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Article

Effect of Influenza Vaccination on the Reduction of the Incidence of Chronic Kidney Disease and Dialysis in Patients with Type 2 Diabetes Mellitus

by
Li-Chin Sung
1,2,3,4,†,
Chun-Chao Chen
1,2,3,5,
Shih-Hao Liu
4,†,
Chun-Chih Chiu
1,2,
Tsung-Yeh Yang
1,2,
Cheng-Hsin Lin
2,6,7,
Yu-Ann Fang
1,2,
William Jian
8,
Meng-Huan Lei
9,
Hsien-Tang Yeh
10,
Min-Huei Hsu
11,12,
Wen-Rui Hao
1,2,3,* and
Ju-Chi Liu
1,2,3,*
1
Division of Cardiology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235, Taiwan
2
Taipei Heart Institute, Taipei Medical University, Taipei 110, Taiwan
3
Division of Cardiology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
4
Department of Primary Care Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235, Taiwan
5
Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
6
Division of Cardiovascular Surgery, Department of Surgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235, Taiwan
7
Division of Cardiovascular Surgery, Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
8
Department of Emergency, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
9
Cardiovascular Center, Lo-Hsu Medical Foundation Luodong Poh-Ai Hospital, Yilan 265, Taiwan
10
Department of Surgery, Lotung Poh-Ai Hospital, Luodong 265, Taiwan
11
Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 110, Taiwan
12
Department of Neurosurgery, Wan-Fang Hospital, Taipei Medical University, Taipei 110, Taiwan
 
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*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Clin. Med. 2022, 11(15), 4520; https://doi.org/10.3390/jcm11154520
Submission received: 5 July 2022 / Revised: 31 July 2022 / Accepted: 1 August 2022 / Published: 3 August 2022
(This article belongs to the Section Endocrinology & Metabolism)
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Abstract

:
Patients with type 2 diabetes mellitus (T2DM) have a higher risk of chronic kidney disease (CKD) due to vascular complications and chronic inflammation. T2DM contributes to a higher risk of mortality and morbidity related to influenza. In Taiwan, influenza vaccination is recommended for patients with T2DM. A previous meta-analysis reported the efficacy of influenza vaccination in reducing hospitalization and mortality in patients with diabetes; however, the renal protective effect of the vaccine remains unclear. This study evaluated whether influenza vaccination could reduce the incidence of CKD and dialysis in patients with T2DM. The study cohort included all patients aged ≥55 years who were diagnosed as having T2DM between 1 January 2000 and 31 December 2012, by using data from Taiwan’s National Health Insurance Research Database. Each patient was followed up with to assess factors associated with CKD. A time-dependent Cox proportional hazard regression model after adjustment for potential confounders was used to calculate the hazard ratio (HR) of CKD in the vaccinated and unvaccinated patients. The study population comprised 48,017 eligible patients with DM; 23,839 (49.7%) received influenza vaccination and the remaining 24,178 (50.3%) did not. The adjusted HRs (aHRs) for CKD/dialysis decreased in the vaccinated patients compared with the unvaccinated patients (influenza season, noninfluenza season, and all seasons: aHRs: 0.47/0.47, 0.48/0.49, and 0.48/0.48, respectively, all p < 0.0001). We observed similar protective effects against CKD during the influenza and noninfluenza seasons. Regardless of comorbidities or drug use, influenza vaccination was an independent protective factor. Furthermore, aHRs for CKD/dialysis were 0.71 (0.65–0.77)/0.77 (0.68–0.87), 0.57 (0.52–0.61)/0.69 (0.56–0.70), and 0.30 (0.28–0.33)/0.28 (0.24–0.31) in the patients who received 1, 2–3, and ≥4 vaccinations during the follow-up period, respectively. This population-based cohort study demonstrated that influenza vaccination exerts a dose-dependent and synergistic protective effect against CKD in the patients with T2DM with associated risk factors.

1. Introduction

Type 2 diabetes mellitus (T2DM) is a leading cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) [1,2]. The prevalence of T2DM in adults is 12.3% and 10.1% in the United States and Taiwan, respectively [3,4]. Patients with T2DM have a high risk of microvascular complications, such as nephropathy, neuropathy, and retinopathy [5]. CKD, as a manifestation of nephropathy, develops in approximately 40% of patients with T2DM and contributes to high mortality and morbidity [6]. The prevalence of not only CKD but also ESRD in Taiwan is the highest worldwide [7,8]. Diabetic kidney disease (DKD) is the leading etiology of CKD development in patients with T2DM [1], with numerous overlapping etiologic pathways, including changes in glomerular hemodynamics, interstitial fibrosis, and tubular atrophy [9]. The natural course of DKD often causes irreversible renal function impairment, resulting in the requirement of dialysis and a poor quality of life [9]. According to the United States Renal Data System, DKD is the single strongest predictor of mortality in patients with T2DM [10,11]. Thus, controlling the development of CKD and ESRD in patients with T2DM is essential.
T2DM increases the risk of severe influenza infection that can lead to higher mortality and morbidity [12]. Severe influenza infection can cause renal complications such as acute kidney injury (AKI) and progression to CKD [13,14]. A prospective cohort study demonstrated that the risk of AKI was higher in patients with T2DM and pandemic influenza A (H1N1) infection, with the prevalence of AKI being 16.2% and 9.6% in the AKI and non-AKI groups, respectively [15]. Moreover, the presence of AKI, which is secondary to rhabdomyolysis, was reported in an 84-year-old man with T2DM and influenza A (H3N2) infection [16]. These findings demonstrate the link between influenza infection and renal impairment in the T2DM population. Considering current influenza treatments, seasonal influenza vaccination is beneficial in minimizing the risks of death and hospitalization from influenza complications in patients with T2DM [17]. A meta-analysis reported the efficacy of the vaccines in reducing hospitalization and mortality in the diabetic population [12]. However, whether influenza vaccines can reduce the incidence of CKD and ESRD in patients with T2DM remains unclear.
The influenza vaccination policy in Taiwan has gradually focused on high-risk populations and key spreaders to effectively reduce the number of influenza cases and deaths. Patients with T2DM as a high-risk population for influenza are considered a priority group for vaccination every year in accordance with the recommendations of the Advisory Committee on Immunization Practices [18]. This study investigated whether the protective benefit of influenza vaccination can reduce the incidence of CKD and ESRD in patients with T2DM. We used data from the National Health Insurance (NHI) Research Database (NHIRD) to determine the association among influenza vaccination, CKD incidence, and dialysis rate in patients with T2DM to evaluate whether influenza vaccination provides renal protection in patients with T2DM.

2. Materials and Methods

The NHI program, which was launched in 1995, currently provides comprehensive health insurance coverage to 98% of >23 million people. In this study, we used data from the NHIRD. No significant differences in age, sex, or health-care costs were observed between the study sample and all enrollees [19]. Data in the NHIRD that might be used to identify patients or care providers, including medical institutions and physicians, are encrypted before being sent to the National Health Research Institutes for database construction and are further encrypted before being released to researchers. In other words, querying data alone to identify individuals at any level by using this database is not possible. All researchers using the NHIRD and its subsets are required to sign a written agreement declaring that they have no intention of attempting to obtain information that can violate the privacy of patients or care providers [20]. This study was approved by the Joint Institutional Review Board of Taipei Medical University (approval no. TMUJIRB N201804043).
This study screened all patients who were diagnosed as having diabetes mellitus based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 250.X and who visited health-care facilities in Taiwan over 13 years (n = 151,605) from 1 January 2000 to 31 December 2012. In the first part, we excluded 98,785 patients due to the following reasons: patients had less than 3 inpatient or outpatient visits related to the diagnosis of diabetes mellitus within 2 years (n = 51,124), patients were aged <55 years (n = 43,742), and patients were diagnosed as having type 1 DM (n = 3919). In the second part, we excluded 4803 patients due to the following reasons: patients had any inpatient or outpatient diagnosis related to CKD before the date of cohort entry (n = 2312), patients had any inpatient or outpatient diagnosis related to dialysis before the date of cohort entry (n = 36), patients had any inpatient or outpatient diagnosis related to renal transplantation before the date of cohort entry (n = 4), and patients had already received vaccination within 6 months before the date of cohort entry (n = 2451; Figure 1). In addition, a 1-year washout period (2000) was included to ensure that all patients in this cohort had no CKD or dialysis before enrollment.
In Taiwan, influenza vaccination is provided free of charge and has been recommended for high-risk adults aged ≥50 years (i.e., those with type 2 diabetes, chronic liver infection or cirrhosis, cardiovascular disease, or chronic pulmonary diseases) since 1998 [21]. The vaccination status was recognized based on the presence of code V048 or the use of the vaccine (confirmed by drug codes). We determined the presence of the following comorbidities in each patient: hypertension, cerebrovascular diseases, dyslipidemia, heart diseases, hepatitis B virus, hepatitis C virus, cirrhosis, moderate and severe liver disease, and asthma. Moreover, we collected information regarding antidiabetic medications, co-medications, urbanization level, and monthly income. The primary endpoints of our study were the incidence of CKD (ICD-9-CM code 585.X) and the requirement of dialysis (NHI procedure codes) in patients with T2DM. All cohorts were followed up until the date of the diagnosis of CKD, dialysis, death, disenrollment from the NHI, or the end of 2012.

3. Statistical Analysis

A propensity score (PS) is used to reduce selection bias and estimate the effect of vaccination by accounting for covariates that predict receiving the intervention (vaccine) by using a logistic regression model [22]. Covariates in the main model were adjusted for PSs for age; sex; hypertension; dyslipidemia; cerebrovascular diseases; heart diseases; hepatitis B virus; hepatitis C virus; cirrhosis; moderate and severe liver disease; asthma; the use of insulin and analogs, biguanides, sulfonamides, urea derivatives, alpha-glucosidase inhibitors, thiazolidinediones, dipeptidyl peptidase 4, other blood glucose–lowering drugs, statins, aspirin, angiotensin-converting enzyme inhibitors, and angiotensin receptor blockers; number of antidiabetic medications; urbanization level; and monthly income (Table 1). Categorical variables were compared using the chi-square test to determine the significance of differences between the vaccinated and unvaccinated groups in terms of the relationship among characteristics listed in Table 1. The unvaccinated group served as the reference arm. The hazard ratio (HR) and 95% confidence interval (CI) for the association between influenza vaccination and the risks of CKD and dialysis in patients with T2DM were calculated using Cox proportional hazards regression. To examine the dose effect of influenza vaccination on the incidence of CKD and dialysis, we categorized patients into four groups by vaccination status: unvaccinated and those receiving 1, 2–3, and ≥4 vaccinations, respectively. These data were stratified according to patients’ age, sex, comorbidity, and associated medication use. Sensitivity analysis was performed to evaluate the difference and consistency between the use of influenza vaccination and the risks of CKD and dialysis in patients with T2DM. All statistical analyses were performed using SPSS 22.0 and SAS 9.4 software. A p-value of <0.05 indicated statistical significance.

4. Results

4.1. Comparison of Baseline Characteristics between the Vaccinated and Unvaccinated Groups

Among the 48,017 eligible individuals enrolled in our cohort study, 49.6% (n = 23,839) received the influenza vaccination and the remaining 50.3% (n = 24,178) did not receive the influenza vaccination. We noted significant differences (p < 0.001) in the distributions of age, sex, preexisting medical comorbidities, antidiabetic medications, number of antidiabetic medications, comorbidity-associated medication use, urbanization level, and monthly income between the groups. The vaccinated group included higher proportions of older and female patients and had a higher prevalence of underlying comorbidities, including hypertension, cerebrovascular diseases, coronary artery diseases, and cirrhosis, before PS adjustment. In addition, the vaccinated group tended to receive different types of antidiabetic medication simultaneously. Moreover, they also received comorbidity-associated medication for longer periods (Table 1).

4.2. Differences in Risks of CKD and Dialysis between the Vaccinated and Unvaccinated Groups

The incidence rate of CKD was significantly lower in the vaccinated group (adjusted HR: 0.48, 95% CI: 0.45–0.50, p < 0.001) than in the unvaccinated group (Table 2). Similar protective effects were observed in both sexes and all age groups, irrespective of influenza seasonality. The adjusted HR for the incidence rate of CKD was 0.45 (95% CI: 0.41–0.50, p < 0.001) and 0.44 (95% CI: 0.41–0.47, p < 0.001) in the subgroups of the patients aged 55–64 and ≥65 years, respectively. The protective effect of the influenza vaccination was stronger in the women than in the men, with adjusted HRs of 0.45 (95% CI: 0.41–0.49, p < 0.001) in the women and 0.50 (95% CI: 0.46–0.54, p < 0.001) in the men in the vaccinated group compared with the unvaccinated group.
The dialysis rate was lower in the vaccinated group (adjusted HR: 0.48, 95% CI: 0.44–0.52, p < 0.001) than in the unvaccinated group (Table 3). A similar trend was noted in both sexes and all age groups, irrespective of influenza seasonality. The adjusted HRs were 0.44 (95% CI: 0.38–0.50, p < 0.001) and 0.47 (95% CI: 0.42–0.53, p < 0.001) in the subgroups of the patients aged 55–64 and ≥65 years, respectively. Similar to the previous finding, the protective effect of the influenza vaccination was stronger in the women than in the men. The men had a higher incidence of dialysis than did the women, with an incidence rate of 799.1 (95% CI: 727.9.2–870.3) and 554.0 (95% CI: 506.8–601.2) per 105 person-years for the unvaccinated and vaccinated men, respectively, and 757.6 (95% CI: 693.4–821.8) and 490.6 (95% CI: 451.6–529.7) per 105 person-years for the unvaccinated and vaccinated women, respectively.

4.3. Sensitivity Analysis

In the sensitivity analysis, we adjusted for potential confounders that affected the evaluation between influenza vaccination use and the risk of CKD and dialysis in different models. Potential confounders included comorbidities, demographic variables, and socioeconomic status. When stratified according to the total number of vaccinations, Table 4 and Table 5 demonstrated the protective effects of vaccinated patients in the main model and various subgroups.
As shown in Table 4, we observed a significant vaccine protective effect for the reduction of CKD risk in the main model in the patients who received 1, 2–3, and ≥4 vaccinations during the follow-up period, with the adjusted HRs of 0.71 (95% CI: 0.65–0.77, p < 0.001), 0.57 (95% CI: 0.52–0.61, p < 0.001), and 0.30 (95% CI: 0.28–0.33, p < 0.001), respectively. The result exhibited a similar protective effect in the various subgroups (Table 4). In the main model and various subgroups, our study demonstrated a dose-dependent protective effect of CKD risk reduction. Moreover, in the patients who received ≥4 vaccinations, those aged >65 years presented a lower CKD risk than did those aged <65 years, with the adjusted HRs of 0.25 (95% CI: 0.23–0.28, p < 0.001) and 0.31 (95% CI 0.27–0.36, p < 0.001), respectively.
We found a significant vaccine protective effect for the reduction of dialysis risk in the main model in the patients who received 1, 2–3, and ≥4 vaccinations during the follow-up period, with the adjusted HRs of 0.77 (95% CI: 0.68–0.87, p < 0.001), 0.63 (95% CI: 0.56–0.70, p < 0.001), and 0.28 (95% CI: 0.24–0.31, p < 0.001; Table 5), respectively. Among the various subgroups, the result showed a similar significant vaccine protective effect. In addition, in the main model and various subgroups, we observed a dose-dependent protective effect of dialysis risk reduction (p for trend < 0.001). A more powerful trend in the reduction of dialysis risk was observed in the group aged >65, with the adjusted HRs of 0.82 (95% CI: 0.70–0.97, p < 0.001) to 0.25 (95% CI: 0.21–0.29; p < 0.001) than in the group aged <65 years, with the adjusted HRs of 0.63 (95% CI: 0.52–0.77, p < 0.001) to 0.29 (95% CI: 0.23–0.36, p < 0.001).

5. Discussion

The results of this population-based cohort study revealed that the patients with T2DM who received the influenza vaccination had lower risks of CKD and ESRD. In our study, patients in the vaccinated group were older, had more preexisting medical comorbidities, used comorbidity-associated medications for longer periods, and used more antidiabetic medications than did the unvaccinated group (Table 1). Patients with T2DM as a high-risk population for influenza and mortality are considered a priority group for vaccination every year in Taiwan [18]. The protective effect of vaccination could still be observed in the vaccinated group compared with the unvaccinated group. Moreover, we observed differences in various factors, including both age and sex, between the groups (Table 2 and Table 3). This is the first population-based cohort study to demonstrate that the protective effect of vaccination can reduce the incidence of CKD and ESRD in the patients with T2DM.
Two mechanisms might explain the main finding of decreased CKD and ESRD risks following influenza vaccination in the patients with T2DM. First, acute hyperglycemia episodes mostly occur in patients with severe influenza infection [23]. Infection-induced glycemic deviation can easily enhance acute hyperglycemia in T2DM and thus worsen infection control (known as a vicious circle) [24,25]. Glycemic deviation is hypothesized to play a crucial role in endothelial cell dysfunction and lead to vascular dysfunction [26,27,28,29]. Glycemic deviation stimulates endothelial cells to overproduce cytokines and overexpress adhesion molecules, leading to the uncontrolled extravasation of leukocytes [30,31] and resulting in organ damage, including renal function impairment [32]. An animal model study reported that hyperglycemia and glycemic deviation impaired renal function by causing endothelial cell dysfunction in conduit vessels and affecting renal circulation [33]. However, additional basic studies are warranted to elucidate mechanisms underlying these findings.
Influenza vaccination can prevent CKD progression by preventing influenza-induced renal injury. Various mechanisms, including acute tubular necrosis caused by renal hypoperfusion or rhabdomyolysis and glomerular microthrombosis resulting from disseminated intravascular coagulation might be responsible for the development of influenza A virus-induced renal injury [13]. Watanabe et al. reported that the prevalence of AKI was high in the population with influenza infection; they examined 45 hospitalized children with seasonal influenza A virus infection and observed that 24.4% of them had renal involvement, of whom 11% developed AKI. Influenza-induced rhabdomyolysis has been reported to cause AKI in several case reports [16,34], indicating the temporal relationship between fever spikes and the subsequent increase in the serum creatine kinase level. AKI episodes are associated with a higher risk of advanced CKD in diabetes mellitus, independent of other major risk factors for progression; each episode of AKI doubles the risk [35]. Several observational studies have consistently demonstrated that a substantial proportion of patients with AKI often recovered while progressing to the advanced stages of CKD [36,37]. Mechanisms underlying progression to CKD after AKI are extremely complex [38] and include the effects of systemic and intrarenal hypertension and glomerular hyperfiltration, tubular hypertrophy and atrophy, tubulointerstitial fibrosis, progressive glomerular sclerosis, arteriosclerosis, genetic susceptibility, and disordered humoral responses [39,40]. Among several pathologic processes, endothelial injury, part of tubulointerstitial damage, and vascular dropout may cause tissue hypoxia and ischemia, thus affecting renal cellular function and causing progression to CKD [41]. However, additional studies are warranted to elucidate the precise mechanism underlying these findings.
In our study, we observed a higher incidence rate of dialysis in the men than in the women, regardless of vaccination status. The protective effect of estrogens on women and the damaging effect of testosterone, together with an unhealthy lifestyle, might result in a faster decline in kidney function in men than in women, as indicated in a previous study [42].
Subgroup analyses by age, sex, preexisting medical comorbidities, and antidiabetic medications demonstrated a significant protective effect in the vaccination group. More favorable protection against CKD was observed in all the age groups in the patients who received 2–3 and ≥4 vaccinations than in those who received only one vaccination (Table 4). A similar trend in the reduction of dialysis risk was noted (Table 5). This dose effect observed in our diabetic population can be related to their dysfunction of the immune response because hyperglycemia reduced the effectiveness of influenza vaccination [43]. Moreover, our study population consisted of patients aged >55 years, and only 17%–53% of clinical vaccine efficacy was noted in the older group, possibly due to an impaired serological response and antibody generation [44]. However, we could not determine whether the patients received annual vaccinations for more than 4 years or received ≥4 vaccinations over the observation period. Our findings indicated that the protective effect of vaccination appeared to be related to its cumulative dose effect. The patients who received more antidiabetic medications had a lower dialysis risk when receiving only one vaccination; stricter blood sugar control might enhance the protective effect of the vaccine. However, additional studies are warranted to elucidate these precise mechanisms. Future prospective studies stratifying patients with factors such as comorbidities and HbA1c levels are required to validate the protective effect of vaccination.
This study has some limitations that should be addressed. First, the retrospective nature of this study limits the generalizability of the findings. Prospective randomized controlled trials are required to confirm the present results. Second, the diagnoses of T2DM and CKD as well as vaccination and dialysis status were determined according to ICD-9-CM codes, drug codes, and procedure codes in our study. The diagnostic accuracy of the database may be questionable. Third, the NHIRD does not contain factual information on the severity classification of T2DM as indicated by physical activity, HbA1c, alcohol intake, body mass index, substantial proteinuria, and other laboratory data. Moreover, several confounding factors relevant to CKD and ESRD, including substantial proteinuria, body mass index, and other over-the-counter drug use, were not included in our database [45]. Finally, we presumed that all prescribed medications, including antidiabetic medications and co-medications, were consumed by patients to mitigate the effect of noncompliance, which might affect our result.

6. Conclusions

This is the first population-based cohort study to investigate the protective effect of influenza vaccination on CKD incidence and progression to ESRD in patients with T2DM. Our study demonstrated that the protective effect of vaccinations reduced the incidence of CKD and dialysis in patients with T2DM aged ≥55 years. Furthermore, this study showed a dose-dependent effect of vaccination. Our study offers support for the vaccination policy of the Taiwanese government. Large prospective clinical trials should be conducted to elucidate underlying mechanisms based on this study.

Author Contributions

Conceptualization, L.-C.S., S.-H.L. and J.-C.L.; methodology, L.-C.S., S.-H.L., W.J. and Y.-A.F.; soft-ware, Y.-A.F.; validation, W.J., C.-C.C. (Chun-Chao Chen) and C.-C.C. (Chun-Chih Chiu); formal analysis, T.-Y.Y. and M.-H.H.; investigation, C.-H.L. and W.-R.H.; resources, J.-C.L.; data curation, Y.-A.F. and C.-C.C. (Chun-Chih Chiu); writing-original draft preparation, L.-C.S., H.-T.Y. and S.-H.L.; writing-review and editing, C.-C.C. (Chun-Chao Chen) and W.-R.H.; visualization, C.-H.L. and M.-H.L.; supervision, L.-C.S. and J.-C.L.; project administration, J.-C.L.; funding acquisition, J.-C.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, Taipei Medical University-National Taiwan University Hospital Joint Research Program (111-TMU085), and 111FRP-02 from the Taipei Medical University-Shuang Ho Hospital, Ministry of Health and Welfare.

Institutional Review Board Statement

This study was approved by the Joint Institutional Review Board of Taipei Medical University (approval no. TMUJIRB N201804043, on 26 April 2018).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Alicic, R.Z.; Rooney, M.T.; Tuttle, K.R. Diabetic kidney disease: Challenges, progress, and possibilities. Clin. J. Am. Soc. Nephrol. 2017, 12, 2032–2045. [Google Scholar] [CrossRef] [PubMed]
  2. Cameron, J.S. The discovery of diabetic nephropathy: From small print to centre stage. J. Nephrol. 2006, 19 (Suppl. S10), S75–S87. [Google Scholar]
  3. Menke, A.; Casagrande, S.; Geiss, L.; Cowie, C.C. Prevalence of and trends in diabetes among adults in the united states, 1988–2012. JAMA 2015, 314, 1021–1029. [Google Scholar] [CrossRef] [PubMed]
  4. Sheen, Y.J.; Hsu, C.C.; Jiang, Y.D.; Huang, C.N.; Liu, J.S.; Sheu, W.H. Trends in prevalence and incidence of diabetes mellitus from 2005 to 2014 in taiwan. J. Formos. Med. Assoc. 2019, 118 (Suppl. S2), S66–S73. [Google Scholar] [CrossRef]
  5. Thomas, M.C.; Brownlee, M.; Susztak, K.; Sharma, K.; Jandeleit-Dahm, K.A.; Zoungas, S.; Rossing, P.; Groop, P.H.; Cooper, M.E. Diabetic kidney disease. Nat. Rev. Dis. Primers 2015, 1, 15018. [Google Scholar] [CrossRef]
  6. Reutens, A.T. Epidemiology of diabetic kidney disease. Med. Clin. N. Am. 2013, 97, 1–18. [Google Scholar] [CrossRef] [PubMed]
  7. Chang, H.L.; Wu, C.C.; Lee, S.P.; Chen, Y.K.; Su, W.; Su, S.L. A predictive model for progression of ckd. Medicine 2019, 98, e16186. [Google Scholar] [CrossRef]
  8. Lin, Y.C.; Hsu, C.Y.; Kao, C.C.; Chen, T.W.; Chen, H.H.; Hsu, C.C.; Wu, M.S. Incidence and prevalence of esrd in taiwan renal registry data system (twrds): 2005–2012. Acta Nephrol. 2014, 28, 65–68. [Google Scholar]
  9. Vallon, V.; Komers, R. Pathophysiology of the diabetic kidney. Compr. Physiol. 2011, 1, 1175–1232. [Google Scholar]
  10. Reidy, K.; Kang, H.M.; Hostetter, T.; Susztak, K. Molecular mechanisms of diabetic kidney disease. J. Clin. Investig. 2014, 124, 2333–2340. [Google Scholar] [CrossRef] [PubMed]
  11. Usrds: The united states renal data system. Am. J. Kidney. Dis. 2003, 42, 1–230. [CrossRef]
  12. Remschmidt, C.; Wichmann, O.; Harder, T. Vaccines for the prevention of seasonal influenza in patients with diabetes: Systematic review and meta-analysis. BMC Med. 2015, 13, 53. [Google Scholar] [CrossRef] [PubMed]
  13. Watanabe, T. Renal complications of seasonal and pandemic influenza a virus infections. Eur. J. Pediatr. 2013, 172, 15–22. [Google Scholar] [CrossRef] [PubMed]
  14. Dovč, A.; Premru, V.; Pečavar, B.; Ponikvar, R. Acute kidney injury in critically-ill adult patients with seasonal influenza infection. Clin. Nephrol. 2017, 88, 18–21. [Google Scholar] [PubMed]
  15. Martin-Loeches, I.; Papiol, E.; Rodríguez, A.; Diaz, E.; Zaragoza, R.; Granada, R.M.; Socias, L.; Bonastre, J.; Valverdú, M.; Pozo, J.C.; et al. Acute kidney injury in critical ill patients affected by influenza a (h1n1) virus infection. Crit. Care 2011, 15, R66. [Google Scholar] [CrossRef] [PubMed]
  16. Gonakoti, S.; Bahirwani, J.; Maddala, R.N.M.; Vidyasagar, S. A case of h3n2 complicated by acute kidney injury secondary to rhabdomyolysis. BMJ Case Rep. 2018, 2018, bcr2018224334. [Google Scholar] [CrossRef] [PubMed]
  17. Wang, I.K.; Lin, C.L.; Chang, Y.C.; Lin, P.C.; Liang, C.C.; Liu, Y.L.; Chang, C.T.; Yen, T.H.; Huang, C.C.; Sung, F.C. Effectiveness of influenza vaccination in elderly diabetic patients: A retrospective cohort study. Vaccine 2013, 31, 718–724. [Google Scholar] [CrossRef]
  18. Chan, T.C.; Fu, Y.C.; Wang, D.W.; Chuang, J.H. Determinants of receiving the pandemic (h1n1) 2009 vaccine and intention to receive the seasonal influenza vaccine in taiwan. PLoS ONE 2014, 9, e101083. [Google Scholar] [CrossRef] [PubMed]
  19. Liu, J.C.; Wang, T.J.; Sung, L.C.; Kao, P.F.; Yang, T.Y.; Hao, W.R.; Chen, C.C.; Hsu, Y.P.; Wu, S.Y. Influenza vaccination reduces hemorrhagic stroke risk in patients with atrial fibrillation: A population-based cohort study. Int. J. Cardiol. 2017, 232, 315–323. [Google Scholar] [CrossRef] [PubMed]
  20. Chen, K.Y.; Wu, S.M.; Liu, J.C.; Lee, K.Y. Effect of annual influenza vaccination on reducing lung cancer in patients with chronic obstructive pulmonary disease from a population-based cohort study. Medicine 2019, 98, e18035. [Google Scholar] [CrossRef] [PubMed]
  21. Sung, L.C.; Chen, C.I.; Fang, Y.A.; Lai, C.H.; Hsu, Y.P.; Cheng, T.H.; Miser, J.S.; Liu, J.C. Influenza vaccination reduces hospitalization for acute coronary syndrome in elderly patients with chronic obstructive pulmonary disease: A population-based cohort study. Vaccine 2014, 32, 3843–3849. [Google Scholar] [CrossRef]
  22. D’Agostino, R.B., Jr. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat. Med. 1998, 17, 2265–2281. [Google Scholar] [CrossRef]
  23. Jafar, N.; Edriss, H.; Nugent, K. The effect of short-term hyperglycemia on the innate immune system. Am.J. Med. Sci. 2016, 351, 201–211. [Google Scholar] [CrossRef]
  24. Hulme, K.D.; Gallo, L.A.; Short, K.R. Influenza virus and glycemic variability in diabetes: A killer combination? Front. Microbiol. 2017, 8, 861. [Google Scholar] [CrossRef]
  25. Knapp, S. Diabetes and infection: Is there a link?—A mini-review. Gerontology 2013, 59, 99–104. [Google Scholar] [CrossRef]
  26. Azuma, K.; Kawamori, R.; Toyofuku, Y.; Kitahara, Y.; Sato, F.; Shimizu, T.; Miura, K.; Mine, T.; Tanaka, Y.; Mitsumata, M.; et al. Repetitive fluctuations in blood glucose enhance monocyte adhesion to the endothelium of rat thoracic aorta. Arterioscler. Thromb. Vasc. Biol. 2006, 26, 2275–2280. [Google Scholar] [CrossRef]
  27. Ceriello, A.; Esposito, K.; Piconi, L.; Ihnat, M.A.; Thorpe, J.E.; Testa, R.; Boemi, M.; Giugliano, D. Oscillating glucose is more deleterious to endothelial function and oxidative stress than mean glucose in normal and type 2 diabetic patients. Diabetes 2008, 57, 1349–1354. [Google Scholar] [CrossRef]
  28. Quagliaro, L.; Piconi, L.; Assaloni, R.; Martinelli, L.; Motz, E.; Ceriello, A. Intermittent high glucose enhances apoptosis related to oxidative stress in human umbilical vein endothelial cells: The role of protein kinase c and nad(p)h-oxidase activation. Diabetes 2003, 52, 2795–2804. [Google Scholar] [CrossRef] [PubMed]
  29. Risso, A.; Mercuri, F.; Quagliaro, L.; Damante, G.; Ceriello, A. Intermittent high glucose enhances apoptosis in human umbilical vein endothelial cells in culture. Am. J. Physiol. Endocrinol. Metab. 2001, 281, E924–E930. [Google Scholar] [CrossRef]
  30. Perrone, L.A.; Plowden, J.K.; García-Sastre, A.; Katz, J.M.; Tumpey, T.M. H5n1 and 1918 pandemic influenza virus infection results in early and excessive infiltration of macrophages and neutrophils in the lungs of mice. PLoS Pathog. 2008, 4, e1000115. [Google Scholar] [CrossRef]
  31. Short, K.R.; Kroeze, E.; Fouchier, R.A.M.; Kuiken, T. Pathogenesis of influenza-induced acute respiratory distress syndrome. Lancet. Infect. Dis. 2014, 14, 57–69. [Google Scholar] [CrossRef]
  32. Teijaro, J.R.; Walsh, K.B.; Cahalan, S.; Fremgen, D.M.; Roberts, E.; Scott, F.; Martinborough, E.; Peach, R.; Oldstone, M.B.; Rosen, H. Endothelial cells are central orchestrators of cytokine amplification during influenza virus infection. Cell 2011, 146, 980–991. [Google Scholar] [CrossRef]
  33. Gomes, M.B.; Affonso, F.S.; Cailleaux, S.; Almeida, A.L.; Pinto, L.F.; Tibiriçá, E. Glucose levels observed in daily clinical practice induce endothelial dysfunction in the rabbit macro- and microcirculation. Fundam. Clin. Pharmacol. 2004, 18, 339–346. [Google Scholar] [CrossRef] [PubMed]
  34. Runnstrom, M.; Ebied, A.M.; Khoury, A.P.; Reddy, R. Influenza-induced rhabdomyolysis. BMJ Case Rep. 2018, 11, e226610. [Google Scholar] [CrossRef] [PubMed]
  35. Thakar, C.V.; Christianson, A.; Himmelfarb, J.; Leonard, A.C. Acute kidney injury episodes and chronic kidney disease risk in diabetes mellitus. Clin. J. Am. Soc. Nephrol. 2011, 6, 2567–2572. [Google Scholar] [CrossRef]
  36. Devarajan, P.; Jefferies, J.L. Progression of chronic kidney disease after acute kidney injury. Prog. Pediatr. Cardiol. 2016, 41, 33–40. [Google Scholar] [CrossRef]
  37. Forni, L.G.; Darmon, M.; Ostermann, M.; Oudemans-van Straaten, H.M.; Pettilä, V.; Prowle, J.R.; Schetz, M.; Joannidis, M. Renal recovery after acute kidney injury. Intensive Care Med. 2017, 43, 855–866. [Google Scholar] [CrossRef]
  38. Basile, D.P.; Bonventre, J.V.; Mehta, R.; Nangaku, M.; Unwin, R.; Rosner, M.H.; Kellum, J.A.; Ronco, C. Progression after aki: Understanding maladaptive repair processes to predict and identify therapeutic treatments. J. Am. Soc. Nephrol. 2016, 27, 687–697. [Google Scholar] [CrossRef] [PubMed]
  39. Chawla, L.S.; Eggers, P.W.; Star, R.A.; Kimmel, P.L. Acute kidney injury and chronic kidney disease as interconnected syndromes. N. Engl. J. Med. 2014, 371, 58–66. [Google Scholar] [CrossRef] [PubMed]
  40. Venkatachalam, M.A.; Griffin, K.A.; Lan, R.; Geng, H.; Saikumar, P.; Bidani, A.K. Acute kidney injury: A springboard for progression in chronic kidney disease. Am. J. Physiol. Ren. Physiol. 2010, 298, F1078–F1094. [Google Scholar] [CrossRef]
  41. Chawla, L.S.; Kimmel, P.L. Acute kidney injury and chronic kidney disease: An integrated clinical syndrome. Kidney. Int. 2012, 82, 516–524. [Google Scholar] [CrossRef]
  42. Brar, A.; Markell, M. Impact of gender and gender disparities in patients with kidney disease. Curr. Opin. Nephrol. Hypertens. 2019, 28, 178–182. [Google Scholar] [CrossRef] [PubMed]
  43. Berbudi, A.; Rahmadika, N.; Tjahjadi, A.I.; Ruslami, R. Type 2 diabetes and its impact on the immune system. Curr. Diabetes Rev. 2020, 16, 442–449. [Google Scholar] [PubMed]
  44. Derhovanessian, E.; Pawelec, G. Vaccination in the elderly. Microb. Biotechnol. 2012, 5, 226–232. [Google Scholar] [CrossRef]
  45. Tsai, W.C.; Wu, H.Y.; Peng, Y.S.; Ko, M.J.; Wu, M.S.; Hung, K.Y.; Wu, K.D.; Chu, T.S.; Chien, K.L. Risk factors for development and progression of chronic kidney disease: A systematic review and exploratory meta-analysis. Medicine 2016, 95, e3013. [Google Scholar] [CrossRef] [PubMed]
Jcm 11 04520 g001 550
Figure 1. Data Selection Process.
Figure 1. Data Selection Process.
Jcm 11 04520 g001
Table
Table 1. Characteristics of the Sample Population.
Table 1. Characteristics of the Sample Population.
Whole Cohort
(n = 48,017)		Unvaccinated
(n = 24,178)		Vaccinated
(n = 23,839)		p-Value
n%n%n%
Age, years (Mean ± SD)66.64 (8.12)64.47 (8.36)68.85 (7.23)<0.001
55–6423,24548.4115,25963.11798633.50<0.001
65–7416,66934.71565423.3811,01546.21
≥75810316.88326513.50483820.29
Sex
Female 2572253.5712,51651.7713,20655.40<0.001
Male22,29546.4311,66248.2310,63344.60
Comorbidities
Hypertension30,21862.9314,68860.7515,53065.15<0.001
Cerebrovascular diseases863617.99414717.15448918.83<0.001
Dyslipidemia17,03735.48883036.52820734.43<0.001
Heart diseases18,63438.81917237.94946239.69 <0.001
Hepatitis B virus16213.389063.757153.00 <0.001
Hepatitis C virus26185.4511944.9414245.97 <0.001
Cirrhosis29376.1213695.6615686.58 <0.001
Moderate and severe liver disease11182.335642.335542.32 0.949
Asthma682014.20343714.22338314.19 0.939
Antidiabetic medications (ADM)
Insulin and analogs11,91824.82482919.97708929.74 <0.001
Biguanides32,91568.5515,84465.5317,07171.61 <0.001
Sulfonamides and urea derivatives30,43363.3814,03958.0716,39468.77 <0.001
Other blood glucose-lowering drugs796316.58315213.04481120.18 <0.001
Alpha glucosidase inhibitors10,41621.69420617.40621026.05 <0.001
Thiazolidinediones935919.49367315.19568623.85 <0.001
Dipeptidyl peptidase 4672414.00303912.57368515.46 <0.001
Number of ADM
0–115,74632.79905937.47668728.05 <0.001
2–317,37536.19929538.44808033.89
>314,89631.02582424.09907238.06
Co-medications
Statin
<28 days23,71049.3812,66352.3711,04746.34<0.001
28–365 days 10,14321.12537522.23476820.00
>365 days14,16429.50614025.39802433.66
Aspirin
<28 days22,80747.5013,99957.90880836.95<0.001
28–365 days 940619.59444118.37496520.83
>365 days15,80432.91573823.7310,06642.22
Angiotensin-converting enzyme inhibitors and Angiotensin receptor blockers
<28 days14,91631.06949639.28542022.74<0.001
28–365 days 924919.26499420.66425517.85
>365 days23,85249.67968840.0714,16459.42
Level of urbanization
Urban33,96870.7418,21575.3415,75366.08<0.001
Suburban945319.69422417.47522921.93
Rural45969.5717397.19285711.98
Monthly income (NT$)
0545811.3722449.28321413.48<0.001
1–19,20015,27331.81675527.94851835.73
19,200–25,00013,71528.56608225.16763332.02
≥25,00113,57128.26909737.63447418.77
Table
Table 2. Risk of CKD in the Unvaccinated and Vaccinated Patients in the Study Cohort.
Table 2. Risk of CKD in the Unvaccinated and Vaccinated Patients in the Study Cohort.
All Groups
(n = 48,017)		Unvaccinated
(Total Follow-Up of 129,238.4 Person-Years)		Vaccinated
(Total Follow-Up of 206,888.8 Person-Years)		Adjusted HR †
(95% CI)
No. of
Patients
with CKD		Incidence Rate
(per 105 Person-Years)
(95% CI)		No. of
Patients
with CKD		Incidence Rate
(per 105 Person-Years)
(95% CI)
Whole cohort
Influenza season14501122.0 (1064.2, 1179.7)1319637.5 (603.1, 671.9)0.47 (0.44, 0.51) ***
Noninfluenza season13081012.1 (957.2, 1066.9)1247602.7 (569.3, 636.2)0.48 (0.44, 0.52) ***
All seasons27582134.0 (2054.4, 2213.7)25661240.3 (1192.3, 1288.3)0.48 (0.45, 0.50) ***
Age, <65 years a
Influenza season743846.5 (785.6, 907.4)350446.1 (399.3, 492.8)0.45 (0.39, 0.51) ***
Noninfluenza season667759.9 (702.2, 817.6)322410.4 (365.6, 455.2)0.46 (0.40, 0.52) ***
All seasons14101606.4 (1522.6, 1690.3)672856.4 (791.7, 921.2)0.45 (0.41, 0.50) ***
Age, ≥65 years b
Influenza season7071705.0 (1579.4, 1830.7)969754.5 (707.0, 802.0)0.44 (0.40, 0.48) ***
Noninfluenza season6411545.9 (1426.2, 1665.6)925720.3 (673.9, 766.7)0.45 (0.40, 0.50) ***
All seasons13483250.9 (3077.4, 3424.5)18941474.8 (1408.4, 1541.2)0.44 (0.41, 0.47) ***
Female c
Influenza season7041008.9 (934.3, 1083.4)636538.9 (497.0, 580.8)0.44 (0.40, 0.50) ***
Noninfluenza season635910.0 (839.2, 980.8)633536.3 (494.6, 578.1)0.45 (0.40, 0.51) ***
All seasons13391918.9 (1816.1, 2021.6)12691075.2 (1016.1, 1134.4)0.45 (0.41, 0.49) ***
Male d
Influenza season7461254.7 (1164.6, 1344.7)683768.6 (710.9, 826.2)0.50 (0.44, 0.55) ***
Noninfluenza season6731131.9 (1046.4, 1217.4)614690.9 (636.3, 745.6)0.51 (0.45, 0.57) ***
All seasons14192386.6 (2262.4, 2510.8)12971459.5 (1380.0, 1538.9)0.50 (0.46, 0.54) ***
***: p < 0.001; a Total follow-up of 87,773.3 person-years for the unvaccinated group and 78,464.8 person-years for the vaccinated group. b Total follow-up of 41,465.1 person-years for the unvaccinated group and 128,424.0 person-years for the vaccinated group. c Total follow-up of 69,781.0 person-years for the unvaccinated group and 118,020.6 person-years for the vaccinated group. d Total follow-up of 59,457.3 person-years for unvaccinated group and 88,868.2 person-years for the vaccinated group. CI: confidence interval. HR: hazard ratio. † Main model was adjusted for propensity scores for age; sex; hypertension; dyslipidemia; cerebrovascular diseases; heart diseases; hepatitis B virus; hepatitis C virus; cirrhosis; moderate and severe liver disease; asthma; use of insulin and analogs, biguanides, sulfonamides, urea derivatives, alpha glucosidase inhibitors, thiazolidinediones, dipeptidyl peptidase 4, other blood glucose-lowering drugs, antidiabetic medications, statins, aspirin, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers; urbanization level; and monthly income.
Table
Table 3. Risk of Dialysis in the Unvaccinated and Vaccinated Patients in the Study Cohort.
Table 3. Risk of Dialysis in the Unvaccinated and Vaccinated Patients in the Study Cohort.
All Groups
(n = 48,017)		Unvaccinated
(Total Follow-Up of 131,187.3 Person-Years)		Vaccinated
(Total Follow-Up of 219,009.2 Person-Years)		Adjusted HR †
(95% CI)
No. of
Patients
with Dialysis		Incidence Rate
(per 105 Person-Years)
(95% CI)		No. of
Patients
with Dialysis		Incidence Rate
(per 105 Person-Years)
(95% CI)
Whole cohort
Influenza season564429.9 (394.4, 465.4) 615280.8 (258.6, 303.0) 0.47 (0.42, 0.53) ***
Noninfluenza season455346.8 (315.0, 378.7) 520237.4 (217.0, 257.8) 0.49 (0.43, 0.56) ***
All seasons1019776.8 (729.1, 824.4) 1135518.2 (488.1, 548.4) 0.48 (0.44, 0.52) ***
Age, <65 years a
Influenza season327368.0 (328.1, 407.9) 151182.9 (153.7, 212.0) 0.38 (0.31, 0.46) ***
Noninfluenza season239269.0 (234.9, 303.1) 154186.5 (157.0, 216.0) 0.52 (0.42, 0.64) ***
All seasons566637.0 (584.5, 689.4) 305369.4 (327.9, 410.8) 0.44 (0.38, 0.50) ***
Age, ≥65 years b
Influenza season237559.9 (488.6, 631.2) 464340.1 (309.1, 371.0) 0.51 (0.43, 0.59) ***
Noninfluenza season216510.3 (442.3, 578.4) 366268.3 (240.8, 295.7) 0.44 (0.37, 0.52) ***
All seasons4531070.2 (971.7, 1168.8) 830608.3 (567.0, 649.7) 0.47 (0.42, 0.53) ***
Female c
Influenza season297420.6 (372.7, 468.4) 325263.1 (234.5, 291.7) 0.44 (0.38, 0.52) ***
Noninfluenza season238337.0 (294.2, 379.8) 281227.5 (200.9, 254.1) 0.46 (0.39, 0.56) ***
All seasons535757.6 (693.4, 821.8) 606490.6 (451.6, 529.7) 0.45 (0.40, 0.51)***
Male d
Influenza season267440.8 (388.0, 493.7) 290303.7 (268.7, 338.6) 0.50 (0.42, 0.60) ***
Noninfluenza season217358.3 (310.6, 406.0) 239250.3 (218.6, 282.0) 0.52 (0.42, 0.63) ***
All seasons484799.1 (727.9, 870.3) 529554.0 (506.8, 601.2) 0.51 (0.45, 0.58) ***
***: p < 0.001; a Total follow-up of 88,860.4 person-years for the unvaccinated group and 82,572.5 person-years for the vaccinated group. b Total follow-up of 42,326.9 person-years for the unvaccinated group and 136,436.7 person-years for the vaccinated group. c Total follow-up of 70,621.2 person-years for the unvaccinated group and 123,518.2 person-years for the vaccinated group. d Total follow-up of 60,566.1 person-years for the unvaccinated group and 95,491.1 person-years for the vaccinated group. CI: confidence interval. HR: hazard ratio. † Main model was adjusted for propensity scores for age; sex; hypertension; dyslipidemia; cerebrovascular diseases; heart diseases; hepatitis B virus; hepatitis C virus; cirrhosis; moderate and severe liver disease; asthma; use of insulin and analogs, biguanides, sulfonamides, urea derivatives, alpha glucosidase inhibitors, thiazolidinediones, dipeptidyl peptidase 4, other blood glucose-lowering drugs, antidiabetic medications, statins, aspirin, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers; urbanization level; and monthly income.
Table
Table 4. Sensitivity Analysis of the Adjusted HRs for Vaccination in the Risk Reduction of CKD in All Seasons.
Table 4. Sensitivity Analysis of the Adjusted HRs for Vaccination in the Risk Reduction of CKD in All Seasons.
UnvaccinatedVaccinatedp-Value for Trend
12–3≥4
Adjusted HR
(95% CI)		Adjusted HR
(95% CI)		Adjusted HR
(95% CI)		Adjusted HR
(95% CI)
Main model †1.000.71 (0.65, 0.77) ***0.57 (0.52, 0.61) ***0.30(0.28, 0.33) ***<0.001
Subgroup effects
Age, years
<651.000.58 (0.51, 0.67) ***0.51 (0.45,0.59) ***0.31 (0.27, 0.36) ***<0.001
≥651.000.71 (0.64, 0.78) ***0.52 (0.47,0.57) *** 0.25 (0.23, 0.28) ***<0.001
Sex
Female1.000.72 (0.64, 0.80) ***0.54 (0.48, 0.60) ***0.28 (0.25, 0.31) ***<0.001
Male1.000.70 (0.63, 0.79) ***0.60 (0.54, 0.67) ***0.33 (0.29, 0.37) ***<0.001
Hypertension
No1.000.64 (0.55, 0.74) ***0.55 (0.48, 0.63) ***0.32 (0.28, 0.36) ***<0.001
Yes1.000.74 (0.67, 0.81) ***0.57 (0.52, 0.63) ***0.29 (0.27, 0.32) ***<0.001
Cerebrovascular diseases
No1.000.72 (0.66, 0.78) ***0.58 (0.53, 0.63) ***0.32 (0.29, 0.35) ***<0.001
Yes1.000.67 (0.56, 0.80) ***0.51 (0.43, 0.61) ***0.23 (0.19, 0.29) ***<0.001
Dyslipidemia
No1.000.71 (0.64, 0.78) ***0.57 (0.52, 0.63) ***0.31 (0.28, 0.35) ***<0.001
Yes1.000.71 (0.62, 0.82) ***0.55 (0.48, 0.63) ***0.28 (0.24, 0.32) ***<0.001
Heart diseases
No1.000.68 (0.61, 0.75) ***0.57 (0.52, 0.63) ***0.32 (0.29, 0.35) ***<0.001
Yes1.000.75 (0.66, 0.85) ***0.55 (0.48, 0.62) ***0.28 (0.24, 0.32) ***<0.001
Asthma
No1.000.70 (0.64, 0.76) ***0.57 (0.53, 0.62) ***0.31 (0.28, 0.34) ***<0.001
Yes1.000.78 (0.62, 0.97) *0.51 (0.41, 0.64) ***0.27 (0.21, 0.34) ***<0.001
Insulin and analogs
No (<28 days)1.000.72 (0.65, 0.79) ***0.56 (0.51, 0.61) ***0.29 (0.26, 0.32) ***<0.001
Yes (≥28 days)1.000.71 (0.61, 0.81) ***0.61 (0.53, 0.69) ***0.35 (0.30, 0.40) ***<0.001
Biguanides
No (<28 days)1.000.61 (0.53, 0.70) ***0.46 (0.40, 0.53) ***0.22 (0.19, 0.26) ***<0.001
Yes (≥28 days)1.000.77 (0.70, 0.85) ***0.63 (0.57, 0.69) ***0.35 (0.32, 0.38) ***<0.001
Sulfonamides, urea derivatives
No (< 28 days)1.000.61 (0.52, 0.71) ***0.40 (0.34, 0.46) ***0.23 (0.19, 0.27) ***<0.001
Yes (≥28 days)1.000.76 (0.70, 0.84) ***0.65 (0.60, 0.71) ***0.34 (0.31, 0.38) ***<0.001
Alpha glucosidase inhibitors
No (<28 days)1.000.68 (0.62, 0.74) ***0.53 (0.48, 0.58) ***0.27 (0.25, 0.30) ***<0.001
Yes (≥28 days)1.000.85 (0.72, 0.99) *0.72 (0.62, 0.83) ***0.42 (0.36, 0.48) ***<0.001
Thiazolidinediones
No (<28 days)1.000.69 (0.63, 0.76) ***0.53 (0.48, 0.58) ***0.27 (0.25, 0.30) ***<0.001
Yes (≥28 days)1.000.79 (0.67, 0.93) **0.72 (0.62, 0.84) ***0.42 (0.37, 0.49) ***<0.001
Dipeptidyl peptidase 4 inhibitor
No (<28 days)1.000.68 (0.63, 0.74) ***0.54 (0.50, 0.59) ***0.28 (0.26, 0.30) ***<0.001
Yes (≥28 days)1.000.97 (0.70, 1.34)0.83 (0.62, 1.12)0.69 (0.54, 0.90)**0.004
Other blood glucose lowering drugs
No (<28 days)1.000.71 (0.65, 0.78) ***0.57 (0.52, 0.62) ***0.28 (0.26, 0.31) ***<0.001
Yes (≥28 days)1.000.72 (0.60, 0.85) ***0.58 (0.50, 0.68) ***0.38 (0.32, 0.44) ***<0.001
Number of Antidiabetes medications
0–11.000.62 (0.53, 0.72) ***0.42 (0.36, 0.49) ***0.23 (0.19, 0.27) ***<0.001
2–31.000.74 (0.65, 0.85) ***0.62 (0.55, 0.71) ***0.29 (0.25, 0.33) ***<0.001
>31.000.79 (0.70, 0.90) ***0.67 (0.59, 0.75) ***0.40 (0.35, 0.45) ***<0.001
*: p < 0.05; **: p < 0.01; ***: p < 0.001; † Main model was adjusted for propensity scores for age; sex; hypertension; dyslipidemia; cerebrovascular diseases; heart diseases; hepatitis B virus; hepatitis C virus; cirrhosis; moderate and severe liver disease; asthma; use of insulin and analogs, biguanides, sulfonamides, urea derivatives, alpha glucosidase inhibitors, thiazolidinediones, dipeptidyl peptidase 4, other blood glucose–lowering drugs, antidiabetic medications, statins, aspirin, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers; urbanization level; and monthly income.
Table
Table 5. Sensitivity Analysis of Adjusted HRs for Vaccination in the Risk Reduction of Dialysis in All Seasons.
Table 5. Sensitivity Analysis of Adjusted HRs for Vaccination in the Risk Reduction of Dialysis in All Seasons.
UnvaccinatedVaccinatedp-Value for Trend
12–3≥4
Adjusted HR
(95% CI)		Adjusted HR
(95% CI)		Adjusted HR
(95% CI)		Adjusted HR
(95% CI)
Main model †1.000.77 (0.68, 0.87) ***0.63 (0.56, 0.70) ***0.28 (0.24, 0.31) ***<0.001
Subgroup effects
Age, years
<651.000.63 (0.52, 0.77) ***0.47 (0.38, 0.58) ***0.29 (0.23, 0.36) ***<0.001
≥651.000.82 (0.70, 0.97) *0.66 (0.58, 0.77) *** 0.25 (0.21, 0.29) ***<0.001
Sex
Female1.000.73 (0.62, 0.87) ***0.59 (0.51, 0.69) ***0.28 (0.23, 0.33) ***<0.001
Male1.000.82 (0.68, 0.98) *0.68 (0.57, 0.80) ***0.28 (0.23, 0.33) ***<0.001
Hypertension
No1.000.64 (0.51, 0.81) ***0.63 (0.51, 0.76) ***0.27 (0.21, 0.34) ***<0.001
Yes1.000.82 (0.71, 0.95) **0.62 (0.54, 0.71) ***0.28 (0.24, 0.32) ***<0.001
Cerebrovascular diseases
No1.000.75 (0.66, 0.87) ***0.65 (0.57, 0.73) ***0.29 (0.25, 0.33) ***<0.001
Yes1.000.80 (0.61, 1.05)0.54 (0.41, 0.70) ***0.22 (0.16, 0.30) ***<0.001
Dyslipidemia
No1.000.72 (0.62, 0.83) ***0.63 (0.55, 0.72) ***0.28 (0.24, 0.32) ***<0.001
Yes1.000.90 (0.72, 1.11)0.63 (0.51, 0.78) ***0.28 (0.23, 0.36) ***<0.001
Heart diseases
No1.000.74 (0.63, 0.86) ***0.62 (0.53, 0.71) ***0.28 (0.24, 0.33) ***<0.001
Yes1.000.82 (0.67, 1.01)0.64 (0.53, 0.78) ***0.27 (0.22, 0.33) ***<0.001
Asthma
No1.000.77 (0.67, 0.87) ***0.63 (0.56, 0.71) ***0.27 (0.24, 0.31) ***<0.001
Yes1.000.82 (0.55, 1.23)0.68 (0.47, 0.98) *0.32 (0.21, 0.48) ***<0.001
Insulin and analogs
No (<28 days)1.000.86 (0.72, 1.03)0.72 (0.61, 0.85) ***0.26 (0.22, 0.32) ***<0.001
Yes (≥28 days)1.000.69 (0.58, 0.82) ***0.57 (0.48, 0.66) ***0.32 (0.27, 0.37) ***<0.001
Biguanides
No (<28 days)1.000.79 (0.62, 1.01)0.58 (0.46, 0.74) ***0.23 (0.17, 0.30) ***<0.001
Yes (≥28 days)1.000.77 (0.67, 0.89) ***0.66 (0.57, 0.75) ***0.30 (0.26, 0.35) ***<0.001
Sulfonamides, urea derivatives
No (<28 days)1.000.84 (0.63, 1.11)0.62 (0.47, 0.82) ***0.26 (0.19, 0.36) ***<0.001
Yes (≥28 days)1.000.77 (0.67, 0.88) ***0.65 (0.57, 0.73) ***0.29 (0.26, 0.33) ***<0.001
Alpha glucosidase inhibitors
No (<28 days)1.000.77 (0.66, 0.89) ***0.64 (0.56, 0.73) ***0.28 (0.24, 0.33) ***<0.001
Yes (≥28 days)1.000.78 (0.63, 0.98) *0.61 (0.49, 0.75) ***0.27 (0.22, 0.34) ***<0.001
Thiazolidinediones
No (<28 days)1.000.69 (0.63, 0.76) ***0.53 (0.48, 0.58) ***0.27 (0.25, 0.30) ***<0.001
Yes (≥28 days)1.000.79 (0.67, 0.93) **0.72 (0.62, 0.84) ***0.42 (0.37, 0.49) ***<0.001
Dipeptidyl peptidase 4 inhibitor
No (<28 days)1.000.73 (0.64, 0.82) ***0.61 (0.54, 0.68) ***0.26 (0.23, 0.30) ***<0.001
Yes (≥28 days)1.001.29 (0.77, 2.17)0.73 (0.43, 1.25)0.54 (0.33, 0.87) *0.004
Other blood glucose lowering drugs
No (<28 days)1.000.76 (0.65, 0.88) ***0.68 (0.59, 0.77) ***0.28 (0.24, 0.32) ***<0.001
Yes (≥28 days)1.000.79 (0.64, 0.98) *0.54 (0.44, 0.67) ***0.29 (0.24, 0.36) ***<0.001
Number of Antidiabetes medications
0–11.000.86 (0.64, 1.16)0.66 (0.50, 0.88) **0.26 (0.19, 0.36) ***<0.001
2–31.000.76 (0.62, 0.94) *0.69 (0.57, 0.83) ***0.29 (0.24, 0.36) ***<0.001
>31.000.78 (0.65, 0.92) **0.61 (0.52, 0.72) ***0.30 (0.25, 0.36) ***<0.001
*: p < 0.05; **: p < 0.01; ***: p < 0.001; † Main model was adjusted for propensity scores for age; sex; hypertension; dyslipidemia; cerebrovascular diseases; heart diseases; hepatitis B virus; hepatitis C virus; cirrhosis; moderate and severe liver disease; asthma; use of insulin and analogs, biguanides, sulfonamides, urea derivatives, alpha glucosidase inhibitors, thiazolidinediones, dipeptidyl peptidase 4, other blood glucose-lowering drugs, antidiabetic medications, statins, aspirin, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers; urbanization level; and monthly income.
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MDPI and ACS Style

Sung, L.-C.; Chen, C.-C.; Liu, S.-H.; Chiu, C.-C.; Yang, T.-Y.; Lin, C.-H.; Fang, Y.-A.; Jian, W.; Lei, M.-H.; Yeh, H.-T.; et al. Effect of Influenza Vaccination on the Reduction of the Incidence of Chronic Kidney Disease and Dialysis in Patients with Type 2 Diabetes Mellitus. J. Clin. Med. 2022, 11, 4520. https://doi.org/10.3390/jcm11154520

AMA Style

Sung L-C, Chen C-C, Liu S-H, Chiu C-C, Yang T-Y, Lin C-H, Fang Y-A, Jian W, Lei M-H, Yeh H-T, et al. Effect of Influenza Vaccination on the Reduction of the Incidence of Chronic Kidney Disease and Dialysis in Patients with Type 2 Diabetes Mellitus. Journal of Clinical Medicine. 2022; 11(15):4520. https://doi.org/10.3390/jcm11154520

Chicago/Turabian Style

Sung, Li-Chin, Chun-Chao Chen, Shih-Hao Liu, Chun-Chih Chiu, Tsung-Yeh Yang, Cheng-Hsin Lin, Yu-Ann Fang, William Jian, Meng-Huan Lei, Hsien-Tang Yeh, and et al. 2022. "Effect of Influenza Vaccination on the Reduction of the Incidence of Chronic Kidney Disease and Dialysis in Patients with Type 2 Diabetes Mellitus" Journal of Clinical Medicine 11, no. 15: 4520. https://doi.org/10.3390/jcm11154520

APA Style

Sung, L.-C., Chen, C.-C., Liu, S.-H., Chiu, C.-C., Yang, T.-Y., Lin, C.-H., Fang, Y.-A., Jian, W., Lei, M.-H., Yeh, H.-T., Hsu, M.-H., Hao, W.-R., & Liu, J.-C. (2022). Effect of Influenza Vaccination on the Reduction of the Incidence of Chronic Kidney Disease and Dialysis in Patients with Type 2 Diabetes Mellitus. Journal of Clinical Medicine, 11(15), 4520. https://doi.org/10.3390/jcm11154520

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