Advertisement

Association of smoking and cardiometabolic parameters with albuminuria in people with type 2 diabetes mellitus: a systematic review and meta-analysis

  • Debasish KarEmail author
  • Clare Gillies
  • Mintu Nath
  • Kamlesh Khunti
  • Melanie J. Davies
  • Samuel Seidu
Open Access
Review Article
  • 709 Downloads
Part of the following topical collections:
  1. Diabetic Nephropathy

Abstract

Aims

Smoking is a strong risk factor for albuminuria in people with type 2 diabetes mellitus (T2DM). However, it is unclear whether this sequela of smoking is brought about by its action on cardiometabolic parameters or the relationship is independent. The aim of this systematic review is to explore this relationship.

Methods

Electronic databases on cross-sectional and prospective studies in Medline and Embase were searched from January 1946 to May 2018. Adult smokers with T2DM were included, and other types of diabetes were excluded.

Results

A random effects meta-analysis of 20,056 participants from 13 studies found that the odds ratio (OR) of smokers developing albuminuria compared to non-smokers was 2.13 (95% CI 1.32, 3.45). Apart from smoking, the odds ratio of other risk factors associated with albuminuria were: age 1.24 (95% CI 0.84, 1.64), male sex 1.39 (95% CI 1.16, 1.67), duration of diabetes 1.78 (95% CI 1.32, 2.23), HbA1c 0.63 (95% CI 0.45, 0.81), SBP 6.03 (95% CI 4.10, 7.97), DBP 1.85 (95% CI 1.08, 2.62), total cholesterol 0.06 (95% CI − 0.05, 0.17) and HDL cholesterol − 0.01 (95% CI − 0.04, 0.02), triglyceride 0.22 (95% CI 0.12, 0.33) and BMI 0.40 (95% CI 0.00–0.80). When the smoking status was adjusted in a mixed effect meta-regression model, the duration of diabetes was the only statistically significant factor that influenced the prevalence of albuminuria. In smokers, each year’s increase in the duration of T2DM was associated with an increased risk of albuminuria of 0.19 units (95% CI 0.07, 0.31) on the log odds scale or increased the odds approximately by 23%, compared to non-smokers. Prediction from the meta-regression model also suggested that the odds ratios of albuminuria in smokers after a diabetes duration of 9 years and 16 years were 1.53 (95% CI 1.10, 2.13) and 5.94 (95% CI 2.53, 13.95), respectively.

Conclusions

Continuing to smoke and the duration of diabetes are two strong predictors of albuminuria in smokers with T2DM. With a global surge in younger smokers developing T2DM, smoking cessation interventions at an early stage of disease trajectory should be promoted.

Keywords

Type 2 diabetes mellitus Albuminuria Smoking 

Abbreviations

T1DM

Type 1 diabetes mellitus

T2DM

Type 2 diabetes mellitus

HbA1c

Glycosylated haemoglobin

HDL

High-density lipoprotein

LDL

Low-density lipoprotein

SBP

Systolic blood pressure

DBP

Diastolic blood pressure

Introduction

Smokers with T2DM are disproportionately affected by premature cardiovascular events. A recent systematic review of over 1 million people revealed that smokers with T2DM were approximately 50% more likely to die prematurely with cardiovascular events, compared to non-smokers [1]. However, the precise underlying cause for this heightened cardiovascular mortality remains unexplored. Smoking exacerbates insulin resistance, and adversely affects some cardiometabolic risk factors in T2DM including HbA1c, HDL cholesterol and arterial blood pressure [2]. Surprisingly, however, smoking cessation does not appear to confer any substantial cardiovascular risk reduction for up to 10 years in people with diabetes, compared to 3 years in people without [3]. Indeed, the World Health Organization (WHO) Multinational Study of Vascular Disease in Diabetes (MSVDD) demonstrated that the risk of cardiovascular mortality in people with diabetes remains up to 50% higher in recent quitters (1–9 years), compared to non-smokers [4]. This incongruous relationship between smoking cessation and mortality suggests that there may be some additional risk factor/s that contribute to a higher cardiovascular risk in recent quitters, which might not be reversed by short-term abstinence from smoking.

Albuminuria is an early indicator of both micro-, and macrovascular involvements in diabetes [5, 6] and the progression of albuminuria is a reliable marker for the extent of vascular perturbation [7]. Aggressive management of traditional risk factors such as glucose, blood pressure and lipid profile has not shown consistent benefit particularly when proteinuria is already established [8]. On the other hand, multifactorial interventions including smoking cessation at an early stage of disease trajectory have shown promising potential for the reversal of microalbuminuria and improved cardiovascular outcome [9]. However, conventional risk stratification score derived from the HbA1c, blood pressure and lipid profile may underestimate the influence of life style factors such as obesity and smoking on albuminuria during this crucial stage of disease trajectory. With a global surge of younger people developing metabolic syndrome and T2DM, it is pivotal to explore how best they can be protected from albuminuria which not only heralds incipient diabetic nephropathy but also poses a higher risk for premature cardiovascular complications. The aim of this systematic review and meta-analysis is to elucidate how smoking impacts upon the prevalence of albuminuria and how this relationship is influenced by cardiovascular risk factors such as age, male sex, duration of diabetes, HbA1c, blood pressure, lipid profile and body mass index (BMI).

Materials and methods

Search strategy and selection criteria

For this systematic review and meta-analysis, we conducted a comprehensive search on Medline and Embase electronic databases from their inceptions to May 2018. The keywords used for the searches were: “type 2 diabetes”, “smoking”, “microalbuminuria” or “macroalbuminuria” or “albuminuria” or “proteinuria” in the title, abstract and keywords; the result was then combined using the Boolean operator “AND”. Additionally, we searched the references of the included studies to identify further suitable studies for inclusion. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocol (PRISMA-P) 2015 guidelines [10] (Fig. 1). We published the protocol in the International Prospective Register for Systematic Reviews (PROSPERO) database (CRD 42018090637). The full search strategy is in Supplementary material 1.

Fig. 1

PRISMA flow chart

The inclusion criteria were studies reporting urinary albumin excretion (UAE) in adults (> 18 years) with T2DM. T2DM was defined as a condition affecting people’s blood sugar level which was then diagnosed by healthcare professionals and treated with diet, lifestyle interventions, oral medication or injectable therapy. People with type 1 diabetes mellitus (T1DM), steroid-induced diabetes, diabetes insipidus, and late auto-immune diabetes of adults (LADA) were excluded, but maturity-onset diabetes of the young (MODY) were included. Smokers were defined as self-reported cigarette smokers for at least a year after being diagnosed with T2DM. For this study, albuminuria was defined as urinary albumin creatinine ratio (ACR) > 20 mg/gm or > 2.5 mg/mmol in male or > 3.5 mg/mmol in female (KDIGO—Kidney Disease Improving Global Outcome guidelines http://kdigo.org) [11]. Total cholesterol and HDL were converted to mmol/l if they were reported in mg/dl (mg/dl = ÷ 38.67 mmol/l), and TG (mg/dl = ÷ 88.57). HbA1c was expressed in both IFCC unit (mmol/mol) and DCCT unit (%). Blood pressure was expressed in mm of Hg and BMI was expressed as kg/m2.

Studies in English language or translated into the English language were accepted for inclusion. Observational prospective and cross-sectional studies were included for this review. Two investigators (DK and CLG) independently screened the articles using the inclusion and exclusion criteria. Any disagreement between the two investigators was resolved either by consensus or by consulting with a third investigator (SS). Included studies were selected by reviewing the titles and abstracts on electronic databases search. Additionally, hand searches were carried out from the references of the included studies.

Data analysis

Data extraction was conducted using a predesigned data extraction template—study name, year of publication, country of study, study design, number of participants, mean age, smoking status of the participants, and presence or absence of albuminuria (Table 1).

Table 1

Characteristics of the included studies (baseline data for prospective studies unless stated otherwise)

Study included name/ID

Study design

Country

Mean age (years)

Sex (% male)

Number of participants (n)

Smoking status (n)

Albuminuria (n)

Mean duration of DM

Mean HbA1c

Mean SBP

S

NS

Q

Yes

No

(years)

mmol/mol(%)

(mm of Hg)

Chuahirun et al., 2003 [50]/08

Prospective

USA

45

55

33

13

20

NS

NS

NS

NS

92 (10.6)

NS

Chuahirun et al., 2004 [51]/09

Prospective

USA

45

54

84

31

53

NS

46

38

5

95 (10.8)

115

Chuahirun et al., 2004 [60]/09

Prospective

USA

49

50

157

69

88

NS

112

45

5

54 (7.06)

132

Ikeda et al., 1996 [52]/15

Cross-sectional

Japan

62

100

142

81

40

21

58

84

NS

62 (7.8)

137

Tseng et al., 2010 [53]/28

Prospective

Taiwan

58

55

519

199

320

NS

240

279

10

64 (8.0)

132

Voulgari et al., 2011 [54]/29

Prospective

Greece

56

50

193

73

NS

120

193

NS

NS

61 (7.75)

143

Phistkul et al., 2008 [55]/23

Prospective

USA

47

52

91

39

52

NS

91

NS

4

59 (7.53)

145

Hsu et al., 2010 [56]/14

Prospective

Taiwan

54

100

509

191

243

75

314

195

4

66 (8.2)

129

Baggio et al., 2002 [37]/02

Cross-sectional

Italy

58

73

96

48

48

NS

96

NS

11

65 (8.1)

NS

Cederholm et al., 2005 [57]/06

Cross-sectional

Sweden

67

59

31,037

4532

26,505

NS

4811

26,226

8

51 (6.85)

147

Savage et al., 1995 [58]/26

Cross-sectional

USA

58

61

931

264

230

439

402

531

9

103 (11.6)

NS

Okhuma et al., 2016 [59]/21

Cross-sectional

Japan

65

100

2770

760

559

1451

NS

NS

19

57 (7.40)

130

Prashanth et al., 2010 [60]/25

Cross-sectional

Oman

NS

51

447

85

362

NS

163

284

10

70 (8.55)

NS

Corradi et al., 1993 [61]/10

Cross-sectional

Italy

NS

100

90

44

46

NS

46

44

NS

60 (7.65)

162

Anan et al., 2007 [62]/01

Cross-sectional

Japan

45

18

55

20

35

NS

NS

NS

5

60 (7.65)

129

Yoem et al., 2016 [63]/31

Cross-sectional

Korea

63

100

629

314

90

225

455

174

9

58 (7.44)

126

Forsblom et al., 1998 [64]/11

Prospectivea (follow-up data)

Finland

58

61

108

36

54

NS

31

59

9

95 (10.8)

152

Tomlinson et al., 2006 [65]/27

Cross-sectional

China

53

100

496

196

300

NS

NS

NS

3

63(7.94)

133

Kanauchi et al., 1998 [66]/16

Cross-sectional

Japan

65

46

155

44

111

NS

78

77

13

56 (7.3)

NS

Gambaro et al., 2001 [67]/12

Prospective

Italy

65

55

273

72

134

67

107

203

13

75 (9.0)

NS

West et al., 1980 [68]/30

Cross-sectional

USA

NS

NS

973

323

421

229

416

557

7

NS

137

Klein et al., 1993 [69]/17

Cross-sectional

USA

NS

NS

376

53

200

123

58

318

NS

NS

NS

Bruno et al., 1996 [70]/04

Cross-sectional

Italy

69

43

1521

NS

NS

NS

756

765

11

64 (8.05)

NS

Bruno et al., 2003 [71]/05

Prospective

Italy

68

38

1103

149

708

222

426

677

10

65 (8.1)

154

Bentata et al., 2016 [72]/03

Prospective

Morocco

65

NS

671

81

590

NS

520

151

8

68 (8.4)

NS

Gerstein et al., 2000 [43]/13

Cross-sectional

Canada

65

63

3503

538

N/A

1777

1128

2375

11

58 (7.46)

142

Kohler et al., 2000 [73]/18

Cross-sectional

USA

51

32

1044

NS

NS

NS

244

760

0.3

76 (9.1)

NS

Nilsson et al., 2004 [74]/20

Cross-sectional

Sweden

65

54

40,648

4512

36,136

NS

5578

35,070

8

48 (6.55)

144

Parving et al., 2006 [76]/22

Cross-sectional

Denmark

61

50

24,151

NS

NS

NS

NS

NS

8

58 (7.5)

NS

Pijls et al., 2001 [75]/24

Cross-sectional

Netherlands

64

49

335

NS

NS

NS

NS

NS

6

NS

143

N/S not specified

aBoth groups are normoalbuminuric at the baseline

Study-level data were also compiled for HbA1c, TC, HDL cholesterol, triglyceride, BMI, SBP and DBP. Continuous data were expressed as mean ± SD (standard deviation). For cross-sectional studies prevalence data, and for prospective studies baseline data, were extracted. In prospective studies, if albuminuria was absent at the baseline but was present at follow-up, then the follow-up data were obtained. Extracting data from all studies at just one time point, allowed both cross-sectional studies and cohort studies to be combined in the meta-analyses. The study team used the Newcastle–Ottawa Tool for the assessment of the quality of observational studies to assess the quality of included studies [12]. A random effect meta-analysis was conducted to assess the odds of having albuminuria between smokers and non-smokers. Further random effects meta-analyses models were fitted to compare participants with and without albuminuria for other risk factors (age, sex, duration of type 2 diabetes systolic and diastolic blood pressure, total cholesterol, HDL cholesterol, triglyceride, BMI and HbA1c), with categorical outcomes fitted as odds ratios and continuous variables as difference in mean values. To explore the relationship between smoking and albuminuria, meta-regression analyses were carried out. To investigate the influence of duration of diabetes on the risk of albuminuria between smokers and non-smokers further, we used the mixed effect meta-regression model to predict the odds ratio and corresponding 95% confidence intervals of albuminuria, among smokers compared to non-smokers for the duration of type 2 diabetes ranging from 4 to 20 years.

The heterogeneity between studies was assessed using the I2 statistic, which represents the total proportion of study variation that is due to heterogeneity rather than sampling error/chance [13]. Publication bias among studies was assessed by visual inspection of the funnel plot and the Egger’s test. The type 1 error to determine the level of statistical significance was set at p = 0.05. All statistical analyses were carried out using the metafor package (version 2.0.0) in the R statistical software environment and Cochrane Collaboration Review Manager version 5.

Results

A total of 2207 studies were identified by electronic database searches. After removing the duplicates, 2119 articles were screened for eligibility; 150 of them were accepted for abstract review, and 58 of them were included for full-text review. Overall, 30 studies (20 cross-sectional and ten prospective observational) with a total of 113,140 people with T2DM were included. The mean age of the study participants was 58 years, and 51% of them were male. Amongst the study participants, 11% were smokers, 60% were non-smokers, and 4% were quitters. Smoking status was unavailable for 25% of the study participants. The prevalence of albuminuria in the included studies was 14%. The mean duration of T2DM was 8 years; the mean HbA1c was 63 mmol/mol (7.9%), and the mean SBP was 125 mmHg. The outcomes from the random effects meta-analysis of 13 studies on 4313 smokers and 15,743 non-smokers showed that the pooled odds ratio of albuminuria in smokers, compared to non-smokers was 2.13 (95% CI 1.32–3.45; p = 0.002; Fig. 2), indicating a statistically significant increased risk of albuminuria in smokers.

Fig. 2

Forest plot showing an odds ratio of albuminuria in smokers compared to non-smokers

Except for one study, the radial plot suggested that the outcomes for most of the studies were consistent, regarding the effects of smoking and the variation in the risk of albuminuria (Fig. 3).

Fig. 3

A radial plot of random effects meta-analysis showing the standardized differences in observed outcomes (zi) between smokers against their corresponding precision (xi). The plot demonstrates that the differences in outcomes between smokers and non-smokers were consistent for most studies suggesting that other factors were unlikely to contribute to the variation in the risk of albuminuria

The visual exploration of the funnel plot showed slight asymmetry of the plot suggesting possible publication bias. However, outcomes from the Egger’s test showed the evidence was not statistically significant (p = 0.063) (Supplementary material 2).

Further meta-analyses demonstrated that cardiometabolic factors associated with albuminuria (OR, 95% CI) were: age 1.24 (95% CI 0.84–1.64, p < 0.001); male sex 1.39 (95% CI 1.16–1.67; p = 0.003); SBP 6.03 (95% CI 4.10–7.97, p < 0.001); DBP 1.85 (95% CI 1.08–2.62, p < 0.001); duration of T2DM 1.78 (95% CI 1.32–2.23, p < 0.001); BMI 0.40 (95% CI 0.00–0.80, p = 0.05); total cholesterol 0.06 (95% CI − 0.05 to 0.17; p = 0.31); HDL − 0.01 (95% CI − 0.04 to 0.02; p = 0.47); triglyceride 0.22 (95% CI 0.12–0.33; p < 0.001) and HbA1c 0.63 (95% CI 0.45–0.81; p < 0.001) (Table 2) (Supplementary material 3).

Table 2

Relationship of cardiometabolic risk factors and albuminuria before adjusting for smoking status

Variables

Mean difference

95% confidence interval

p value

Age

1.24

0.84–1.64

< 0.001

Male sex

1.39

1.16–1.67

0.003

SBP

6.03

4.10–7.97

< 0.001

DBP

1.85

1.08–2.62

< 0.001

HbA1c

0.63

0.45–0.81

< 0.001

Duration of diabetes

1.78

1.32–2.23

< 0.001

Total cholesterol

0.06

− 0.05 to 0.17

0.31

HDL cholesterol

− 0.01

− 0.04 to 0.02

0.47

Triglyceride

0.22

0.12–0.33

< 0.001

Body mass index

0.40

− 0.00 to 0.80

0.05

Meta-regression analyses found most moderator variables were not associated with the study effect except the duration of diabetes showing a significant association (p = 0.001). We observed that the inclusion of duration of diabetes as a moderator variable reduced the residual heterogeneity although there was still evidence of residual heterogeneity (Q statistic = 10.09, p = 0.002); the estimate of residual heterogeneity (τ2) reduced from 0.69 (95% CI 0.38–3.84) based on the random effect meta-analysis model to 0.23 (95% CI 0.10–2.13) based on the mixed effect meta-regression model (Table 3) (Supplementary material 3). Therefore, the time to diabetes as a moderator variable accounted for almost 60% of the heterogeneity.

Table 3

Relationship of cardiometabolic risk factors with albuminuria after adjusting for smoking status

Moderator variables

Overall effect size (Z)

Heterogeneity (τ2)

p value

Age

0.75 (− 0.084–0.18)

0.70 (0.33–6.44)

0.46

Male sex

0.27 (− 0.02–0.03)

0.79 (0.36–6.81)

0.78

HbA1c

1.43 (0.1–0.65)

0.76 (0.30–4.94)

0.15

HDL

− 0.50 (− 47.78 to 28.83)

9.93 (1.66–100)

0.61

Total cholesterol

0.92 (− 1.36 to 3.75)

1.74 (0.56–15.78)

0.35

Triglyceride

− 1.14 (− 0.51 to 0.14)

0.01 (0–1.28)

0.25

Duration of diabetes

3.18 (0.07–0.31)

0.23 (0.10–2.13)

0.001

SBP

1.09 (− 0.29 to 0.101)

1.26 (0.44–10.22)

0.27

DBP

0.26 (− 0.13 to 0.17)

2.05 (0.66–18.43)

0.79

BMI

2.48 (0.15–1.30)

0.74 (0.36–6.86)

0.93

Statistically significant variable that influenced the relationship between smoking and albuminuria was the duration of T2DM (highlighted in bold font)

The statistically significant residual heterogeneity suggested that other moderators not investigated in this study might be important. The duration of T2DM was positively associated with albuminuria: each year increase in the duration of T2DM was associated with an increased log of odds of albuminuria on an average by 0.19 units (95% CI 0.07–0.31), or it increased the odds approximately by 21% (Fig. 4). After 9 years of diabetes, the odds of albuminuria in smokers was approximately 50% higher 1.53 (1.10–2.43) compared to non-smokers. The odds ratio rose further to almost three times at 12-year duration 2.74 (1.74–4.30) and almost six times after 16 years 5.94 (2.58–15.05). The predicted mean odds ratio of albuminuria among smokers compared to non-smokers conditional on a range of the duration of diabetes are presented in Supplementary material 4.

Fig. 4

Predicted odds ratio (OR) of albuminuria in smokers compared to non-smokers with duration of type 2 diabetes based on the outcome of the logistic mixed model. The solid line shows the predicted mean and dashed line shows the corresponding 95% confidence interval. The OR below the horizontal dotted line is not statistically significant (p > 0.05). The plot also shows the observed OR of individual studies (points) where the point sizes are proportional to the inverse of the corresponding standard errors

Discussion

This systematic review summarises the relationship between smoking and albuminuria in people with T2DM, and whether this relationship is influenced by other confounding variables such as age, sex, the duration of T2DM, HbA1c, BMI, HDL and total cholesterol, systolic and diastolic blood pressure. The meta-analysis suggests that smoking is a strong predictor of albuminuria in people with T2DM. The meta-regression, on the other hand, concedes that apart from the duration of T2DM, none of the above confounding variables has any statistically significant influence on albuminuria, when adjusted for smoking status. There is a linear relationship between smoking and the duration of T2DM with albuminuria. Smokers with T2DM have 21% increased annual risk of albuminuria, compared to non-smokers. Therefore, smoking cessation at an early stage of disease trajectory is likely to be one of the most effective intervention strategies to prevent the development of albuminuria in smokers with T2DM.

This is the first systematic review and meta-analysis exploring the relationship between smoking and albuminuria, and how other cardiometabolic parameters influence this relationship. Although multiple studies have shown smoking augments the risk of albuminuria in people with type 1 diabetes [14, 15], its role in T2DM remains undetermined. T2DM, as opposed to T1DM, is one of the components of metabolic syndrome. In addition to hyperglycaemia, it is often accompanied by obesity, hypertension and dyslipidaemia [16]. All these risk factors are closely associated with albuminuria [17], and therefore, the relationship between smoking and albuminuria is much more intricate in T2DM, compared to T1DM. Previous studies have shown that smokers have a higher urinary albumin excretion rate, which might have been independent of glycaemic effects [18]. Meta-analyses in this systematic review concluded that there is a close association between smoking and albuminuria in people with T2DM. Meta-regression, on the other hand, taking into consideration all the above confounding variables, concluded that the duration of diabetes is the most important predictor of albuminuria in smokers with T2DM.

Early detection of albuminuria at the stage of microalbuminuria, and multifactorial intervention including smoking cessation, are advocated in all the guidelines across the globe, including the European Association of Study on Diabetes (EASD) and the American Diabetes Association (ADA) [19]. This recommendation is based on the observation that once the daily urinary albumin excretion rate reaches the level of proteinuria (urinary albumin excretion > 300 mg/day), no interventions appears to be effective in reversing it [20, 21]. Addressing other anthropometric and metabolic risk factors including hip–waist ratio, BMI, HbA1c, blood pressure and lipid profiles remain at the centre of this intervention strategy. For glycaemic management, the choice of drugs seems to be a determinant factor of albuminuria. Insulin sensitizers have shown better efficacy in halting the prevalence and progression of albuminuria compared to insulin and its secretagogues. In BARI-2D trial, the researchers have shown that insulin, and its secretagogues are more likely to cause an increased prevalence of albuminuria and coronary artery disease, compared to insulin-sensitizing drugs [22]. However, it will be interesting to know if this outcome is influenced by the choice of drugs or people who were on insulin had poorer glycaemic control.

Irrespective of hypertension, treatment with angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB) has shown promising prospect of halting the prevalence and progression of albuminuria [23]. However, studies have shown that this reno-protective effect of ACEI and ARBs can be revoked in smokers [24], suggesting that renin–angiotensin axis blockade is less effective to prevent the progression of albuminuria in smokers. Raised triglyceride, and raised total and LDL cholesterol, with low HDL cholesterol are the hallmarks of dyslipidaemia in T2DM [25], but in smokers the predominant abnormality in lipid profile seems to be lower HDL cholesterol [26]. Smoking downregulates the hepatic and endothelial lipoprotein lipase activities [27] and tampers with the reverse cholesterol transport pathway [28]. As a consequence, they have lower HDL cholesterol compared to their non-smoker counterparts. Smoking cessation, on the other hand improves lipid profile particularly the HDL cholesterol, despite moderate weight gain [29, 30], which in turn halts the progression of albuminuria [31]. Conversely, isolated and piecemeal management of glucose, blood pressure and lipid profile did not show consistent efficacy to prevent the prevalence or progression of albuminuria in smokers with diabetes [9, 32]. These observations are suggestive of an independent relationship between smoking and albuminuria mediated by a constellation of underlying pathophysiological processes.

Several mechanisms have been proposed to explain the albuminuria in smokers with T2DM. They include increased blood pressure, altered intrarenal haemodynamics such as activation of the sympatho-adrenergic pathway, activation of the renin–angiotensin–aldosterone axis and the endothelin system [33, 34, 35]. In addition, smoking directly causes tubulo-interstitial disease [36] and causes neuro-endocrine disruption, vascular endothelial damage and metabolic deregulations which adversely affect renal structure and function [18, 37, 38]. Therefore, addressing hyperglycaemia, hypertension and dyslipidaemia without smoking cessation may not halt the prevalence and progression of albuminuria in smokers with diabetes.

Nicotine and other toxic metabolites in cigarettes appear to be handled differently in people with and without diabetes [39]. Nicotine infusion acutely increases insulin resistance in people with T2DM but not in people without [40]. Although smokers have lower BMI than non-smokers, nonetheless they have more visceral adiposity and lower insulin sensitivity [41]. Smoking cessation, on the other hand, despite causing moderate weight gain, is associated with the reversal of visceral adiposity and an improvement in insulin sensitivity [30]. But this reversal takes longer in people with T2DM, compared to people without [3, 42]. Therefore, short-term abstinence may not yield any meaningful benefit in smokers with T2DM. The Heart Outcomes Prevention Evaluation (HOPE) study examined the factors that influence the prevalence and progression of albuminuria in people with and without diabetes. This study demonstrated that smoking, hypertension, older age, abdominal adiposity, vascular disease and left ventricular hypertrophy were significantly associated with albuminuria, in people with and without diabetes. However, in people with diabetes, the most significant determinants of albuminuria were the duration of diabetes, HbA1c and the use of insulin. People with diabetes were 1.16 times more likely to develop albuminuria after a diabetes duration of 10.4 years (irrespective of their HbA1c), the risk of albuminuria increased by 8% for each 0.9% increase in the HbA1c, and the people with albuminuria were 1.3 times more likely to be on insulin compared to people who had normoalbuminuria. Sex, dyslipidaemia, creatinine, and BMI were not independently associated with albuminuria after adjustment of other factors [43]. Taking all these evidence into account, this systematic review emphasises that to effectively manage the prevalence and the progression of albuminuria in T2DM, the most effective strategy would be a multifactorial intervention where smoking cessation is one of the key components.

The findings of this systematic review have significant clinical implications. The World Health Organization (WHO) estimates that by 2030, a staggering number of 366 million people will suffer from T2DM worldwide. Amongst them, 60 million will be between 20 and 44 years, and 180 million will be between 45 and 64 years [44]. Young smokers with T2DM are at a higher risk of albuminuria as they will live longer with the condition. This study showed that the risk of albuminuria was similar in smokers and non-smokers up to around 8.5 years of T2DM duration, and then the risk increased approximately by 20% annually. Albuminuria marks the onset of microvascular complications which is often associated with retinopathy, neuropathy and macrovascular involvement [45]. Several studies have shown a rapid rise in the prevalence of albuminuria and cardiovascular complications in younger patients with T2DM, compared to T1DM, despite having similar glycaemic control [46, 47]. Poor lifestyle choices including smoking have been attributed to this disparate response of glycaemic control in T2DM, as opposed to T1DM. Therefore, this study emphasises that smokers, particularly the younger smokers should be encouraged to quit soon after the diagnosis and persuaded to remain abstinent.

One of the strengths of this study is that it included all the major studies available on the electronic databases from their inception and included 30 studies with 113,400 participants. The quality of the papers was determined by the Newcastle–Ottawa scale, which is a validated tool, and the review process followed PRISMA protocol [10], which is considered to be the gold standard. Publication bias was addressed by conducting the appropriate sensitivity test, which did not show any significant bias. On the other hand, the weaknesses of the study were that it was based mainly on cross-sectional, or the baseline data of prospective studies, and therefore, no temporal relationship between smoking and albuminuria can be confirmed. Second, most of the included studies used self-reported smoking behaviour which might not be accurate. There was also considerable heterogeneity in the included studies, and therefore, the findings may not be generalisable. Although between-study heterogeneity was investigated, meta-regression models lacked statistical power to assess associations between the effect size and study-level covariates.

Conclusion

Albuminuria is one of the earliest biochemically measurable risk factors in T2DM, which heralds incipient micro- and macrovascular complications. It is a substantial milestone in the trajectory of disease progression and is independently associated with cardiovascular and all-cause mortality. This study reiterates that smoking is a strong predictor of albuminuria; the longer the duration of T2DM, the higher the risk. With a rapidly changing global prevalence of T2DM with an anticipated surge of younger people with T2DM [48], and an approximately 70% of them already having complications [49], it is important to raise awareness about the effect of smoking and duration of T2DM on albuminuria, and its impact on cardiovascular mortality. Future research should be focused on elucidating the relationship between smoking cessation, and the progression of albuminuria in people with T2DM, particularly the length of abstinence required to reverse the risk of albuminuria.

Notes

Author contributions

The original idea of the research came from DK, who did the searches, conducted the study design, and registered the project in PROSPERO. CLG and SS contributed to data collection, data analysis and writing up. CLG and MN contributed to statistical analyses of the data. DK wrote up the manuscript, and all the co-authors contributed to it. KK and MJD were involved in overall supervision and worked in advisory roles in all aspects of the research.

Funding

This research was partially funded by the East Midlands Collaboration for Leadership in Applied Health Research and Care (Project 17).

Compliance with ethical standards

Conflict of interest

DK, CLG and MN have no competing interests. MJD reports personal fees from Novo Nordisk, Sanofi-Aventis, Lilly, Merck Sharp & Dohme, Boehringer Ingelheim, AstraZeneca, Janssen, Servier, Mitsubishi Tanabe Pharma Corporation, Takeda Pharmaceuticals International Inc. She has also received grants from Novo Nordisk, Sanofi-Aventis, Lilly, Boehringer Ingelheim, Janssen outside the submitted work. Prof Khunti has acted as a consultant and speaker for Amgen, AstraZeneca, Bayer, Novartis, Novo Nordisk, Roche, Sanofi-Aventis, Lilly, Servier and Merck Sharp & Dohme. He has received grants in support of investigator and investigator-initiated trials from AstraZeneca, Novartis, Novo Nordisk, Sanofi-Aventis, Lilly, Pfizer, Boehringer Ingelheim and Merck Sharp & Dohme. KK has received funds for research, honoraria for speaking at meetings and has served on advisory boards for AstraZeneca, Lilly, Sanofi-Aventis, Merck Sharp & Dohme and Novo Nordisk. SS has acted as a consultant on advisory boards and speaker for Novartis, Novo Nordisk, Sanofi-Aventis, Lilly, and Merck Sharp & Dohme, Amgen, Boehringer Ingelheim, Janssen and Takeda Pharmaceuticals International Inc.

Ethics standard statement

Not applicable.

Consent for publication

All the authors have approved the final manuscript and consented for publication.

Availability of data and material

The corresponding author has all the data and materials.

Supplementary material

592_2019_1293_MOESM1_ESM.docx (45 kb)
Supplementary material 1 (DOCX 45 KB)
592_2019_1293_MOESM2_ESM.docx (207 kb)
Supplementary material 2 (DOCX 206 KB)
592_2019_1293_MOESM3_ESM.docx (1.2 mb)
Supplementary material 3 (DOCX 1190 KB)

References

  1. 1.
    Pan A, Wang Y, Talaei M, Hu FB (2015) Relation of smoking with total mortality and cardiovascular events among patients with diabetes mellitus: a meta-analysis and systematic review. Circulation 132(19):1795–1804Google Scholar
  2. 2.
    Kar D, Gillies C, Zaccardi F, Webb D, Seidu S, Davies M et al (2016) Relationship of cardiometabolic parameters in non-smokers, current smokers, and quitters in diabetes: a systematic review and meta-analysis. Cardiovasc Diabetol 15:158Google Scholar
  3. 3.
    Clair C, Rigotti NA, Porneala B, Fox CS, D’Agostino RB, Pencina MJ et al (2013) Association of smoking cessation and weight change with cardiovascular disease among adults with and without diabetes. JAMA 309(10):1014–1021Google Scholar
  4. 4.
    Chaturvedi N, Stevens L, Fuller JH (1997) Which features of smoking determine mortality risk in former cigarette smokers with diabetes? The World Health Organization Multinational Study Group. Diabetes care 20(8):1266–1272Google Scholar
  5. 5.
    Eijkelkamp WB, Zhang Z, Brenner BM, Cooper ME, Devereux RB, Dahlof B et al (2007) Renal function and risk for cardiovascular events in type 2 diabetic patients with hypertension: the RENAAL and LIFE studies. J Hypertens 25:876Google Scholar
  6. 6.
    Lee ET, Howard BV, Wang W, Welty TK, Galloway JM, Best LG et al (2006) Prediction of coronary heart disease in a population with high prevalence of diabetes and albuminuria: the Strong Heart Study. Circulation 113(25):2897–2905Google Scholar
  7. 7.
    Singh DK, Winocour P, Summerhayes B, Sivakumar G, Viljoen A, Farrington K (2009) The relationship of albuminuria and vascular calcification in type 2 diabetes. Diabetes 58(1):A198Google Scholar
  8. 8.
    Di Landro D, Catalano C, Lambertini D, Bordin V, Fabbian F, Naso A et al (1998) The effect of metabolic control on development and progression of diabetic nephropathy. Nephrol Dial Transplant 13(Suppl 8):35–43Google Scholar
  9. 9.
    Ascic-Buturovic B, Kacila M, Kulic M (2009) Effects of aggressive approach to the multiple risk factors for diabetic nephro-pathy on proteinuria reduction in diabetes type 2 patients. Bosn J Basic Med Sci 9(1):44–48Google Scholar
  10. 10.
    Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M et al (2015) Preferred reporting items for systematic review and meta-analysis protocols (prisma-p) 2015: elaboration and explanation. BMJ (Online) 349:1Google Scholar
  11. 11.
    Miller W, Bruns D, Hortin G, Sandberg S, Aakre K, McQueen M et al (2009) Current issues in measurement and reporting of urinary albumin excretion. Clin Chem 55(1):24–38Google Scholar
  12. 12.
    Stang A (2010) Critical evaluation of the Newcastle–Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol 25(9):603–605Google Scholar
  13. 13.
    Higgins JPT, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21(11):1539–1558Google Scholar
  14. 14.
    Chase HP, Garg SK, Marshall G, Berg CL, Harris S, Jackson WE et al (1991) Cigarette smoking increases the risk of albuminuria among subjects with type I diabetes. JAMA 265(5):614–617Google Scholar
  15. 15.
    Amin R, Widmer B, Prevost AT, Schwarze P, Cooper J, Edge J et al (2008) Risk of microalbuminuria and progression to macroalbuminuria in a cohort with childhood onset type 1 diabetes: prospective observational study. BMJ 336(7646):697–701Google Scholar
  16. 16.
    Abuaisha B, Kumar S, Malik R, Boulton AJ (1998) Relationship of elevated urinary albumin excretion to components of the metabolic syndrome in non-insulin-dependent diabetes mellitus. Diabetes Res Clin Pract 39(2):93–99Google Scholar
  17. 17.
    Temimovic R, Rasic S (2015) Impact of obesity and smoking on the values of albuminuria and proteinuria in high risk patients and its impact on development of early chronic kidney disease in outpatients in bosnia herzegovina. Nephrol Dial Transplant 30:iii489Google Scholar
  18. 18.
    Christiansen JS (1978) Cigarette smoking and prevalence of microangiopathy in juvenile-onset insulin-dependent diabetes mellitus. Diabetes care 1(3):146Google Scholar
  19. 19.
    Inzucchi SE, Bergenstal RM, Buse JB, Diamant M, Ferrannini E, Nauck M et al (2015) Management of hyperglycemia in type 2 diabetes, 2015: a patient-centered approach: update to a position statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes care 38(1):140–149Google Scholar
  20. 20.
    Boger CA, Haak T, Gotz AK, Christ J, Ruff E, Hoffmann U et al (2006) Effect of ACE and AT-2 inhibitors on mortality and progression to microalbuminuria in a nested case-control study of diabetic nephropathy in diabetes mellitus type 2: results from the GENDIAN study. Int J Clin Pharmacol Ther 44:364–74Google Scholar
  21. 21.
    Yamashita T, Makino H, Nakatani R, Ohata Y, Miyamoto Y, Kishimoto I (2013) Renal insufficiency without albuminuria is associated with peripheral artery atherosclerosis and lipid metabolism disorders in patients with type 2 diabetes. J Atheroscler Thromb 20(11):790–797Google Scholar
  22. 22.
    Wall BM, Hardison RM, Molitch ME, Marroquin OC, McGill JB, August PA (2010) High prevalence and diversity of kidney dysfunction in patients with type 2 diabetes mellitus and coronary artery disease: the BARI 2D baseline data. Am J Med Sci 339:401–10Google Scholar
  23. 23.
    Boger CA, Haak T, Gotz AK, Christ J, Ruff E, Hoffmann U et al (2006) Effect of ACE and AT-2 inhibitors on mortality and progression to microalbuminuria in a nested case-control study of diabetic nephropathy in diabetes mellitus type 2: results from the GENDIAN study. Int J Clin Pharmacol Ther 44(8):364–374Google Scholar
  24. 24.
    Chuahirun T, Wesson DE (2002) Cigarette smoking predicts faster progression of type 2 established diabetic nephropathy despite ACE inhibition. Am J Kidney Dis 39(2):376–382Google Scholar
  25. 25.
    de Boer IH, Astor BC, Kramer H, Palmas W, Seliger SL, Shlipak MG et al (2008) Lipoprotein abnormalities associated with mild impairment of kidney function in the multi-ethnic study of atherosclerosis. Clin J Am Soc Nephrol CJASN 3(1):125–132Google Scholar
  26. 26.
    Berlin I (2008) Smoking-induced metabolic disorders: a review. Diabetes Metab 34(4 Pt 1):307–314Google Scholar
  27. 27.
    Freeman DJ, Caslake MJ, Griffin BA, Hinnie J, Tan CE, Watson TD et al (1998) The effect of smoking on post-heparin lipoprotein and hepatic lipase, cholesteryl ester transfer protein and lecithin:cholesterol acyl transferase activities in human plasma. Eur J Clin Investig 28(7):584Google Scholar
  28. 28.
    Zong C, Song G, Yao S, Guo S, Yu Y, Yang N et al (2015) Cigarette smoke exposure impairs reverse cholesterol transport which can be minimized by treatment of hydrogen- saturated saline. Lipids Health Dis 14(1):159-Google Scholar
  29. 29.
    Campbell SC, Moffatt RJ, Stamford BA (2008) Smoking and smoking cessation—the relationship between cardiovascular disease and lipoprotein metabolism: a review. Atherosclerosis 201(2):225–235Google Scholar
  30. 30.
    Ferrara CM, Kumar M, Nicklas B, McCrone S, Goldberg AP (2001) Weight gain and adipose tissue metabolism after smoking cessation in women. Int J Obes 25(9):1322–1326Google Scholar
  31. 31.
    Zoppini G, Targher G, Chonchol M, Perrone F, Lippi G, Muggeo M (2009) Higher HDL cholesterol levels are associated with a lower incidence of chronic kidney disease in patients with type 2 diabetes. Nutr Metab Cardiovasc Dis 19:580–6Google Scholar
  32. 32.
    Sawicki PT, Muhlhauser I, Bender R, Pethke W, Heinemann L, Berger M (1996) Effects of smoking on blood pressure and proteinuria in patients with diabetic nephropathy. J Intern Med 239(4):345–352Google Scholar
  33. 33.
    Orth SR (2004) Effects of smoking on systemic and intrarenal hemodynamics: influence on renal function. J Am Soc Nephrol 15(Suppl 1):58Google Scholar
  34. 34.
    Ritz E, Ogata H, Orth SR (2000) Smoking: a factor promoting onset and progression of diabetic nephropathy. Diabetes Metab 26(Suppl 4):54–63Google Scholar
  35. 35.
    Orth SR, Schroeder T, Ritz E, Ferrari P (2005) Effects of smoking on renal function in patients with type 1 and type 2 diabetes mellitus. Nephrol Dial Transplant 20(11):2414–2419Google Scholar
  36. 36.
    Orth SR (2002) Cigarette smoking: an important renal risk factor—far beyond carcinogenesis. Tob Induc Dis 1(2):137–155Google Scholar
  37. 37.
    Baggio B, Budakovic A, Dalla Vestra M, Saller A, Bruseghin M, Fioretto P (2002) Effects of cigarette smoking on glomerular structure and function in type 2 diabetic patients. J Am Soc Nephrol 13(11):2730–2736Google Scholar
  38. 38.
    Agarwal R (2005) Smoking, oxidative stress and inflammation: impact on resting energy expenditure in diabetic nephropathy. BMC Nephrol 6:13Google Scholar
  39. 39.
    Canoy D, Wareham N, Luben R, Welch A, Bingham S, Day N et al (2005) Cigarette smoking and fat distribution in 21,828 british men and women: a population-based study. Obesity 13(8):1466–1475Google Scholar
  40. 40.
    Axelsson T, Jansson PA, Smith U, Eliasson B, Sahlgrenska a, Institutionen för i et al (2001) Nicotine infusion acutely impairs insulin sensitivity in type 2 diabetic patients but not in healthy subjects. J Intern Med 249(6):539–544Google Scholar
  41. 41.
    Kim JH, Shim KW, Yoon YS, Lee SY, Kim SS, Oh SW (2012) Cigarette smoking increases abdominal and visceral obesity but not overall fatness: an observational study. PLoS One 7(9):e45815Google Scholar
  42. 42.
    Clair C, Rigotti N, Shrader P, Caroline P, Pencina M, Meigs J (2011) Effects of smoking, cessation and weight change on cardiovascular disease among people with and without diabetes. J Gen Intern Med 26:S12–S14Google Scholar
  43. 43.
    Gerstein HC, Mann JF, Pogue J, Dinneen SF, Halle JP, Hoogwerf B et al (2000) Prevalence and determinants of microalbuminuria in high-risk diabetic and nondiabetic patients in the Heart Outcomes Prevention Evaluation Study. The HOPE Study Investigators. Diabetes Care 23(Suppl 2):35Google Scholar
  44. 44.
    Organization WH (2016) Global report on diabetesGoogle Scholar
  45. 45.
    Savage S, Estacio RO, Jeffers B, Schrier RW (1996) Urinary albumin excretion as a predictor of diabetic retinopathy, neuropathy, and cardiovascular disease in NIDDM. Diabetes care 19(11):1243–1248Google Scholar
  46. 46.
    Constantino M, Molyneaux L, Gisler F, Al Saeed A, Luo C, Wu T et al (2012) Long term complications and mortality in youth onset diabetes: type 2 diabetes is more lethal than type 1 diabetes. Diabetes Care 61(s1):A88-AGoogle Scholar
  47. 47.
    Group TS (2013) Rapid rise in hypertension and nephropathy in youth with type 2 diabetes: the TODAY clinical trial. Diabetes Care 36(6):1735–1741Google Scholar
  48. 48.
    Whiting DR, Guariguata L, Weil C, Shaw J (2011) IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract 94(3):311–321Google Scholar
  49. 49.
    Dabelea D, Department of Epidemiology CSoPH, Aurora, Stafford JM, Department of Biostatistical Sciences WFSoM, Winston-Salem, North Carolina, Mayer-Davis EJ, Departments of Nutrition and Medicine UoNC, Chapel Hill et al (2017) Association of type 1 diabetes vs type 2 diabetes diagnosed during childhood and adolescence with complications during teenage years and young adulthood. JAMA 317(8):825–835Google Scholar
  50. 50.
    Chuahirun T, Khanna A, Kimball K, Wesson DE (2003) Cigarette smoking and increased urine albumin excretion are interrelated predictors of nephropathy progression in type 2 diabetes. Am J Kidney Dis 41:13–21Google Scholar
  51. 51.
    Chuahirun T, Simoni J, Hudson C, Seipel T, Khanna A, Harrist RB et al (2004) Cigarette smoking exacerbates and its cessation ameliorates renal injury in type 2 diabetes. Am J Med Sci 327(2):57–67Google Scholar
  52. 52.
    Ikeda Y, Suehiro T, Takamatsu K, Hashimoto K, Yamashita H, Tamura T (1997) Effect of smoking on the prevalence of albuminuria in Japanese men with non-insulin-dependent diabetes mellitus. Diabetes Res Clin Pract 36(1):57–61Google Scholar
  53. 53.
    Tseng CH, Tseng CP, Chong CK. Joint effects of hypertension, smoking, dyslipidemia and obesity and angiotensin-converting enzyme DD genotype on albuminuria in Taiwanese patients with type 2 diabetes mellitus. Clin Biochem 2010;43:629–34Google Scholar
  54. 54.
    Voulgari C, Katsilambros N, Tentolouris N (2011) Smoking cessation predicts amelioration of microalbuminuria in newly diagnosed type 2 diabetes mellitus: a 1-year prospective study. Metabolism 60:1456–64Google Scholar
  55. 55.
    Phisitkul K, Hegazy K, Chuahirun T, Hudson C, Simoni J, Rajab H et al (2008) Continued smoking exacerbates but cessation ameliorates progression of early type 2 diabetic nephropathy. Am J Med Sci 335:284–91Google Scholar
  56. 56.
    Hsu CC, Hwang SJ, Tai TY, Chen T, Huang MC, Shin SJ et al (2010) Cigarette smoking and proteinuria in Taiwanese men with type 2 diabetes mellitus. Diabet Med 27(3):295–302Google Scholar
  57. 57.
    Cederholm J, Eliasson B, Nilsson PM, Weiss L, Gudbjörnsdottir S (2005) Microalbuminuria and risk factors in type 1 and type 2 diabetic patients. Diabetes Res Clin Pract 67(3):258–266Google Scholar
  58. 58.
    Savage S, Nagel NJ, Estacio RO, Lukken N, Schrier RW. Clinical factors associated with urinary albumin excretion in type II diabetes. Am J Kidney Dis 1995;25:836–44Google Scholar
  59. 59.
    Ohkuma T, Nakamura U, Iwase M, Ide H, Fujii H, Jodai T et al (2016) Effects of smoking and its cessation on creatinine- and cystatin C-based estimated glomerular filtration rates and albuminuria in male patients with type 2 diabetes mellitus: the Fukuoka Diabetes Registry. Hypertens Res 39(10):744–751Google Scholar
  60. 60.
    Prashanth P, Sulaiman KJ, Kadaha G, Bazarjani N, Bakir S, Jabri KE et al (2010) Prevalence and risk factors for albuminuria among type 2 diabetes mellitus patients: a middle-east perspective. Diabetes Res Clin Pract 88(3):e24–e27Google Scholar
  61. 61.
    Corradi L, Zoppi A, Tettamanti F, Malamani G, Lazzari P, Fogari R (1993) Association between smoking and micro-albuminuria in hypertensive patients with type 2 diabetes mellitus. J Hypertens 11:S190-1Google Scholar
  62. 62.
    Anan F, Nakagawa M, Yonemochi H, Saikawa T, Masaki T, Takahashi N et al (2007) Smoking is associated with urinary albumin excretion: an evaluation of premenopausal patients with type 2 diabetes mellitus. Metab Clin Exp 56(2):179–184Google Scholar
  63. 63.
    Hyungseon Y, Jung Hyun L, Hyeon Chang K, Il S (2016) 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 49(2):108–117Google Scholar
  64. 64.
    Forsblom CM, Totterman KJ, Saloranta C, Groop PH, Ekstrand A, Sane T et al (1998) Predictors of progression from normoalbuminuria to microalbuminuria in NIDDM. Diabetes Care 21(11):1932–1938Google Scholar
  65. 65.
    Thomas GN, Tomlinson B, McGhee SM, Lam TH, Abdullah ASM, Yeung VTF et al (2006) Association of smoking with increasing vascular involvement in type 2 diabetic chinese patients. Exp Clin Endocrinol Diabetes 114(06):301–305Google Scholar
  66. 66.
    Kanauchi M, Kawano T, Akai M, Yashima I, Nishioka H, Nakashima Y et al (1998) Smoking habit and progression of diabetic nephropathy. J Nara Med Assoc 49(2):85–89Google Scholar
  67. 67.
    Gambaro G, Bax G, Fusaro M, Normanno M, Manani SM, Zanella M et al (2001) Cigarette smoking is a risk factor for nephropathy and its progression in type 2 diabetes mellitus. Diabetes Nutr Metab Clin Exp 14(6):337–342Google Scholar
  68. 68.
    West KM, Erdreich LS, Stober JA (1980) Absence of a relationship between smoking and diabetic microangiopathy. Diabetes care 3(2):250–252Google Scholar
  69. 69.
    Klein R, Klein BE, Moss SE (1993) Incidence of gross proteinuria in older- onset diabetes. A population- based perspective. Diabetes 42(3):381Google Scholar
  70. 70.
    Bruno G, Cavallo-Perin P, Bargero G, Borra M, Calvi V, D’Errico N et al (1996) Prevalence and risk factors for micro- and macroalbuminuria in an Italian population-based cohort of NIDDM subjects. Diabetes Care 19(1):43–47Google Scholar
  71. 71.
    Bruno G, Merletti F, Biggeri A, Bargero G, Ferrero S, Pagano G et al (2003) Progression to overt nephropathy in type 2 diabetes: the Casale Monferrato Study. Diabetes Care 26(7):2150–2155Google Scholar
  72. 72.
    Bentata Y, Karimi I, Benabdellah N, El Alaoui F, Haddiya I, Abouqal R (2016) Does smoking increase the risk of progression of nephropathy and/or cardiovascular disease in type 2 diabetic patients with albuminuria and those without albuminuria? Am J Cardiovasc Dis 6(2):66–69Google Scholar
  73. 73.
    Kohler KA, McClellan WM, Ziemer DC, Kleinbaum DG, Boring JR (2000) Risk factors for microalbuminuria in black americans with newly diagnosed type 2 diabetes. Am J Kidney Dis 36(5):903–913Google Scholar
  74. 74.
    Nilsson P, Gudbjörnsdottir S, Eliasson B, Cederholm J (2004) Smoking is associated with increased HbA 1c values and microalbuminuria in patients with diabetes—data from the National Diabetes Register in Sweden. Diabetes Metab 30(3):261–268Google Scholar
  75. 75.
    Pijls LT, de Vries H, Kriegsman DM, Donker AJ, van Eijk JT (2001) Determinants of albuminuria in people with type 2 diabetes mellitus. Diabetes Res Clin Pract 52(2):133–143Google Scholar
  76. 76.
    Parving HH, Lewis JB, Ravid M, Remuzzi G, Hunsicker LG, DEMAND investigators (2006) Prevalence and risk factors for microalbuminuria in a referred cohort of type II diabetic patients: a global perspective. Kidney Int 69(11):2057–2063Google Scholar

Copyright information

© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Diabetes Research CentreUniverisity of LeicesterLeicesterUK
  2. 2.Academic Unit of Diabetes and EndocrinologyUniversity of SheffieldSheffieldUK

Personalised recommendations