parkinson's disease. Mol Neurodegener 2017; 12: 11.

55.

Pan

Kondo

, et al. The role of autophagy-lysosome pathway in neurodegeneration associated with Parkinson's disease. Brain 2008; 131: 1969–1978.

56.

Pawlos

Broncel

Wozniak

, et al. Neuroprotective effect of SGLT2 inhibitors. Molecules 2021; 26. doi:https://doi.org/10.3390/molecules26237213

57.

Tomita

Kume

Sugahara

, et al. SGLT2 Inhibition mediates protection from diabetic kidney disease by promoting ketone body-induced mTORC1 inhibition. Cell Metab 2020; 32: 404–419.e6.

58.

Meyer

Hullin

. Use of SGLT2 inhibitors in cardiovascular diseases: why, when and how? Swiss Med Wkly 2020; 150: w20341.

59.

Liu

Shi

Shao

. Sodium-glucose cotransporter 1/2 inhibition and risk of neurodegenerative disorders: a Mendelian randomization study. Brain Behav 2024; 14: e3624.

60.

Olawore

Stupsilonrmer

Glynn

, et al. The healthy user effect in pharmacoepidemiology. Am J Epidemiol 2024.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.25 MB