Associations of hypoglycemic medications, cordycepin and vaccination with clinical outcomes in diabetic kidney disease patients with COVID-19

Abstract

The associations between hypoglycemic medications, cordycepin, COVID-19 vaccination, and clinical outcomes of SARS-CoV-2 infection were examined in a retrospective cohort of patients with diabetic kidney disease (DKD) in Ningbo, China, between December 2022 and June 2023. Acute COVID-19 outcomes included fever, pneumonia, hospitalization, and prolonged symptoms. Short-term DKD outcomes at three months post-infection included a serum creatinine increase ≥ 30%, an estimated glomerular filtration rate (eGFR) decrease ≥ 10 mL/min/1.73 m², a urinary albumin-to-creatinine ratio (UACR) increase ≥ 30%, a fasting blood glucose increase ≥ 1.1 mmol/L, and a hemoglobin A1c (HbA1c) increase ≥ 0.3%. Among 642 DKD patients with COVID-19, 66.8% were treated with sodium-glucose cotransporter-2 inhibitors (SGLT2i) at baseline, 36.9% with dipeptidyl peptidase-4 inhibitors (DPP4i), 30.8% with metformin, and 36.4% with cordycepin. Logistic regression analysis indicated that SGLT2i use was associated with a reduced risk of hospitalization and less worsening of UACR. Metformin use was linked to a lower incidence of COVID-19 pneumonia but an increased risk of serum creatinine elevation. DPP4i use showed no significant association with adverse outcomes. Cordycepin use was associated with reduced risks of hospitalization and serum creatinine elevation. Compared with unvaccinated patients, multiple-dose COVID-19 vaccination was associated with reduced risks of adverse outcomes, including prolonged COVID-19 symptoms, pneumonia, decreased eGFR, and elevated blood glucose and HbA1c levels. In conclusion, pretreatment with SGLT2i, cordycepin, and multiple-dose COVID-19 vaccination was associated with reduced adverse outcomes among DKD patients with COVID-19.

Introduction

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as a significant global health concern, affecting over 700 million people worldwide as of 1 July 2024 [1]. Advances in disease management and increased awareness have contributed to a reduction in mortality associated with SARS-CoV-2 infection. However, unfavorable clinical outcomes persist, particularly among individuals with chronic diseases [2]. Diabetic kidney disease (DKD), a major microvascular complication of diabetes mellitus, is the leading cause of chronic kidney disease (CKD) and end-stage renal disease worldwide [3,4]. Studies have shown that patients with DKD who contract SARS-CoV-2 experience longer hospitalization and more adverse prognoses [5]. Compared to individuals with CKD, DKD patients exhibited a two-fold greater susceptibility to COVID-19 [6], with elevated rates of endotracheal intubation and mortality, potentially due to a pro-inflammatory state and immune dysregulation. The coexistence of diabetes and kidney disease may exacerbate the severity and mortality of COVID-19, precipitating acute respiratory distress syndrome and multi-organ failure [7]. Furthermore, evidence suggests that COVID-19 infection can disrupt glycemic control in diabetic patients and accelerate CKD progression in those with DKD [8,9].

Medications commonly used in DKD patients, including hypoglycemic and renoprotective agents, may interact with COVID-19, thereby influencing clinical outcomes and renal prognosis. For instance, metformin has been shown to activate AMP-activated protein kinase (AMPK), stabilize angiotensin-converting enzyme 2 (ACE2), inhibit multiple inflammatory pathways, improve organ perfusion, and potentially prevent lung damage [10,11]. Studies have reported a significant reduction in mortality and severity of COVID-19 among diabetic individuals treated with metformin [12]. Similarly, dipeptidyl peptidase-4 inhibitors (DPP4i) and sodium-glucose cotransporters 2 inhibitors (SGLT2i) have been associated with reduced mortality rates and improved clinical outcomes in COVID-19 patients [13–15]. Cordycepin, a traditional herbal medicine widely used in China, has demonstrated renoprotective effects by reducing oxidative stress and inflammation in DKD [16]. The therapeutic properties of cordycepin, including its efficacy in mitigating pulmonary fibrosis and renal insufficiency, align with the pathological manifestations associated with COVID-19 [17]. However, research on the effects of cordycepin in CKD or DKD patients with COVID-19 is scarce and warrants further investigation. Alongside medications, COVID-19 vaccination is crucial in determining clinical outcomes of SARS-CoV-2 infection. Studies have indicated that vaccinated patients are less likely to experience prolonged COVID-19 symptoms than unvaccinated individuals [18,19].

In December 2022, changes in public health policy in China resulted in a nationwide outbreak of COVID-19. Ningbo, a coastal metropolitan city in China, reported a significant increase in DKD patients diagnosed with COVID-19. We conducted a retrospective cohort study of DKD patients with concurrent COVID-19, who were regularly followed at the First Affiliated Hospital of Ningbo University between December 2022 and June 2023. The study investigated the associations between prior use of hypoglycemic agents, the renoprotective agent cordycepin, and COVID-19 vaccination and clinical outcomes in patients with COVID-19 and DKD.

Methods

Setting and patients

The study population was recruited from the three main campuses of the First Affiliated Hospital of Ningbo University, the largest public hospital in Ningbo City. Since 2017, the hospital has operated a specialized DKD clinic offering comprehensive assessment and management for patients with DKD. DKD patients undergo regular follow-ups every one to two months, and a comprehensive database was established [20,21]. In this retrospective cohort study, 678 regularly followed patients with type 2 diabetes mellitus (T2DM) and DKD who received an initial diagnosis of COVID-19 between December 2022 and June 2023 were enrolled. Of these, 642 patients with complete laboratory, medication, and vaccination data both prior to COVID-19 infection and at three months post-infection were included in the analysis. This study was approved by the Ethics Committee of the First Affiliated Hospital of Ningbo University (Approval No. 2024132RS01).

Data collection

DKD patients with COVID-19 were identified through nasopharyngeal swab testing for SARS-CoV-2 via PCR at the hospital. For each participant, comprehensive demographic and clinical data, including vaccination history and medication use, were collected during the study period. Baseline levels of fasting blood glucose, serum creatinine, uric acid, estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), hemoglobin A1c (HbA1c), and blood urea nitrogen (BUN) were obtained within one month prior to infection. These markers were subsequently monitored every one to two months post-infection. Clinical outcomes following SARS-CoV-2 infection, including fever (body temperature >37.5 °C), diagnosis of COVID-19 pneumonia, hospitalization, and duration of COVID-19 symptoms, were also documented. All blood and urine specimens were analyzed in the three central clinical laboratories of the hospital to ensure standardization and quality control.

Definition and outcome

The definition of DKD was based on the guidelines of the National Kidney Foundation–Kidney Disease Outcomes Quality Initiative (NKF–K/DOQI) [22]. Specifically, DKD was defined as CKD caused by diabetes, characterized by UACR ≥30 mg/g and/or eGFR < 60 mL/min^/1.73 m² for more than three months. In addition, DKD was categorized into five stages based on eGFR levels. Inclusion criteria for the study were participants aged 18–80 years with a confirmed diagnosis of type 2 diabetes and DKD stages 1–4. Exclusion criteria included type 1 diabetes, DKD stage 5 or dialysis, as well as other renal diseases [20]. Baseline use of studied hypoglycemic medications included metformin, SGLT2i (dapagliflozin, canagliflozin, empagliflozin), DPP4i (linagliptin, saxagliptin, sitagliptin, vildagliptin). These medications were prescribed at standard dosages, with adjustments according to the guidelines for patients with impaired renal function. Baseline use of the renoprotective agent cordycepin was administered in the form of Bailing capsules, a prescription herbal medicine in China with approved indications for CKD and chronic respiratory disease. Each Bailing capsule contains 0.5 g of 100% artificial Cordyceps sinensis powder, with a composition closely resembling that of wild Cordyceps sinensis. The active compounds in artificial cordycepin include approximately 2% nucleosides, 0.55% sterols, and 4.4% polysaccharides, which collectively confer its primary renoprotective and anti-fibrotic properties [23]. DKD participants were prescribed Bailing capsules by licensed nephrologists at a standard dosage of 2 g per administration, three times daily. Patients who had been taking these capsules orally for six months or longer prior to COVID-19 infection were considered baseline users.

The COVID-19 vaccines administered in Ningbo, China, during the study period were Sinopharm or Sinovac, both inactivated vaccines. COVID-19 vaccination status prior to infection was categorized as unvaccinated, one dose, or two or more doses. Valid vaccine dose counts were determined from participants’ vaccination records, documenting Sinopharm or Sinovac doses administered within 12 months before COVID-19 infection.

Hypertension was defined as use of antihypertensive medications, a systolic blood pressure ≥140 mmHg or a diastolic blood pressure ≥90 mmHg, or a documented history of hypertension. Cardiovascular diseases included coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease, deep vein thrombosis, and pulmonary embolism. Stroke was identified as the sudden onset of focal or global brain dysfunction persisting for more than 24 h, with vascular causes as the primary etiology. Diabetic retinopathy was characterized by retinal abnormalities resulting from the long-term effects of diabetes, whereas diabetic peripheral neuropathy was defined by symptoms or clinical markers of peripheral nerve impairment in diabetic individuals, after excluding other potential causes.

Measured acute COVID-19 indicators included fever, COVID-19 pneumonia, hospitalization, and the duration of symptoms (e.g., chills, sore throat, cough, or myalgia) lasting longer than seven days. Biochemical indicators of adverse clinical outcomes, assessed three months post-infection versus baseline, included a ≥ 30% increase in serum creatinine, a ≥ 10 mL/min/1.73 m² decrease in eGFR, a ≥ 30% increase in UACR, a ≥ 1.1 mmol/L increase in fasting blood glucose, and a ≥ 0.3% increase in HbA1c [24,25].

Statistical analysis

Data analysis was performed using SPSS Statistics version 27.0. Categorical variables were reported as frequencies and percentages, whereas continuous variables with normal distributions were presented as mean ± standard deviation. For non-normally distributed continuous variables, the median and interquartile range (IQR) were used. Group comparisons of categorical variables were conducted using the chi-square test. Comparisons of clinical characteristics between users and non-users of specific medications were performed using independent sample t-tests for normally distributed variables and the Mann-Whitney U test for non-normally distributed variables. For comparisons across COVID-19 vaccination groups, one-way ANOVA was applied to continuous variables with normal distribution and homogeneity of variance, whereas the Kruskal-Wallis test was used for variables not meeting these assumptions. DKD patients were stratified into stages 1–2 and stages 3–4 based on eGFR for subsequent analyses. Propensity score matching (PSM, 1:1 ratio) was performed with a caliper of 0.02 to mitigate baseline imbalances across comparison subgroups, covariates incorporated in the PSM model included age, sex, BMI, smoking and alcohol history, comorbidities, stroke history, cancer history, concomitant medication use, and baseline laboratory parameters (triglycerides, total cholesterol, LDL, HDL, serum uric acid, serum creatinine, eGFR, blood urea nitrogen, UACR, fasting blood glucose, and HbA1c). Multivariable binary logistic regression was conducted to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for clinical outcomes associated with medication use and vaccination status. The regression analysis was adjusted for age, sex, body mass index (BMI), alcohol consumption history, smoking history, stroke history, cancer history, comorbidities, vaccination status, and medication use. In logistic regression models evaluating clinical outcomes associated with metformin, SGLT2i, DPP4i, and cordycepin, all four agents were included as independent variables to account for potential interactions.

Results

Baseline clinical characteristics by drug class

Among 642 patients with T2DM and DKD infected with SARS-CoV-2, 429 (66.8%) were treated with SGLT2i at baseline, 237 (36.9%) with DPP4i, 198 (30.8%) with metformin, and 234 (36.4%) with the renoprotective agent cordycepin. The general characteristics of the participants, stratified by medication use, are shown in Table 1. Among all participants, the proportions of DKD stages 1–4 were 23.3%, 43.3%, 25.4%, and 8.0%, respectively. No significant differences were observed between users and non-users of SGLT2i, DPP4i, metformin, and cordycepin in terms of age, sex, BMI, comorbidities, or key laboratory parameters, including serum creatinine, eGFR, UACR, and fasting blood glucose and HbA1c levels.

Table 1.

Baseline clinical characteristics of patients with diabetic kidney disease by use of SGLT2i, DPP4i, metformin, and the renoprotective agent cordycepin

	SGLT2i Non-users n = 213	Users n = 429	p Value	DPP4i Non-users n = 405	Users n = 237	p Value	Metformin Non-users n = 444	Users n = 198	p Value	Cordycepin Non-users n = 408	Users n = 234	p Value
Age, years	60.00(50.00, 72.00)	62.00(52.00, 73.00)	0.303	62.00(50.00, 73.00)	61.00(51.00, 72.00)	0.888	62.00(51.00, 72.00)	60.00(49.75, 74.25)	0.904	61.00(51.00, 72.00)	62.00(50.75, 74.00)	0.425
Men, n (%)	136(63.8)	262(61.1)	0.495	251(62.0)	147(62.0)	0.990	268(60.4)	130(65.7)	0.202	249(61.0)	149(63.7)	0.506
BMI, kg/m²	23.30(21.63, 25.14)	23.23(21.54, 25.78)	0.631	23.13(21.55, 25.61)	23.39(21.62, 25.37)	0.955	23.31(21.64, 25.29)	23.08(21.45, 25.74)	0.960	23.08(21.54, 25.38)	23.43(21.70, 25.62)	0.465
Smoker, n (%)	72(33.8)	150(35.0)	0.771	139(34.3)	83(35.0)	0.857	159(35.8)	63(31.8)	0.326	130(31.9)	92(39.3)	0.056
Drinker, n (%)	56(26.3)	122(28.4)	0.567	116(28.6)	62(26.2)	0.498	117(26.4)	61(30.8)	0.244	122(29.9)	56(23.9)	0.104
Diabetic retinopathy, n (%)	109(51.2)	217(50.6)	0.888	197(48.6)	129(54.4)	0.157	231(52.0)	95(48.0)	0.343	210(51.5)	116(49.6)	0.643
DPN, n (%)	109(51.2)	222(51.7)	0.891	202(49.9)	129(54.4)	0.265	227(51.1)	104(52.5)	0.743	222(54.4)	109(46.6)	0.056
Hypertension, n (%)	156(73.2)	312(72.7)	0.891	302(74.6)	166(70.0)	0.213	330(74.3)	138(69.7)	0.223	301(73.8)	167(71.4)	0.509
Cardiovascular disease, n (%)	96(45.1)	182(42.4)	0.524	178(44.0)	100(42.2)	0.665	191(43.0)	87(43.9)	0.828	182(44.6)	96(41.0)	0.378
Stroke, n (%)	20(9.4)	31(7.2)	0.340	38(9.4)	13(5.5)	0.078	37(8.3)	14(7.1)	0.585	33(8.1)	18(7.7)	0.858
Cancer, n (%)	20(9.4)	37(8.6)	0.748	41(10.1)	16(6.8)	0.147	44(9.9)	13(6.6)	0.169	36(8.8)	21(9.0)	0.948
Triglycerides, mmol/L	1.57(1.03, 3.10)	1.63(1.11, 2.74)	0.656	1.64(1.12, 2.87)	1.45(0.99, 2.72)	0.141	1.62(1.05, 3.02)	1.58(1.11, 2.49)	0.549	1.57(1.05, 2.74)	1.72(1.07, 3.02)	0.256
Total cholesterol, mmol/L	4.26(3.49, 5.24)	4.41(3.57, 5.41)	0.138	4.41(3.55, 5.38)	4.32(3.51, 5.24)	0.529	4.34(3.49, 5.24)	4.63 ± 1.33	0.078	4.32(3.51, 5.35)	4.57 ± 1.35	0.387
LDL, mmol/L	2.71(2.10, 3.35)	2.76(2.24, 3.46)	0.178	2.78(2.20, 3.46)	2.71(2.17, 3.37)	0.478	2.72(2.16, 3.37)	2.76(2.26, 3.52)	0.167	2.76(2.19, 3.45)	2.75(2.17, 3.43)	0.938
HDL, mmol/L	1.18(0.96, 1.41)	1.15(0.98, 1.38)	0.889	1.14(0.96, 1.40)	1.17(1.00, 1.39)	0.609	1.15(0.98, 1.40)	1.15(0.97, 1.38)	0.914	1.14(0.96, 1.37)	1.17(1.00, 1.44)	0.286
Serum uric acid, μmol/L	341.30(279.90, 398.50)	345.00(295.00, 413.00)	0.202	341.80(287.50, 404.00)	356.39 ± 95.93	0.430	344.25(288.32, 413.00)	348.80 ± 91.53	0.672	338.10(288.10, 404.00)	356.34 ± 93.87	0.231
Serum creatinine, μmol/L	85.75(65.10, 112.70)	83.05(67.99, 104.71)	0.726	84.27(67.33, 108.40)	83.22(67.44, 104.80)	0.668	82.18(64.92, 108.37)	87.60(72.20, 104.63)	0.067	83.55(67.15, 108.46)	83.97(67.92, 103.05)	0.676
eGFR, ml/min/1.73 m2	79.00(53.00, 90.00)	80.00(56.00, 89.00)	0.571	80.29(55.10, 89.88)	80.00(53.00, 89.00)	0.436	80.00(53.36, 90.00)	81.00(57.37, 88.81)	0.684	80.15(55.00, 89.45)	78.28(53.00, 89.13)	0.382
Blood urea nitrogen, mmol/L	6.68(5.22, 8.68)	6.48(5.10, 8.30)	0.326	6.54(5.05, 8.30)	6.60(5.30, 8.50)	0.406	6.67(5.21, 8.50)	6.23(5.04, 8.22)	0.183	6.50(5.11, 8.75)	6.65(5.21, 8.25)	0.706
UACR, mg/g	110.00(29.75, 352.45)	100.00(30.34, 392.83)	0.780	94.78(30.75, 356.41)	119.40(26.36, 512.74)	0.901	111.49(31.37, 378.96)	94.68(29.26, 381.57)	0.597	95.84(31.73, 379.37)	111.05(27.81, 427.43)	0.922
Fasting blood glucose, mmol/L	7.91(6.43, 10.55)	7.62(6.42, 9.99)	0.561	7.67(6.37, 10.09)	7.72(6.46, 10.62)	0.602	7.76(6.49, 10.22)	7.42(6.35, 10.07)	0.443	7.75(6.47, 10.25)	7.60(6.28, 10.03)	0.282
HbA1c, %	6.90(6.30, 8.30)	6.80(6.20, 8.10)	0.396	6.90(6.20, 8.25)	6.80(6.20, 7.70)	0.596	6.90(6.20, 8.20)	6.85(6.20, 7.78)	0.650	6.90(6.20, 8.10)	6.90(6.30, 8.40)	0.367
Baseline medication use, n (%)
SGLT2i	0(0.0)	429(100.0)	<0.001	251(62.0)	178(75.1)	<0.001	259(58.3)	170(85.7)	<0.001	271(66.4)	158(67.5)	0.776
DPP4i	59(27.7)	178(41.5)	<0.001	0(0.0)	237(100.0)	<0.001	170(38.3)	67(33.8)	0.281	135(33.1)	102(43.6)	0.008
Metformin	28(13.1)	170(39.6)	<0.001	131(32.3)	67(28.3)	0.281	0(0.0)	198(100.0)	<0.001	154(37.7)	44(18.8)	<0.001
Insulin	23(10.8)	56(13.1)	0.413	46(11.4)	33(13.9)	0.340	61(13.7)	18(9.1)	0.098	47(11.5)	32(13.7)	0.424
RAASi	177(83.1)	358(83.4)	0.910	342(84.4)	193(81.4)	0.323	374(84.2)	161(81.3)	0.359	343(84.1)	192(82.1)	0.509
Cordycepin	76(35.7)	158(36.8)	0.776	132(32.6)	102(43.0)	0.008	190(42.8)	44(22.2)	<0.001	0(0.0)	234(100.0)	<0.001

Continuous variables are expressed as mean ± standard deviation (SD) or as median (interquartile range, IQR), and categorical variables are expressed as n (%). p <0.05 was considered statistically significant. SGLT2i, sodium-glucose cotransporter-2 inhibitor; DPP4i, dipeptidyl peptidase-4 inhibitor; RAASi, renin–angiotensin–aldosterone system inhibitor; BMI, body mass index; DPN, diabetic peripheral neuropathy; LDL, low-density lipoprotein; HDL, high-density lipoprotein; eGFR, estimated glomerular filtration rate; UACR, urinary albumin-to-creatinine ratio; HbA1c, hemoglobin A1c.c

Indicators of disease severity and outcomes by drug class

From the onset of SARS-CoV-2 infection, 504 (78.5%) DKD patients developed fever, 105 (16.4%) progressed to COVID-19 pneumonia, 150 (23.4%) required hospitalization, and 196 (30.5%) experienced COVID-19-related symptoms that persisted for more than seven days. At three months post-infection, 213 (33.2%) patients exhibited a ≥ 30% increase in serum creatinine, 193 (30.1%) experienced a decline in eGFR of ≥10 mL/min/1.73 m², 178 (27.7%) showed a ≥ 30% increase in UACR, 213 (33.2%) recorded an increase in fasting blood glucose of ≥1.1 mmol/L, and 170 (26.5%) had a ≥ 0.3% increase in HbA1c.

As shown in Table 2, patients receiving SGLT2i at baseline exhibited fewer cases of COVID-19 pneumonia (14.2% vs 20.7%, p = 0.038), a significantly lower hospitalization rate (17.5% vs 35.2%, p < 0.001), and reduced incidences of both HbA1c increase ≥0.3% (23.8% vs 31.9%, p = 0.028) and UACR increase ≥30% (22.6% vs 38.0%, p < 0.001) compared with non-users. Metformin users experienced fewer cases of COVID-19 pneumonia (7.1% vs 20.5%, p < 0.001), a shorter duration of symptoms (24.7% vs 33.1%, p = 0.034), and lower rates of HbA1c increase of ≥0.3% (19.7% vs 29.5%, p = 0.009), but a higher incidence of serum creatinine increase ≥30% (44.4% vs 28.2%, p < 0.001). Patients receiving cordycepin at baseline had a lower hospitalization rate (17.1% vs 27.0%, p = 0.004) and fewer cases of serum creatinine increase ≥30% (21.8% vs 39.7%, p < 0.001) compared with non-users. No statistically significant differences in clinical outcomes were observed between users and non-users of DPP4i (Table 2).

Table 2.

Clinical outcomes of COVID-19 by baseline use of SGLT2i, DPP4i, metformin, and cordycepin

Acute COVID-19 outcomes	SGLT2i Non-users n = 213	Users n = 429	p Value	DPP4i Non-users n = 405	Users n = 237	p Value	Metformin Non-users n = 444	Users 198	p Value	Cordycepin Non-users n = 408	Users n = 234	p Value
Fever, n (%)	165(77.5)	339(79.0)	0.651	315(77.8)	189(79.7)	0.558	352(79.3)	152(76.8)	0.474	322(78.9)	182(77.8)	0.734
COVID-19 pneumonia, n (%)	44(20.7)	61(14.2)	0.038	73(18.0)	32(13.5)	0.135	91(20.5)	14(7.1)	<0.001	68(16.7)	37(15.8)	0.778
Hospitalization, n (%)	75(35.2)	75(17.5)	<0.001	100(24.7)	50(21.1)	0.299	112(25.2)	38(19.2)	0.095	110(27.0)	40(17.1)	0.004
Duration of symptoms >7 days, n (%)	75(35.2)	121(28.2)	0.070	127(31.4)	69(29.1)	0.551	147(33.1)	49(24.7)	0.034	122(29.9)	74(31.6)	0.648
Clinical outcomes, three months post-infection versus baseline
Serum creatinine increase ≥30%, n (%)	70(32.9)	143(33.3)	0.905	129(31.9)	84(35.4)	0.351	125(28.2)	88(44.4)	<0.001	162(39.7)	51(21.8)	<0.001
eGFR decrease ≥10 ml/min/1.73 m2, n (%)	69(32.4)	124(28.9)	0.364	121(29.9)	72(30.4)	0.893	133(30.0)	60(30.3)	0.929	127(31.1)	66(28.2)	0.437
UACR increase ≥ 30%, n (%)	81(38.0)	97(22.6)	<0.001	107(26.4)	71(30.0)	0.334	133(30.0)	45(22.7)	0.059	106(26.0)	72(30.8)	0.192
Fasting blood glucose increase ≥1.1 mmol/L, n (%)	73(34.3)	140(32.6)	0.687	132(32.6)	81(34.2)	0.681	147(33.1)	66(33.3)	0.955	128(31.4)	85(36.3)	0.200
HbA1c increase ≥0.3%, n (%)	68(31.9)	102(23.8)	0.028	109(26.9)	61(25.7)	0.745	131(29.5)	39(19.7)	0.009	103(25.2)	67(28.6)	0.349

Categorical variables are expressed as n (%). p <0.05 was considered statistically significant. Abbreviations: SGLT2i, sodium-glucose cotransporter-2 inhibitor; DPP4i, dipeptidyl peptidase-4 inhibitor; eGFR, estimated glomerular filtration rate; UACR, urinary albumin-to-creatinine ratio.

Bold values emphasize results that were found to be statistically significant in the analyses.

Association between medication use and clinical outcomes

Baseline use of SGLT2i among DKD patients prior to SARS-CoV-2 infection was associated with a reduced risk of hospitalization (OR 0.391, 95% CI 0.258–0.592, p < 0.001) and a lower likelihood of a ≥ 30% increase in UACR (OR 0.451, 95% CI 0.305–0.667, p < 0.001), as shown in Table 3. Pretreatment with metformin was linked to a decreased risk of developing COVID-19 pneumonia (OR 0.310, 95% CI 0.165–0.581, p < 0.001), but an increased risk of experiencing a ≥ 30% increase in serum creatinine levels (OR 1.951, 95% CI 1.324–2.876, p < 0.001). Baseline use of cordycepin was associated with a reduced risk of hospitalization (OR 0.543, 95% CI 0.352–0.839, p = 0.006) and a lower incidence of a ≥ 30% increase in serum creatinine levels (OR 0.436, 95% CI 0.295–0.643, p < 0.001). No significant association was observed between baseline use of DPP4i and clinical outcomes of COVID-19 infection (Table 3). Moreover, similar associations between baseline use of hypoglycemic agents or cordycepin and adverse clinical outcomes were identified using multivariable logistic regression analyses following propensity score matching (PSM), as presented in Table S5.

Table 3.

Multivariable logistic regression analysis of the association between baseline use of hypoglycemic agent, cordycepin, and clinical outcomes

Acute COVID-19 outcome	SGLT2i OR (95%CI)	p Value	DPP4i OR (95%CI)	p Value	Metformin OR (95%CI)	p Value	Cordycepin OR (95%CI)	p Value
Fever	1.218(0.769–1.928)	0.401	1.238(0.803–1.909)	0.334	1.052(0.658–1.684)	0.832	0.856(0.551–1.329)	0.488
COVID-19 pneumonia	0.951(0.591–1.528)	0.834	0.672(0.413–1.094)	0.110	0.310(0.165–0.581)	<0.001	0.853(0.531–1.369)	0.509
Hospitalization	0.391(0.258–0.592)	<0.001	0.981(0.648–1.486)	0.930	0.889(0.559–1.414)	0.621	0.543(0.352–0.839)	0.006
Duration of symptoms >7 days	0.872(0.589–1.292)	0.495	0.914(0.625–1.337)	0.643	0.791(0.518–1.207)	0.277	1.047(0.716–1.531)	0.814
Clinical outcomes, three months post-infection versus baseline
Serum creatinine increase ≥30%	0.807(0.548–1.189)	0.279	1.370(0.954–1.969)	0.089	1.951(1.324–2.876)	<0.001	0.436(0.295–0.643)	<0.001
eGFR decrease ≥10 ml/min/1.73 m2	0.809(0.546–1.199)	0.292	1.076(0.742–1.560)	0.700	1.153(0.766–1.736)	0.494	0.828(0.565–1.215)	0.335
UACR increase ≥30%	0.451(0.305–0.667)	<0.001	1.322(0.904–1.933)	0.150	0.961(0.624–1.482)	0.858	1.235(0.843–1.811)	0.279
Fasting blood glucose increase ≥1.1 mmol/L	0.953(0.653–1.388)	0.800	1.080(0.757–1.542)	0.670	1.153(0.778–1.709)	0.478	1.266(0.884–1.813)	0.198
HbA1c increase ≥ 0.3%	0.787(0.530–1.169)	0.236	1.007(0.686–1.479)	0.971	0.668(0.429–1.040)	0.074	1.083(0.737–1.590)	0.686

Models were adjusted for age, sex, BMI, alcohol consumption history, smoking history, stroke history, cancer history, comorbidities, vaccination status, and concomitant medication use. p <0.05 was considered statistically significant. Abbreviations: SGLT2i, sodium-glucose cotransporter-2 inhibitor; DPP4i, dipeptidyl peptidase-4 inhibitor; eGFR, estimated glomerular filtration rate; UACR, urinary albumin-to-creatinine ratio; HbA1c, hemoglobin A1c.

Bold values emphasize results that were found to be statistically significant in the analyses.

Further stratified analyses after PSM were also performed separately for individuals with DKD stages 1–2 (Table S1) and stages 3–4 (Table S2). Baseline use of SGLT2i was associated with a reduced risk of hospitalization and less worsening of UACR in both DKD stage groups. In addition, its use was linked to a decreased risk of an eGFR decline ≥10 mL/min/1.73 m² among patients with DKD stages 3–4 (OR 0.252, 95% CI 0.079–0.805, p = 0.020). Baseline use of cordycepin was associated with lower risks of multiple adverse clinical outcomes among patients with DKD stages 3–4, including COVID-19 pneumonia (OR 0.232, 95% CI 0.058–0.933, p = 0.040), hospitalization (OR 0.341, 95% CI 0.121–0.963, p = 0.042) and a ≥ 30% increase in serum creatinine (OR 0.344, 95% CI 0.146–0.811, p = 0.015).

Indicators of disease severity and outcomes by vaccination status

Among 642 DKD patients infected with SARS-CoV-2, 136 (21.2%) were unvaccinated against COVID-19, 96 (15.0%) had received a single dose of the Sinovac or Sinopharm vaccine, and 410 (63.8%) had received two or more doses. No significant differences were observed among the three groups in age, sex, BMI, comorbidities, and key laboratory parameters, including serum creatinine, eGFR, UACR, and blood glucose (Table 4). As shown in Table 5, among DKD patients infected with SARS-CoV-2, there was no significant difference in clinical outcomes between unvaccinated individuals and those who received a single vaccine dose. However, patients who received two or more vaccine doses exhibited significantly better clinical outcomes compared with both unvaccinated patients and those who received a single dose. Specifically, compared with unvaccinated patients, those who received ≥ two vaccine doses had fewer cases of fever (70.5% vs 94.9%, p < 0.001), COVID-19 pneumonia (11.2% vs 25.7%, p < 0.001), and symptoms persisting for more than seven days (21.5% vs 47.1%, p < 0.001), they also had reduced incidences of eGFR decline ≥10 mL/min/1.73 m (26.6% vs 38.2%, p = 0.010), fasting blood glucose increase ≥1.1 mmol/L (28.5% vs 44.9%, p < 0.001) and HbA1c increase ≥ 0.3% (22.0% vs 39.0%, p < 0.001) at three months post-infection (Table 5).

Table 4.

Baseline clinical characteristics of patients with diabetic kidney disease according to vaccination status

	Unvaccinated n = 136	One dose n = 96	≥Two doses n = 410	p Value
Age, years	63.50(51.00, 73.00)	59.50(49.00, 70.75)	60.00(51.00, 73.00)	0.553
Men, n (%)	81(59.6)	62(64.6)	255(62.2)	0.733
BMI, kg/m²	23.05(21.52, 24.64)	23.31 ± 2.95	23.43(21.66, 25.96)	0.121
Smoker, n (%)	41(30.1)	31(32.3)	150(36.6)	0.344
Drinker, n (%)	30(22.1)	35(36.5)	113(27.6)	0.054
Diabetic retinopathy, n (%)	71(52.2)	56(58.3)	199(48.5)	0.209
DPN, n (%)	73(53.7)	58(60.4)	200(48.8)	0.104
Hypertension, n (%)	104(76.5)	64(66.7)	300(73.2)	0.249
Cardiovascular disease, n (%)	68(50.0)	37(38.5)	173(42.2)	0.167
Cerebral stroke, n (%)	14(10.3)	8(8.3)	29(7.1)	0.479
Cancer, n (%)	16(11.8)	9(9.4)	32(7.8)	0.365
Triglycerides, mmol/L	1.49(1.05, 2.86)	1.56(0.99, 2.69)	1.65(1.09, 2.87)	0.519
Total cholesterol, mmol/L	4.41 ± 1.23	4.13(3.50, 5.43)	4.42(3.56, 5.41)	0.398
LDL, mmol/L	2.89 ± 0.96	2.60(2.15, 3.29)	2.77(2.21, 3.45)	0.544
HDL, mmol/L	1.12(0.96, 1.37)	1.17(1.00, 1.44)	1.15(0.97, 1.38)	0.180
Serum uric acid, μmol/L	341.00(285.50, 398.48)	335.10(288.78, 379.50)	345.40(290.98, 416.05)	0.441
Serum creatinine, μmol/L	81.60(64.68, 110.60)	74.97(60.06, 110.07)	86.64(69.96, 104.92)	0.063
eGFR, ml/min/1.73 m2	81.15(53.05, 90.00)	84.87(57.87, 95.00)	80.00(54.00, 88.81)	0.082
Blood urea nitrogen, mmol/L	6.69(5.20, 8.49)	6.55(5.57, 8.15)	6.50(5.10, 8.50)	0.763
UACR, mg/g	156.23(41.47, 568.60)	105.27(37.06, 326.28)	91.32(26.16, 370.80)	0.137
Fasting blood glucose, mmol/L	7.65(6.54, 10.02)	7.44(6.56, 9.46)	7.81(6.35, 10.29)	0.821
HbA1c, %	6.90(6.30, 7.60)	6.95(6.30, 8.08)	6.90(6.20, 8.32)	0.918
Baseline medication use, n (%)
SGLT2i	79(58.1)	60(62.5)	290(70.7)	0.016
DPP4i	45(33.1)	34(35.4)	158(38.5)	0.494
Metformin	28(20.6)	26(27.1)	144(35.1)	0.004
Insulin	18(13.2)	12(12.5)	49(12.0)	0.923
RAASi	115(84.6)	82(85.4)	338(82.4)	0.711
Cordycepin	62(45.6)	29(30.2)	143(34.9)	0.031

Continuous variables are presented as mean ± SD or median (interquartile range, IQR), categorical variables are expressed as n (%). p <0.05 was considered statistically significant. Abbreviations: BMI, body mass index; DPN, diabetic peripheral neuropathy; LDL, low-density lipoprotein; HDL, high-density lipoprotein; eGFR, estimated glomerular filtration rate; UACR, urinary albumin-to-creatinine ratio; HbA1c, hemoglobin A1c; SGLT2i, sodium–glucose cotransporter-2 inhibitor; DPP4i, dipeptidyl peptidase-4 inhibitor; RAASi, renin–angiotensin–aldosterone system inhibitor.

Table 5.

Clinical outcomes of COVID-19 according to vaccination status

Acute COVID-19 outcomes	Unvaccinated n = 136	One dose n = 96	p Value	One dose n = 96	≥ Two doses n = 410	p Value	Unvaccinated n = 136	≥ Two doses n = 410	p Value
Fever, n (%)	129(94.9)	86(89.6)	0.129	86(89.6)	289(70.5)	<0.001	129(94.9)	289(70.5)	<0.001
COVID-19 pneumonia, n (%)	35(25.7)	24(25.0)	0.899	24(25.0)	46(11.2)	<0.001	35(25.7)	46(11.2)	<0.001
Hospitalization, n (%)	37(27.2)	29(30.2)	0.618	29(30.2)	84(20.5)	0.040	37(27.2)	84(20.5)	0.102
Duration of symptoms >7 days, n (%)	64(47.1)	44(45.8)	0.854	44(45.8)	88(21.5)	<0.001	64(47.1)	88(21.5)	<0.001
Clinical outcomes, three months post-infection versus baseline
Serum creatinine increase ≥30%, n (%)	41(30.1)	33(34.4)	0.496	33(34.4)	139(33.9)	0.930	41(30.1)	139(33.9)	0.419
eGFR decrease ≥10 ml/min/1.73 m, n (%)	52(38.2)	32(33.3)	0.444	32(33.3)	109(26.6)	0.184	52(38.2)	109(26.6)	0.010
UACR increase ≥30%, n (%)	44(32.4)	21(21.9)	0.080	21(21.9)	113(27.6)	0.256	44(32.4)	113(27.6)	0.285
Fasting blood glucose increase ≥1.1 mmol/L, n (%)	61(44.9)	35(36.5)	0.201	35(36.5)	117(28.5)	0.127	61(44.9)	117(28.5)	<0.001
HbA1c increase ≥ 0.3%, n (%)	53(39.0)	27(28.1)	0.087	27(28.1)	90(22.0)	0.197	53(39.0)	90(22.0)	<0.001

Categorical variables are expressed as n (%). p <0.05 was considered statistically significant. Abbreviations: eGFR, estimated glomerular filtration rate; UACR, urinary albumin-to-creatinine ratio; HbA1c, hemoglobin A1c.

Bold values emphasize results that were found to be statistically significant in the analyses.

Association between vaccination status and clinical outcomes

In multivariable logistic regression analysis, receiving two or more doses of the COVID-19 vaccine was associated with significantly better clinical outcomes compared with being unvaccinated or receiving one dose. As shown in Table 6, compared with single-dose vaccination, receiving multiple vaccine doses was associated with reduced risks of developing fever (OR 0.237, 95% CI 0.116–0.488, p < 0.001), COVID-19 pneumonia (OR 0.418, 95% CI 0.232–0.752, p = 0.004), and experiencing symptoms for more than seven days among DKD patients (OR 0.313, 95% CI 0.193–0.509, p < 0.001). Compared with unvaccinated DKD patients, receiving multiple vaccine doses was linked to reduced risks of adverse clinical outcomes, including fever (OR 0.123, 95% CI 0.055–0.276, p < 0.001), COVID-19 pneumonia (OR 0.388, 95% CI 0.232–0.649, p < 0.001), prolonged symptoms (OR 0.315, 95% CI 0.205–0.483, p < 0.001), an eGFR decline of ≥10 mL/min/1.73 m² (OR 0.529, 95% CI 0.342–0.820, p = 0.004), a fasting blood glucose increase of ≥1.1 mmol/L (OR 0.492, 95% CI 0.325–0.746, p < 0.001) and a HbA1c increase of ≥ 0.3% (OR 0.468, 95%CI 0.303–0.721, p < 0.001). No significant differences in clinical outcomes were observed between unvaccinated patients and those who received a single vaccine dose (Table 6). In addition, similar associations between vaccination status and adverse clinical outcomes were identified through multivariable logistic regression analyses after PSM (Table S6).

Table 6.

Multivariable logistic regression analysis of the association between vaccination status and clinical outcomes

Acute COVID-19 outcomes	One dose vs Unvaccinated		≥Two doses vs One dose		≥Two doses vs Unvaccinated
	OR (95%CI)	p Value	OR (95%CI)	p Value	OR (95%CI)	p Value
Fever	0.519(0.186–1.449)	0.210	0.237(0.116–0.488)	<0.001	0.123(0.055–0.276)	<0.001
COVID-19 pneumonia	0.928(0.488–1.767)	0.821	0.418(0.232–0.752)	0.004	0.388(0.232–0.649)	<0.001
Hospitalization	1.094(0.589–2.034)	0.775	0.655(0.385–1.116)	0.120	0.717(0.444–1.157)	0.173
Duration of symptoms >7 days	1.005(0.581–1.736)	0.987	0.313(0.193–0.509)	<0.001	0.315(0.205–0.483)	<0.001
Clinical outcomes, three months post-infection versus baseline
Serum creatinine increase ≥30%	1.031(0.574–1.853)	0.919	0.948(0.579–1.551)	0.831	0.977(0.625–1.526)	0.919
eGFR decrease ≥10 ml/min/1.73 m2	0.706(0.396–1.260)	0.239	0.749(0.453–1.240)	0.261	0.529(0.342–0.820)	0.004
UACR increase ≥30%	0.666(0.354–1.252)	0.207	1.410(0.808–2.459)	0.226	0.939(0.603–1.461)	0.780
Fasting blood glucose increase ≥1.1 mmol/L	0.706(0.406–1.227)	0.217	0.697(0.431–1.129)	0.143	0.492(0.325–0.746)	<0.001
HbA1c increase ≥ 0.3%	0.632(0.352–1.135)	0.125	0.740(0.439–1.245)	0.256	0.468(0.303–0.721)	<0.001

Models were adjusted for age, sex, BMI, alcohol consumption history, smoking history, stroke history, cancer history, comorbidities and medication use. p <0.05 was considered statistically significant. Abbreviations: eGFR, estimated glomerular filtration rate; UACR, urinary albumin-to-creatinine ratio; HbA1c, hemoglobin A1c.

Bold values emphasize results that were found to be statistically significant in the analyses.

Further stratified analyses following PSM were conducted for patients with DKD stages 1–2 (Table S3) and 3–4 (Table S4). Compared with unvaccinated patients, receiving multiple doses of COVID-19 vaccine was associated with lower risks of fever and a fasting blood glucose increase of ≥1.1 mmol/L in both DKD stage groups. However, the association between receiving multiple vaccine doses and reduced risks of adverse clinical outcomes was more pronounced among patients with DKD stages 1–2 than among those with stages 3–4.

Discussion

This retrospective cohort study examined Chinese DKD patients infected with SARS-CoV-2. Our findings revealed that baseline use of SGLT2i, metformin, the renoprotective agent cordycepin, and COVID-19 vaccination were associated with reductions in various adverse clinical outcomes related to COVID-19 in this cohort. Based on these results, the proposed impacts of hypoglycemic medications, cordycepin, and COVID-19 vaccination on clinical outcomes among DKD patients with COVID-19 are summarized in Table 7. Notably, although metformin use was linked to an increased risk of elevated serum creatinine, it was also associated with protective effects against COVID-19 pneumonia. The results also suggested potential beneficial effects of SGLT2i, cordycepin, and COVID-19 vaccination in DKD patients with COVID-19, particularly their protective properties against severe COVID-19 requiring hospitalization, and in mitigating the exacerbation of kidney disease or diabetes. However, DPP4i appeared neutral and showed no significant effect on the clinical outcomes among DKD patients with COVID-19.

Table 7.

Proposed impact of hypoglycemic medications, cordycepin, and COVID-19 vaccination on clinical outcomes in DKD patients with COVID-19.

Intervention class	Duration of COVID-19 symptoms	Risk of hospitalization	Risk of COVID-19 pneumonia	Risk of Increased proteinuria	Risk of reduced kidney function	Risk of elevated blood glucose
SGLT2 inhibitors	Neutral	Decreased	Neutral	Decreased	Neutral	Neutral
DPP4 inhibitors	Neutral	Neutral	Neutral	Neutral	Neutral	Neutral
Metformin	Neutral	Neutral	Decreased	Neutral	Increased	Neutral
Cordycepin	Neutral	Decreased	Neutral	Neutral	Decreased	Neutral
Multi-dose COVID-19 vaccination	Decreased	Neutral	Decreased	Neutral	Decreased	Decreased

Adverse clinical outcomes of COVID-19 were categorized as acute or short-term. Acute outcomes included the duration of COVID-19 infection, hospitalization and the risk of COVID-19 pneumonia. Short-term outcomes, assessed three months post-infection, comprised increased proteinuria (UACR increase ≥ 30%), kidney function decline (serum creatinine increase ≥ 30% or eGFR decrease ≥ 10 ml/min/1.73 m2), and elevated blood glucose (fasting blood glucose increase ≥ 1.1 mmol/L or HbA1c increase ≥ 0.3%). This table summarizes the potential impact of hypoglycemic medications, cordycepin, and COVID-19 vaccination on these outcomes in DKD patients with COVID-19. Abbreviations: SGLT2i, sodium-glucose cotransporter-2 inhibitor; DPP4i, dipeptidyl peptidase-4 inhibitor.

The beneficial impact of SGLT2 inhibitors on COVID-19 outcomes, including their ability to suppress systemic inflammation and fibrosis, has been reported in several studies [26,27]. In a recent meta-analysis [28] examining various hypoglycemic agents in COVID-19 patients with diabetes, treatment with SGLT2i was associated with lower mortality. Additional studies [29,30] have suggested that SGLT2i may confer cardioprotective and renoprotective benefits in individuals with type 2 diabetes, thereby offering protection against COVID-19-induced organ damage. These findings are consistent with the results of the present study, which suggest potential renoprotective effects of SGLT2i in DKD patients with COVID-19, particularly in reducing proteinuria.

The present study revealed no significant association between DPP4i use and the clinical prognosis of COVID-19, consistent with several previous investigations [31,32] that similarly failed to identify a link between DPP4i administration and COVID-19 outcomes. However, a cohort study of diabetic patients with COVID-19 reported a reduced mortality rate among those treated with DPP4i [33]. These conflicting findings suggest that the association between DPP4i and COVID-19 outcomes remains inconclusive, underscoring the need for further investigations to elucidate this relationship.

Metformin has been reported to exert protective effects on vascular endothelial cells by reducing oxidative stress, inflammation, and leukocyte-endothelium interactions [34]. It has also been shown to possess immunomodulatory properties that may attenuate the hyperactive immune response observed in COVID-19, thereby contributing to the prevention of cytokine storms and severe pulmonary impairment [35]. A recent meta-analysis [36] reported an association between metformin use and reduced mortality following SARS-CoV-2 infection. Moreover, metformin has been reported to reduce the incidence of acute lung injury in diabetic patients with COVID-19 [37]. In the present study, the potential of metformin to reduce the risk of developing COVID-19 pneumonia was suggested in DKD patients. However, a potential adverse effect of metformin on renal function was also indicated in these patients. Notably, other studies have similarly reported adverse outcomes linked to metformin use in COVID-19. Evidence has suggested that administration of high-dose of metformin may exacerbate renal impairment in COVID-19 patients and is associated with an increased risk of acidosis, particularly in severe cases [38]. These findings underscore the importance of close monitoring of acidosis and renal function in COVID-19 patients treated with metformin [39].

Cordycepin, a traditional Chinese medicine, has demonstrated substantial renoprotective effects in DKD patients [16,40] as well as notable therapeutic potential against COVID-19 [41]. Both in vitro and in vivo studies have indicated that cordycepin treatment protected renal cells from high-glucose-induced renal injury by attenuating oxidative stress, inflammation, and fibrosis, while also promoting renal cell autophagy and inhibiting apoptosis [16,23,40]. Notably, nucleosides and ergosterols have been identified as the principal active components responsible for its antioxidant and antifibrotic effects [23]. Furthermore, Singh et al. [42] reported that the steroids present in cordycepin may mitigate the cytokine storm associated with COVID-19, thereby reducing inflammatory complications in affected patients. In addition, ergosterol has been recognized as a highly efficacious and promising compound in cordycepin against SARS-CoV-2 [43]. To our knowledge, the present study is the first to demonstrate a significant association between cordycepin use and reduced hospitalization rates as well as a lower incidence of renal function impairment among DKD patients with COVID-19. Collectively, these findings suggest that cordycepin’s diverse therapeutic properties, including its antioxidant, antifibrotic, immunoregulatory, and anti-inflammatory effects, render it a promising therapeutic candidate for managing DKD and COVID-19.

The rapid distribution of COVID-19 vaccines has substantially reduced both the incidence and mortality from the disease [18]. Multiple studies have demonstrated that individuals with diabetes [44,45] and chronic kidney disease (CKD) [46] are at increased risk of severe complications or death from COVID-19, underscoring the importance of vaccination for these populations. COVID-19 vaccination has been shown to be safe and effective in patients with DKD, producing favorable outcomes with respect to potential adverse reactions [6]. The present study further supports the efficacy of the COVID-19 vaccination in DKD patients, particularly in those receiving multiple vaccine doses. Pre-vaccination against COVID-19 was strongly associated with reduced severity of infection, and may also help preserve renal function and glycemic control.

Unlike previous studies that primarily assessed the impact of medications and vaccines on acute clinical outcomes of diabetic patients with COVID-19, this study investigated the short-term effects of hypoglycemic agents, cordycepin and COVID-19 vaccination on adverse renal and glycemic outcomes among DKD patients three months post-infection. However, several limitations of this study should be acknowledged. First, as a retrospective observational cohort study, it was inherently subject to unmeasured confounding and potential observational biases. Second, the relatively small sample size may limit the precision of effect estimates in subgroup analyses. Third, the analysis focused on the three-month impact of medications and vaccination on adverse outcomes in DKD patients with COVID-19. Whether the administration of these medications and vaccines exerts long-term effects on renal and diabetic outcomes, particularly in patients with long COVID, warrants further investigation with extended follow-up. Fourth, the study population comprised Chinese DKD patients infected with the SARS-CoV-2 Omicron variant between December 2022 and June 2023, predominantly non-severe cases with low mortality, which may limit the generalizability of the findings to other ethnic groups or viral variants. Finally, although multivariable logistic regression was employed and study drugs were included as covariates to mitigate confounding, the possible influence of concomitant baseline medications could not be completely excluded. Consequently, causality between hypoglycemic agents, cordycepin, COVID-19 vaccination, and clinical outcomes cannot be established.

In conclusion, this retrospective cohort study conducted among Chinese patients with DKD and COVID-19 demonstrated that baseline use of SGLT2i, cordycepin, and COVID-19 vaccination was associated with reduced adverse clinical outcomes related to COVID-19. However, potential concerns were raised regarding metformin, as its use appeared protective against COVID-19 but was linked to adverse effects on renal function in these patients. Given the ongoing periodic outbreaks of COVID-19 and the emergence of new variants, this study provides crucial evidence and novel insights for optimizing the management of DKD patients during the pandemic. The findings underscore the need for tailored therapeutic strategies to mitigate risks and improve clinical outcomes in this vulnerable population.

Funding Statement

This work was supported by grants from the Ningbo Natural Science Foundation [Grant No. 2024J344] and Zhejiang Medical Technology Project [Grant No. 2021KY274].

Ethical approval

The study was approved by the Ethics Committee of The First Affiliated Hospital of Ningbo University (Ethics Approval No: 2024132RS01), and was conducted in accordance with the Declaration of Helsinki treaty.

Authors’ contributions

M.Z. and Y.L. designed and conducted the study, analyzed the data, and drafted the manuscript. X.Y. and J.X. were involved in conducting the investigations, collecting and analyzing the data. R.H. provided statistical support, reviewed and edited the manuscript; A.R. and M.S. contributed to the discussion and reviewed and edited the manuscript.

Disclosure statement

The authors declare no conflict of interest associated with the paper.

Data availability statement

The data presented in this study are available upon request from the corresponding author.