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. 2024 Dec 27;16(3):442–450. doi: 10.1111/jdi.14385

Smoking and diabetic nephropathy: An updated systematic review and meta‐analysis

Haihui Zhu 1,†, Liang Li 2,†, Songchun Liu 3, Jing Li 4,✉
PMCID: PMC11871389  PMID: 39728025

ABSTRACT

Objectives

Several studies have reported the potential association between smoking and diabetic nephropathy. However, the studies of non‐significant association results were against the association between smoking and diabetic nephropathy. Therefore, the relationship between smoking and diabetic nephropathy was still debated and controversial.

Methods

Prospective cohort studies were included in the current meta‐analysis. The tobacco smoking (current smokers or former smokers) and non‐smoking groups in the enrolled studies were compared for the hazard ratio (HR) of diabetic nephropathy. Fifteen studies with 221,821 subjects were included in this meta‐analysis. Subgroup analysis of the type 1 diabetes and type 2 diabetes groups was also performed individually to investigate the effects of different types of diabetes on the relationship between smoking and diabetic nephropathy.

Results

Current smoking was significantly associated with a greater log HR of diabetic nephropathy [1.44 (1.22–1.70), Z = 4.39]. In addition, former smoking was significantly associated with diabetic nephropathy [log HR = 1.04 (1.03–1.05), Z = 8.02]. The individual subgroup analysis of type 1 diabetes and type 2 diabetes subjects showed that smoking might be both significantly associated with greater log HRs of diabetic nephropathy.

Discussion

Current and former smoking might be the risk factors for diabetic nephropathy in the current meta‐analytic results. The phenomenon of such significant associations were discovered in type 1 and 2 diabetes.

Keywords: Diabetic nephropathy, Hazard ratio, Smoking

Current and former smoking might be the risk factors for diabetic nephropathy in the current meta‐analytic results. The phenomenon of such significant associations was discovered in type 1 and 2 diabetes.

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INTRODUCTION

Diabetes mellitus (DM) is a serious issue of worldwide health care and causes a significant economic and care burden due to its complex and chronic characteristics 1 . It has a lot of sequelae, including diabetic nephropathy, which belongs to the microvascular complications of DM and is usually the origin of chronic kidney disease 2 . The triad of persistent albuminuria, higher arterial blood pressure, and a decline in the glomerular filtration rate are the crucial symptom components of diabetic nephropathy, which is irreversible and prone to be associated with the increase of death due to cardiovascular complications 3 . The risk factors of diabetic nephropathy were numerous, including poor glycemic control, long duration of DM, dyslipidemia, genetic predisposition, hypertension, depression, aging, male, high body mass index, decreases in vitamin D and hemoglobin, etc 2 , 4 , 5 , 6 . Radcliffe et al. ever mentioned the possible association between smoking status and diabetic nephropathy 5 . The first meta‐analysis of this issue suggested that smoking may be the independent risk factor for diabetic nephropathy. However, the first meta‐analysis included different kinds of observational studies (one case–control, eight cross‐sectional, and 10 prospective cohort studies), which might be limited in the interpretations due to the significant clinical heterogeneity 7 . Another meta‐analysis of the prospective cohort replicated the findings of the first meta‐analysis and emphasized the independent risk factor characteristics in type 1 DM 8 . Since the literature of systematic review and meta‐analysis in this field were still limited, the authors designed the current meta‐analysis study to update the included studies of prospective cohort for the relationship between smoking and diabetic nephropathy. Based on the previous meta‐analysis results, the authors hypothesized that smoking might be the significant risk factor of diabetic nephropathy. We would focus on the prospective cohort to clarify the relationship between smoking and diabetic nephropathy.

MATERIALS AND METHODS

Selection keywords and strategy

The population was set as the prospective cohort with the recording of smoking and diabetic nephropathy. The exposure was set as the cohort with smoking vs the cohort without smoking. The comparison was made between the cohort with smoking and the cohort without smoking for the risk of diabetic nephropathy. The outcome would focus on the hazard ratio (HR) of diabetic nephropathy when the cohort with smoking was compared to the cohort without smoking. The selection keywords were “smoking” or “tobacco” or “cigarette” or “nicotine” or “diabetes” or “diabetic” or “nephropathy” or “proteinuria” or “albuminuria” or “microalbuminuria” or “prospective” or “cohort”, “hazard” or “ratio” or “events” or “clinical” or “versus” or “outcome” or “comparison” or “current” or “former.” We searched for related articles in ScienceDirect, PubMed, Google Scholar, Web of Science, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), and Scopus databases. Our search filter was limited to prospective cohort studies. No search strategies from other literature reviews were adapted or reused for a substantive part or all of the search in the current meta‐analysis. The literature search was limited to articles published (including e‐published) before April 2024.

The following eligibility criteria were used in this meta‐analysis: (1) Comparisons between smoking and non‐smoking for the risk of diabetic nephropathy. (2) Studies using smoking, non‐smoking, and diabetic nephropathy data. (3) Studies with data on diabetic nephropathy events, including proteinuria, albuminuria, and micro‐albuminuria. (4) Smoking vs non‐smoking for diabetic nephropathy events. (5) Prospective cohort studies. The following exclusion criteria were used to exclude unwanted literature: (1) No outcome data in the content of the articles. (2) The authors could not access the dataset and did not respond to our requests regarding the data of published articles. (3) Studies without related data on smoking, HR, and diabetic nephropathy. (4) Review Articles.

The evaluative extraction of data

The current systematic review and meta‐analysis were performed using the Cochrane Handbook for Systematic Reviews and Interventions. We report the results using the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines 9 . Several data perspectives were extracted from the selected literature. First, the rate of diabetic nephropathy events in smoking and non‐smoking subjects. Second, the HR of diabetic nephropathy events. Third, the data of smoking, non‐smoking, and comparison between the smoking group and non‐smoking group in the patients with type 1 DM for diabetic nephropathy events. Fourth, the data of smoking, non‐smoking, and comparison between the smoking group and non‐smoking group in the patients with type 2 DM for diabetic nephropathy events.

The collection and evaluation of extracted data

ZH and LL reviewed and screened abstracts and articles. ZH and LL independently extracted clinical outcome data from the content of the eligible articles. The eligible studies had clinical outcome data in the content or Supplementary Materials. Finally, ZH and LL performed a collaborative review to resolve discrepancies. The risk of bias assessment of the included studies by the ROBINS‐E (Risk Of Bias In Non‐randomized Studies – of Exposures; https://www.riskofbias.info/welcome/robins‐e‐tool) was performed by ZH and LL. ROBINS‐E included the seven risk of bias domains, such as due to confounding, arising from measurement of the exposure, in selection of participants into the study, due to post‐exposure interventions, due to missing data, arising from measurement of the outcome, and in selection of the reported result. ROBINS‐E is a useful and validated tool to evaluate the risk of bias of exposure (such as the smoking) in the systematic review. The risk of bias assessment was reported and visualized according to the above seven bias dimensions. The results were evaluated and confirmed by all authors.

The statistical analysis of the current meta‐analysis

For diabetic nephropathy events, we generated pooled estimates of HR along with the associated 95% confidence interval. Owing to the lack of patient‐level data, we used summary statistics for each trial by extracting the reported HRs. In studies without HRs and respective 95% confidence intervals, the Kaplan–Meier survival curves were calculated for each individual study, and used the Guyton algorithm to reconstruct individual patient data to subsequently obtain an estimate of HRs and 95% confidence interval in survival analysis. The data were transformed to the log‐HRs using the HR and the start of 95% confidence intervals in the Rev Man calculation function. The risk estimates of individual studies were combined using the inverse variance weighted averages of log HRs in the random effects model. In addition, random and fixed effects models were used with an inverse variance function‐weighted log HR. Smoking and non‐smoking were compared to each other to determine whether smoking would be associated with a higher log HR of diabetic nephropathy events. Chi‐square tests were used to assess heterogeneity between the enrolled studies. The derived I 2 statistics were used to estimate the statistical heterogeneity of the studies included in the meta‐analysis 10 . To investigate the clinical heterogeneity, we planned to survey the DM subtypes of smoking groups and diabetic nephropathy. Therefore, we performed a subgroup analysis based on the type 1 DM and type 2 DM subgroups. The comparison between the smoking group and non‐smoking group in the patients with type 1 DM was applied to explore the effect of smoking on diabetic nephropathy events in the subgroup of type 1 DM. In addition, a comparison between the smoking group and non‐smoking group in the patients with type 2 DM was applied to explore the effect of smoking on diabetic nephropathy events in the subgroup of type 2 DM.

RESULTS

Description of studies and risk of bias assessment

The PRISMA flow chart revealed the selection process of included studies (Figure 1). A qualitative analysis of the remaining 15 articles was performed, and the remaining 15 studies were included in this meta‐analysis 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 . A PRISMA flow diagram of the current meta‐analysis is shown in Figure 1. The risk of bias assessment was presented as Figure 2.

Figure 1.

Figure 1

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The selection process of enrolled studies. The PRISMA flowchart of article collection and selection process for the current meta‐analysis.

Figure 2.

Figure 2

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The risk of bias assessment by ROBINS‐E. The ROBINS‐E assessment summary plot of the included studies in the current meta‐analysis.

Log HR of diabetic nephropathy events: Current smoking vs non‐smoking

It showed a greater log HR of diabetic nephropathy for the current smoking group vs the non‐smoking group under random effects model [1.44 (95% CI = 1.22–1.70)]. A high statistical heterogeneity was noted (I 2 = 76%). The test for the overall effect was Z = 4.39 (P < 0.0001; Figure 3). The results remained significant even after excluding the potentially high‐risk study 21 (Z = 4.41, P < 0.0001).

Figure 3.

Figure 3

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A significantly greater log HR for current smoking vs non‐smoking in the diabetic nephropathy events. A significantly greater log HR of diabetic nephropathy events when the current smoking group was compared to non‐smoking group.

Log HR of diabetic nephropathy events: Former smoking vs non‐smoking

It showed a mildly greater log HR of diabetic nephropathy for the former smoking group vs non‐smoking group under random effects model [1.04 (95% CI = 1.03–1.05)]. A low statistical heterogeneity was noted (I 2 = 0%). The test for the overall effect was Z = 8.02 (P < 0.00001; Figure 4).

Figure 4.

Figure 4

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A significantly greater log HR for former smoking vs non‐smoking in the diabetic nephropathy events. A significantly greater log HR of diabetic nephropathy events when the former smoking group was compared to non‐smoking group.

Subgroup analysis: Log HR of diabetic nephropathy events in smoking groups of type 1 DM patients

It showed a greater log HR of diabetic nephropathy events for the smoking group vs non‐smoking group under the random effects model in the patients with type 1 DM subgroup [1.40 (95% CI = 1.14–1.72)]. A substantial statistical heterogeneity was noted (I 2 = 82%). The test for the overall effect was Z = 3.17 (P = 0.001; Figure 5).

Figure 5.

Figure 5

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Type 1 DM subgroup analysis results for smoking vs non‐smoking in the diabetic nephropathy events. Type 1 DM subgroup analysis showed a greater log HR of diabetic nephropathy events for smoking patients when compared to non‐smoking patients.

Subgroup analysis: Log HR of diabetic nephropathy events in smoking groups of type 2 DM patients

It showed a greater log HR of diabetic nephropathy events for the smoking group vs non‐smoking group under the random effects model in the patients with type 2 DM subgroup [1.31 (95% CI = 1.07–1.61)]. A moderate statistical heterogeneity was noted (I 2 = 32%). The test for the overall effect was Z = 2.65 (P = 0.008; Figure 6).

Figure 6.

Figure 6

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Type 2 DM subgroup analysis results for smoking vs non‐smoking in the diabetic nephropathy events. Type 2 DM subgroup analysis showed a greater log HR of diabetic nephropathy events for smoking patients when compared to the non‐smoking patients.

DISCUSSION

The authors demonstrated that current smoking might be the most significant risk factor for the diabetic nephropathy in patients with diabetes. Therefore, the clinicians should pay more attention to the current smokers who are comorbid with diabetes. For the health promotion and prevention of nephropathy, the patients with diabetes and current smoking habits should be the first priority of clinicians to monitor the renal function to prevent the occurrence of diabetic nephropathy. Apart from the current smokers, the former smokers who are comorbid with diabetes might also have the risk of diabetic nephropathy. However, according to the values of log OR, the risk of the former smoking group might be lower than that of the current smoking group. From the meta‐analysis results, even quitting smoking, the former smokers who are comorbid with diabetes also need the clinicians to monitor the renal function. Therefore, clinicians should pay more attention to the history taking of smoking in patients with diabetes. The smoking behaviors seemed to be significantly associated with diabetic nephropathy, either current smokers or former smokers who are comorbid with diabetes. The subgroup analysis of type 1 and 2 DM also suggested the smoking behaviors would be significantly associated with diabetic nephropathy. The log OR value of type 1 DM was higher than that of type 2 DM, which might suggest that the smoking patients with type 1 DM may be easier to proceed to diabetic nephropathy. Since type I DM is early‐onset, the higher risk of diabetic nephropathy in smoking patients is predictable. Therefore, the clinicians should also pay more attention to smoking patients with type 1 DM, who are easier to proceed to the stage of diabetic nephropathy. Our meta‐analysis results were different from the previous meta‐analysis 8 in the following perspectives: (1) Our meta‐analysis included more updated studies and more sample size. (2) Our study supported that current smoking also increased the risk of diabetic nephropathy, which was not demonstrated in the previous meta‐analysis 8 . (3) Our study also supported that smoking type I DM patients would have a higher risk of diabetic nephropathy, which was not mentioned in the previous meta‐analysis 8 . The substantial heterogeneity might originate from the clinical, methodological, and statistical heterogeneity. In the included studies of our meta‐analysis, the variability in the patients and focused outcomes studied with smoking in DM patients and the comparison group might contribute to clinical heterogeneity. In addition, the variability in study design and risk of bias might contribute to methodological heterogeneity. Clinical and methodological heterogeneity might contribute to the consequence of statistical heterogeneity in the current meta‐analysis. Therefore, based on the summarized findings of diverse included studies in the current meta‐analysis, we might suggest that the smoking DM patients might have a higher risk of diabetic nephropathy. Future meta‐analyses can be aimed at reducing the heterogeneity due to the clinical heterogeneity. A more sophisticated and homogeneous study sample and comparison groups should be included in the future meta‐analysis to confirm the risk of diabetic nephropathy in the DM patients with smoking. In summary, according to the results of our meta‐analysis, current smoking status and type 1 DM should be the first priority of monitor for diabetic nephropathy. The former smoking status and type 2 DM might be the second priority. However, more prospective cohort studies with less heterogeneity will be needed in the future to confirm the findings.

Smoking is well known as a risk factor of cancer, such as lung cancer. In recent years, the role of smoking in the formation of chronic kidney disease has been investigated. A previous meta‐analysis suggested that cigarette smoking might be an independent risk factor for the incident chronic kidney disease 26 . The underlying mechanism remained unclear. The rarely formed isomers of DNA bases with the spontaneous mutations during the copying of DNA and tautomerization model of mutagenesis 27 might be associated with the impacts from smoking to lead to diabetic nephropathy. In addition, smoking behaviors might lead to the increase of prothrombotic factors and platelet activation to cause the thrombosis, oxidative stress, and inflammation of vessels. The 80% of smoking subjects might have the significant neovascularization of glomerular or mesangial areas within the kidney. The accompanying endothelial and subendothelial tissue alterations with the chronic healing thrombotic microangiopathy might lead to glomerular sclerosis, and the atrophy of renal tube 28 , 29 , 30 . The nicotine‐related enhancement of mesangial cell proliferation, the poor glycemic control, the unfavorable lipid profile, and the accompanying hypertension might also play roles in the progression of diabetic nephropathy in smoking patients with diabetes 7 . In the subgroup analysis, the log OR value of type 1 DM was higher than that of type 2 DM, which might suggest that the smoking patients with type 1 DM have a significantly higher risk proceeding to diabetic nephropathy. Since type I DM is early‐onset, the clinicians should note the higher risk of diabetic nephropathy in this kind of patients. Ahead of the appearance of albuminuria, the genetic mutations of type 1 DM patients might be predisposed to developing kidney disease, such as diabetic nephropathy 31 . If comorbid with smoking, the above‐mentioned kidney damage mechanisms might be strengthened and increase the risk of diabetic nephropathy in type 1 DM patients. Therefore, the clinicians should also pay more attention to smoking patients with type 1 DM, who are easier to proceed to the stage of diabetic nephropathy. In addition, according to the current results, the promotion of smoking cessation in DM patients should be enhanced. In the previous study, the smoking cessation of DM patients would be beneficial from the 30% reduction of mortality, including the nephropathy‐related mortality 32 . The smoking cessation might also ameliorate the progressive renal damage caused by smoking in DM patients with microalbuminuria 33 . The clinicians can collaborate with the diabetic educators to strengthen the monitoring of smoking conditions and promote the smoking cessation under the architecture of diabetic education to enhance the knowledge of the damage of smoking behaviors in the renal function of DM patients 34 .

Several limitations should be considered when interpreting the results of this meta‐analysis. First, the variation of age in the included studies might influence the meta‐analysis results. The prevalence in the different age groups of diabetic nephropathy is also variable. However, it is impossible to confirm a possible subgroup effect related to patient age. Therefore, the age factors should be kept in mind. Second, gender might be variable in the enrolled studies, which might influence the interpretation of our results. However, subgroup analysis in this perspective seemed difficult due to lack of gender comparison of smoking and diabetic nephropathy. Third, the different types of diabetic nephropathy in the enrolled studies might not have been revealed in the current meta‐analysis. For example, proteinuria, albuminuria, or microalbuminuria may vary in the enrolled studies. In addition, the detailed diagnosis and classification of diabetic nephropathy have not been performed in all included studies. Fourth, the lack of patient‐level data might also influence the interpretation of our results due to the lack of a full evaluation of patient‐level covariates across comparisons. Fifth, variations in the total sample size of enrolled subjects in the enrolled studies should not be ignored when interpreting the meta‐analysis results. Some included studies had a huge sample size and other included studies had a relatively limited sample size. The heterogeneity of sample size might also lead to clinical heterogeneity.

Current and former smoking might be the risk factors for diabetic nephropathy in the current meta‐analytic results. The phenomenon of such significant associations were also discovered in type 1 and type 2 diabetes.

DISCLOSURE

The authors declare no conflict of interest.

Approval of the research protocol: Not applicable because this is a systematic review and meta‐analysis article.

Informed Consent: Not applicable.

Approval date of Registry and the Registration No. of the study/trial: Not applicable because this is a systematic review and meta‐analysis article.

Animal Studies: None.

FUNDING

This research received funding from Application evaluation of TCM diagnosis and treatment scheme of diabetes nephropathy based on the theory of “kidney collateral stasis” in real world (2022SF‐204) of Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, 710000, China.

ETHICS STATEMENTS

Our study did not require an ethical board approval because this is a systematic review and meta‐analysis article.

ACKNOWLEDGMENTS

Application evaluation of TCM diagnosis and treatment scheme of diabetes nephropathy based on the theory of “kidney collateral stasis” in real world (2022SF‐204) of Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, 710000, China.

REFERENCES

  • 1. Introduction: Standards of medical care in diabetes‐2018. Diabetes Care 2018; 41(Suppl 1): S1–S2. [DOI] [PubMed] [Google Scholar]
  • 2. Ahn JH, Yu JH, Ko SH, et al. Prevalence and determinants of diabetic nephropathy in Korea: Korea national health and nutrition examination survey. Diabetes Metab J 2014; 38: 109–119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Gross JL, de Azevedo MJ, Silveiro SP, et al. Diabetic nephropathy: Diagnosis, prevention, and treatment. Diabetes Care 2005; 28: 164–176. [DOI] [PubMed] [Google Scholar]
  • 4. Yu MK, Weiss NS, Ding X, et al. Associations between depressive symptoms and incident ESRD in a diabetic cohort. Clin J Am Soc Nephrol 2014; 9: 920–928. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Radcliffe NJ, Seah JM, Clarke M, et al. Clinical predictive factors in diabetic kidney disease progression. J Diabetes Investig 2017; 8: 6–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Xue R, Gui D, Zheng L, et al. Mechanistic insight and management of diabetic nephropathy: recent progress and future perspective. J Diabetes Res 2017; 2017: 1839809. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Jiang N, Huang F, Zhang X. Smoking and the risk of diabetic nephropathy in patients with type 1 and type 2 diabetes: A meta‐analysis of observational studies. Oncotarget 2017; 8: 93209–93218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Liao D, Ma L, Liu J, et al. Cigarette smoking as a risk factor for diabetic nephropathy: A systematic review and meta‐analysis of prospective cohort studies. PLoS One 2019; 14: e0210213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Knobloch K, Yoon U, Vogt PM. Preferred reporting items for systematic reviews and meta‐analyses (PRISMA) statement and publication bias. J Craniomaxillofac Surg 2011; 39: 91–92. [DOI] [PubMed] [Google Scholar]
  • 10. Cumpston MS, McKenzie JE, Welch VA, et al. Strengthening systematic reviews in public health: Guidance in the Cochrane handbook for systematic reviews of interventions, 2nd edition. J Public Health 2022; 44: e588–e592. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Braffett BH, Rice MM, Young HA, et al. Mediation of the association of smoking and microvascular complications by glycemic control in type 1 diabetes. PLoS One 2019; 14: e0210367. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Cederholm J, Eliasson B, Nilsson P, et al. Microalbuminuria and risk factors in type 1 and type 2 diabetic patients. Diabetes Res Clin Pract 2005; 67: 258–266. [DOI] [PubMed] [Google Scholar]
  • 13. Feodoroff M, Harjutsalo V, Forsblom C, et al. Smoking and progression of diabetic nephropathy in patients with type 1 diabetes. Acta Diabetol 2016; 53: 525–533. [DOI] [PubMed] [Google Scholar]
  • 14. Group RCoPoEDR . Near‐normal urinary albumin concentrations predict progression to diabetic nephropathy in type 1 diabetes mellitus. Diabet Med 2000; 17: 782–791. [DOI] [PubMed] [Google Scholar]
  • 15. Harjutsalo V, Kallio M, Forsblom C, et al. The impact of diabetic nephropathy and severe diabetic retinopathy on chronic limb threatening ischemia risk in individuals with type 1 diabetes: A nationwide, population study. Lancet Reg Health Eur 2023; 28: 100594. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Klein R, Klein BE, Moss SE, et al. Ten‐year incidence of gross proteinuria in people with diabetes. Diabetes 1995; 44: 916–923. [DOI] [PubMed] [Google Scholar]
  • 17. Moriya T, Tanaka S, Sone H, et al. Patients with type 2 diabetes having higher glomerular filtration rate showed rapid renal function decline followed by impaired glomerular filtration rate: Japan diabetes complications study. J Diabetes Complications 2017; 31: 473–478. [DOI] [PubMed] [Google Scholar]
  • 18. Rossing P, Hougaard P, Parving H‐H. Risk factors for development of incipient and overt diabetic nephropathy in type 1 diabetic patients: A 10‐year prospective observational study. Diabetes Care 2002; 25: 859–864. [DOI] [PubMed] [Google Scholar]
  • 19. Scott LJ, Warram JH, Hanna LS, et al. A nonlinear effect of hyperglycemia and current cigarette smoking are major determinants of the onset of microalbuminuria in type 1 diabetes. Diabetes 2001; 50: 2842–2849. [DOI] [PubMed] [Google Scholar]
  • 20. Thomas M, Harjutsalo V, Feodoroff M, et al. The long‐term incidence of hospitalization for ketoacidosis in adults with established T1D‐A prospective cohort study. J Clin Endocrinol Metab 2020; 105: 231–241. [DOI] [PubMed] [Google Scholar]
  • 21. Winitzki D, Zacharias HU, Nadal J, et al. Educational attainment is associated with kidney and cardiovascular outcomes in the German CKD (GCKD) cohort. Kidney Int Rep 2022; 7: 1004–1015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Yeom H, Lee JH, Kim HC, et al. The association between smoking tobacco after a diagnosis of diabetes and the prevalence of diabetic nephropathy in the Korean male population. J Prev Med Public Health 2016; 49: 108–117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Yokoyama H, Okudaira M, Otani T, et al. High incidence of diabetic nephropathy in early‐onset Japanese NIDDM patients: risk analysis. Diabetes Care 1998; 21: 1080–1085. [DOI] [PubMed] [Google Scholar]
  • 24. Zitt E, Pscheidt C, Concin H, et al. Anthropometric and metabolic risk factors for ESRD are disease‐specific: Results from a large population‐based cohort study in Austria. PLoS One 2016; 11: e0161376. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Zoppini G, Targher G, Chonchol M, et al. Higher HDL cholesterol levels are associated with a lower incidence of chronic kidney disease in patients with type 2 diabetes. Nutr Metab Cardiovasc Dis 2009; 19: 580–586. [DOI] [PubMed] [Google Scholar]
  • 26. Xia J, Wang L, Ma Z, et al. Cigarette smoking and chronic kidney disease in the general population: A systematic review and meta‐analysis of prospective cohort studies. Nephrol Dial Transplant 2017; 32: 475–487. [DOI] [PubMed] [Google Scholar]
  • 27. Goodman MF. Smoking gun for a rare mutation mechanism. Nature 2018; 554: 180–181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Barua RS, Ambrose JA. Mechanisms of coronary thrombosis in cigarette smoke exposure. Arterioscler Thromb Vasc Biol 2013; 33: 1460–1467. [DOI] [PubMed] [Google Scholar]
  • 29. Caimi G, Hopps E, Montana M, et al. Nitric oxide metabolites (nitrite and nitrate) in several clinical condition. Clin Hemorheol Microcirc 2014; 56: 359–369. [DOI] [PubMed] [Google Scholar]
  • 30. Salvatore SP, Troxell ML, Hecox D, et al. Smoking‐related glomerulopathy: Expanding the morphologic spectrum. Am J Nephrol 2015; 41: 66–72. [DOI] [PubMed] [Google Scholar]
  • 31. Kovacevic S, Zdravkovic V, Blagojevic J, et al. Association of variants in AGTR1, ACE, MTHFR genes with microalbuminuria and risk factors for the onset of diabetic nephropathy in adolescents with type 1 diabetes in the population of Serbia. PLoS One 2024; 19: e0312489. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Blomster JI, Woodward M, Zoungas S, et al. The harms of smoking and benefits of smoking cessation in women compared with men with type 2 diabetes: An observational analysis of the ADVANCE (action in diabetes and vascular disease: Preterax and Diamicron modified release controlled evaluation) trial. BMJ Open 2016; 6: e009668. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Chuahirun T, Simoni J, Hudson C, et al. Cigarette smoking exacerbates and its cessation ameliorates renal injury in type 2 diabetes. Am J Med Sci 2004; 327: 57–67. [DOI] [PubMed] [Google Scholar]
  • 34. Macaller T, Brown M, Black K, et al. Collaborating with diabetes educators to promote smoking cessation for people with diabetes: The California experience. Diabetes Educ 2011; 37: 625–632. [DOI] [PubMed] [Google Scholar]

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